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Journal articles on the topic 'Rail area detection'
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1
Fang, Zhongbin, Xiaojie Huang, Kangquan Ye, et al. "An algorithm for extracting groove rail area based on improved Hough transform." MATEC Web of Conferences 336 (2021): 02025. http://dx.doi.org/10.1051/matecconf/202133602025.
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In order to improve the accuracy and real-time performance of the automatic cleaning of groove rails in modern trams, this paper proposes a groove rail region extraction algorithm based on improved Hough transform. First, in order to speed up the detection and remove noise, the algorithm performs a series of pre-processing on the images collected by the camera, and then use the Canny edge detection method to extract the edge feature information of the groove rail. Finally, the algorithm is improved on the basis of the traditional Hough transform method according to the actual environment. The algorithm proposes three constraints from the straight line length, the slope of the straight line and the distance between the left and right edges, making the algorithm more feasible and accurate in extracting groove rail area. The extraction accuracy reached 97.9%, and the average extraction speed was 0.1903s, laying the foundation for the automatic cleaning of trough rails of modern trams.
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Choi, Jung-Youl, and Jae-Min Han. "Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects." Applied Sciences 14, no. 5 (2024): 1874. http://dx.doi.org/10.3390/app14051874.
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In current railway rails, trains are propelled by the rolling contact between iron wheels and iron rails, and the high frequency of train repetition on rails results in a significant load exertion on a very small area where the wheel and rail come into contact. Furthermore, a contact stress beyond the allowable stress of the rail may lead to cracks due to plastic deformation. The railway rail, which is the primary contact surface between the wheel and the rail, is prone to rolling contact fatigue cracks. Therefore, a thorough inspection and diagnosis of the condition of the cracks is necessary to prevent fracture. The Detailed Guideline on the Performance Evaluation of Track Facilities in South Korea specifies the detailed requirements for the methods and procedures for conducting track performance evaluations. However, diagnosing rail surface damage and determining the severity solely rely on visual inspection, which depends on the qualitative evaluation and subjective judgment of the inspector. Against this backdrop, rail surface defect detection was investigated using Fast R-CNN in this study. To test the feasibility of the model, we constructed a dataset of rail surface defect images. Through field investigation, 1300 images of rail surface defects were obtained. Aged rails collected from the field were processed, and 1300 images of internal defects were generated through SEM testing; therefore, a total of 1300 pieces of learning data were constructed. The detection results indicated that the mean average precision was 94.9%. The Fast R-CNN exhibited high efficiency in detecting rail surface defects, and it demonstrated a superior recognition performance compared with other algorithms.
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di Scalea, Francesco Lanza, Ivan Bartoli, Piervincenzo Rizzo, and Mahmood Fateh. "High-Speed Defect Detection in Rails by Noncontact Guided Ultrasonic Testing." Transportation Research Record: Journal of the Transportation Research Board 1916, no. 1 (2005): 66–77. http://dx.doi.org/10.1177/0361198105191600110.
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Recent train accidents have reaffirmed the need to develop rail defect detection systems that are more effective than those used today. This paper proposes new inspection systems for detecting transverse-type cracks in the rail head, notoriously the most dangerous flaws in rails. In principle these systems can be applied to both continuous welded rail and jointed tracks because bidirectional inspection can be implemented. However, the systems may fail to detect defects located close to a joint. The proposed technology uses ultrasonic guided waves that are detected by remote sensors positioned as far away as 76 mm (3 in.) from the top of the rail head. An impulse hammer is used to generate waves below 50 kHz that can successfully detect cracks larger than 15% of the head cross-sectional area. For smaller cracks-those as shallow as 1 mm-a pulsed laser is used for generating waves above 100 kHz. The inspection ranges are at least 10 m (32 ft) for cracks larger than 15% of the head area and at least 500 mm (20 in.) for surface head cracks as shallow as 1 mm. The defect detection reliability is improved by using both reflection and transmission measurements.
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Wang, Zhangyu, Xinkai Wu, Guizhen Yu, and Mingxing Li. "Efficient Rail Area Detection Using Convolutional Neural Network." IEEE Access 6 (2018): 77656–64. http://dx.doi.org/10.1109/access.2018.2883704.
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Tverdomed, Volodymyr, Anatoliy Gorban, and Lesia Kushmar. "Image segmentation method of rail head defects and area measurement of selected segments." MATEC Web of Conferences 390 (2024): 04008. http://dx.doi.org/10.1051/matecconf/202439004008.
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The operation safety of railway transport, which is the most important economic and social factor, is largely determined by the technical condition of the rail track and measures to maintain the quality of its track management system. One of the system elements for ensuring the accident-free operation of the track is the technical diagnosis of rails using a method complex of non-destructive control of rails, such as acoustic (ultrasonic), magnetic, combined, etc., and monitoring of the track using methods of measuring the geometry of the rail track and its disturbances. When the wheel interacts with the rail, especially on high-speed and load-stressed sections, defects and damage inevitably occur in the rails. A rather large share of such defects are on the rolling surface of the rail head. Formed defects develop rapidly, which seriously complicates the safety of train traffic. Therefore, accurate and quick detection of defects on the rolling surface of the rail head is very important. However, it is quite difficult to detect defects on the rolling surface of the rail by the acoustic (ultrasound) method due to the violation of tight contact between the rolling surface of the rail head and the piezoelectric transducer. In this case, it is quite convenient to detect surface defects of the rail head using video control. The article provides a comparative analysis of segmentation methods. There has been presented the method of image segmentation of main rail defects based on general contour preparation and parallel-hierarchical (PH) transformation using their classification. The parallel-hierarchical transformation method allows to increase the segmentation accuracy of individual areas in the original image compared to similar ones. The algorithm of pyramidal generalized-contour preparation and the criterion system allows, by calculating the threshold for each level of the gray scale, to present the study of the image with the corresponding contour preparations at the segmentation level. Modeling of recursive generalized-contour preparation and PH transformation method for image segmentation problem of rail head defects shows that, compared to the segmentation method based on the increase of areas, the accuracy of image segmentation is better. A modified method of calculating the image contour area based on the coding of lines forming the boundaries of the black and white areas of the two-gradation image has been given.
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Li, Liming, Rui Sun, Shuguang Zhao, Xiaodong Chai, Shubin Zheng, and Ruichao Shen. "Semantic-Segmentation-Based Rail Fastener State Recognition Algorithm." Mathematical Problems in Engineering 2021 (March 2, 2021): 1–15. http://dx.doi.org/10.1155/2021/8956164.
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Rail fastener status recognition and detection are key steps in the inspection of the rail area status and function of real engineering projects. With the development of and widespread interest in image processing techniques and deep learning theory, detection methods that combine the two have yielded promising results in practical detection applications. In this paper, a semantic-segmentation-based algorithm for the state recognition of rail fasteners is proposed. On the one hand, we propose a functional area location and annotation method based on a salient detection model and construct a novel slab-fastclip-type rail fastener dataset. On the other hand, we propose a semantic-segmentation-framework-based model for rail fastener detection, where we detect and classify rail fastener states by combining the pyramid scene analysis network (PSPNet) and vector geometry measurements. Experimental results prove the validity and superiority of the proposed method, which can be introduced into practical engineering projects.
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Shen, Tuo, Jinhuang Zhou, Tengfei Yuan, Yuanxiang Xie, and Xuanxiong Zhang. "LiDAR-Based Urban Three-Dimensional Rail Area Extraction for Improved Train Collision Warnings." Sensors 24, no. 15 (2024): 4963. http://dx.doi.org/10.3390/s24154963.
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The intrusion of objects into track areas is a significant issue affecting the safety of urban rail transit systems. In recent years, obstacle detection technology based on LiDAR has been developed to identify potential issues, in which accurately extracting the track area is critical for segmentation and collision avoidance. However, because of the sparsity limitations inherent in LiDAR data, existing methods can only segment track regions over short distances, which are often insufficient given the speed and braking distance of urban rail trains. As such, a new approach is developed in this study to indirectly extract track areas by detecting references parallel to the rails (e.g., tunnel walls, protective walls, and sound barriers). Reference point selection and curve fitting are then applied to generate a reference curve on either side of the track. A centerline is then extrapolated from the two curves and expanded to produce a 2D track area with the given size specifications. Finally, the 3D track area is acquired by detecting the ground and removing points that are either too high or too low. The proposed technique was evaluated using a variety of scenes, including tunnels, elevated sections, and level urban rail transit lines. The results showed this method could successfully extract track regions from LiDAR data over significantly longer distances than conventional algorithms.
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Cao, Jinghao, Yang Li, and Sidan Du. "Robust Artificial Intelligence-Aided Multimodal Rail-Obstacle Detection Method by Rail Track Topology Reconstruction." Applied Sciences 14, no. 7 (2024): 2795. http://dx.doi.org/10.3390/app14072795.
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Detecting obstacles in the rail track area is crucial for ensuring the safe operation of trains. However, this task presents numerous challenges, including the diverse nature of intrusions, and the complexity of the driving environment. This paper presents a multimodal fusion rail-obstacle detection approach by key points processing and rail track topology reconstruction. The core idea is to leverage the rich semantic information provided by images to design algorithms for reconstructing the topological structure of railway tracks. Additionally, it combines the effective geometric information provided by LiDAR to accurately locate the railway tracks in space and to filter out intrusions within the track area. Experimental results demonstrate that our method outperforms other approaches with a longer effective working distance and superior accuracy. Furthermore, our post-processing method exhibits robustness even under extreme weather conditions.
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Zheng, Danyang, Liming Li, Shubin Zheng, et al. "A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network." Computational Intelligence and Neuroscience 2021 (July 29, 2021): 1–15. http://dx.doi.org/10.1155/2021/2565500.
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As a result of long-term pressure from train operations and direct exposure to the natural environment, rails, fasteners, and other components of railway track lines inevitably produce defects, which have a direct impact on the safety of train operations. In this study, a multiobject detection method based on deep convolutional neural network that can achieve nondestructive detection of rail surface and fastener defects is proposed. First, rails and fasteners on the railway track image are localized by the improved YOLOv5 framework. Then, the defect detection model based on Mask R-CNN is utilized to detect the surface defects of the rail and segment the defect area. Finally, the model based on ResNet framework is used to classify the state of the fasteners. To verify the robustness and effectiveness of our proposed method, we conduct experimental tests using the ballast and ballastless railway track images collected from Shijiazhuang-Taiyuan high-speed railway line. Through a variety of evaluation indexes to compare with other methods using deep learning algorithms, experimental results show that our method outperforms others in all stages and enables effective detection of rail surface and fasteners.
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Signore, James M., Mohamed G. Abdel-Maksoud, and Barry J. Dempsey. "Fiber-Optic Sensing Technology for Rail-Buckling Detection." Transportation Research Record: Journal of the Transportation Research Board 1584, no. 1 (1997): 41–45. http://dx.doi.org/10.3141/1584-06.
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Buckling and cracking of steel rails is a contributing factor in accidents on railroads today. Detection and notification of buckled track sections before a train reaches these locations will significantly increase rail safety. A fiber-optic-based sensing system, with the fiber affixed to a beam, was developed and evaluated to detect buckled regions. The purpose of this research is to evaluate the sensitivity of the fiber-optic sensing system to buckling of a long structural member. Numerous facets of fiber-optic sensing have been explored. Fiber-to-steel bonding techniques were examined and tested. Full-scale laboratory testing was conducted by elastically buckling a 24.4-m-long (80-ft) wide-flange section with hydraulic rams. Typical measurement accuracy within 10 percent of theoretical predictions was achieved by optical time domain reflectometry techniques. For field testing, however, a more robust solution is sought and is currently under development. It is suggested that a lower-cost fiber break or bend detector is a suitable option. The optical fiber will break or bend at the location of rail elongation in the buckled area, allowing the detection equipment to locate the buckled area.
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Ji, Guoyi, Wen Chen, Jinbai Zou, and Shiyan Wei. "Research on foreign object detection method in track area based on Mask-RCNN." Journal of Physics: Conference Series 2365, no. 1 (2022): 012005. http://dx.doi.org/10.1088/1742-6596/2365/1/012005.
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Abstract As the train speeds up, the damage caused by the collisions between trains and foreign objects are becoming more and more severe. Therefore, it is of great significance to monitor the intrusion of foreign objects in the track environment. In this paper, transfer learning is introduced into Mask-RCNN deep learning model. And the data set of rail image is used to train the model, which improves the effect of rail segmentation. The trained model is used to segment the rail in the picture, and the rail vulnerable area is divided based on the segmentation results. The sliding window ORB feature matching algorithm is used to calculate the similarity of vulnerable area. The detection of foreign objects in the area where is easy to invade is realized, and the detection reliability is improved. Experiments represent that this method has high accuracy, strong practicability, good robustness and universality.
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Hsu, Wei-Lun, and Chia-Ming Chang. "Rail Corrugation Index Development by Sound-Field Excitation on the Carriage Floor of In-Service Train." Sensors 23, no. 17 (2023): 7539. http://dx.doi.org/10.3390/s23177539.
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The steel rail and wheel in the railway system offer a high precision and smooth-running surface. Nevertheless, the point of contact between the rail and wheel presents a critical area that can give rise to rail corrugation. This phenomenon can potentially elevate sound and vibration levels in the vicinity considerably, necessitating advanced monitoring and assessment measures. Recently, many efforts have been directed towards utilizing in-service trains for evaluating rail corrugation, and the evaluation has primarily relied on axle-box acceleration (ABA). However, the ABA measurements require a higher threshold for vibration detection. This study introduces a novel approach to rail corrugation detection by carriage floor acceleration (CFA), aimed at lowering the detection threshold. The method capitalizes on the acceleration data sensed on the carriage floor, which is induced by the sound pressure (e.g., sound-field excitation) generated at the wheel–rail contact point. An exploration of the correlation between these datasets is undertaken by simultaneously measuring both ABA and CFA. Moreover, a pivotal aspect of this research is the development of the eigenfrequency rail corrugation index (E-RCI), a mechanism that culminates energy around specific eigenfrequencies by CFA. Through this index, a focused analysis of rail corrugation patterns is facilitated. The study further delves into the stability, repeatability, and sensitivity of the E-RCI via varied measurement scenarios. Ultimately, the CFA-based rail corrugation identification is verified, establishing its practical applicability and offering a distinct approach to detecting and characterizing rail corrugation phenomena. This study has introduced an innovative methodology for rail corrugation detection using CFA, with the principal objective of lowering the detection threshold. This approach offers an efficient measurement technique for identifying rail corrugation areas, thereby potentially reducing maintenance costs and enhancing efficiency within the railway industry.
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Ragala, Z., A. Retbi, and S. Bennani. "RAILWAY TRACK FAULTS DETECTION BASED ON IMAGE PROCESSING USING MOBILENET." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W3-2022 (December 2, 2022): 135–41. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w3-2022-135-2022.
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Abstract. The use of rail transport is increasing. Damaged rails interrupt traffic to carry out repairs. In fact, when a conductor or railway operator reports a damaged rail that interrupted the traffic in the affected area. A team of specialized agents dispatch to the site and carries out the repairs. Hence, the importance of automation of railway track faults detection to ensure track safety and reduce maintenance costs. In this work, we propose a method using image processing technologies and deep learning networks. We have studied the correlation effects of MobileNetV2 and optimization algorithms on accuracy and other performance metrics to generate a model that can achieve good performance in classifying railway track faults. The results show that the Rmsprop can improve the effectiveness of feature extraction and classification of MobileNetV2.
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Mauz, Florian, Remo Wigger, Alexandru-Elisiu Gota, and Michal Kuffa. "Automatic Detection of the Running Surface of Railway Tracks Based on Laser Profilometer Data and Supervised Machine Learning." Sensors 24, no. 8 (2024): 2638. http://dx.doi.org/10.3390/s24082638.
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The measurement of the longitudinal rail profile is relevant to the condition monitoring of the rail infrastructure. The running surface is recognizable as a shiny metallic area on top of the rail head. The detection of the running surface is crucial for vehicle-based rail profile measurements, as well as for defect detection. This paper presents a methodology for the automatic detection of the running surface based on a laser profilometer. The detection of the running surface is performed based on the light reflected from the rail surface. Three rail surfaces with different surface conditions are considered. Supervised machine learning is applied to classify individual surface elements as part of the running surface. Detection by a linear support vector machine is performed with accuracy of >90%. The lateral position of the running surface and its width are calculated. The average deviation from the labeled widths varies between −1.2mm and 5.6mm. The proposed measurement approach could be installed on a train for the future onboard monitoring of the rail network.
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Seavers, Connor, Guiherme Caselato Gandia, Jackson Winn, James Mathias, Tsuchin Chu, and Anish Poudel. "Line Scanning Thermography for Rail Base Defect Detection." Materials Evaluation 82, no. 11 (2024): 30–40. http://dx.doi.org/10.32548/2024.me-04445.
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Rail base defects present significant maintenance challenges within the railway industry, as they are difficult to detect before they lead to failure. While various in-motion nondestructive evaluation (NDE) methods exist for inspecting rail, none of the existing NDE methods can inspect the rail base area reliably and efficiently. This paper presents finite element analysis (FEA) and experimental results applying a line scanning thermography (LST) approach developed for rail base area inspection. This work demonstrates the LST approach for dynamic inspection of thick steel components containing surface and subsurface anomalies. Initially, FEA was used to help design the experimental setup, then to compare trends in surface temperature variations with those from experimental trials. Through motorized testing, test specimens were moved through a region heated by three stationary line heaters, utilizing LST to capture temperature variations on the surface of each sample. The results have shown that subsurface features 6.3 mm in diameter and with aspect ratios greater than 1.5 were detectable in all specimens. This is a promising development that warrants further study.
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Wang, Chensong, Wei Cui, Xingguang Li, and Xinrou Liu. "Foreign Body Detection in the Electrified Area of Urban Rail Trains Using Improved Yolov3 Algorithm." Tobacco Regulatory Science 7, no. 5 (2021): 1059–66. http://dx.doi.org/10.18001/trs.7.5.23.
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Foreign body invade the electric receiving area of urban rail train, interfere with the operation of electric equipment on the roof, and affect the normal operation of urban rail traffic. Aiming at the problems of the traditional non-contact foreign body detection in the electric area of urban rail train, such as slow detection speed and poor detection accuracy of small target foreign body, An improved YOLOV3 (You Only Look Once) network model based on PAN feature pyramid structure and adaptive spatial feature fusion is proposed. By improving the main body of the YOLOv3 network model, it can alleviate the problem that the network prediction size map is too large and the experience field is too small. The features of different levels of foreign objects are initially fused with PAN’s feature pyramid to extract strong location information and strong semantic information of the foreign objects, then the method of adaptive spatial feature fusion was used to learn the spatial weights of the fusion of feature maps at various scales, obtaining more effective prediction feature maps at different scales after fusion and improving the detection ability of small targets. The improved k-means clustering algorithm is used to obtain the size of anchor and match it to the corresponding feature layer, which can mark the position of foreign body more accurately. Experimental results show that the detection accuracy of the improved YOLOV3 reaches 95.7%, which is 5.1% higher than the detection effect of the original network. It can accurately and quickly identify the different size of intrusive foreign body in the electric area of the roof of the urban rail train.
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Najya, Hilma, and Ari Purno Wahyu Wibowo. "TECHNOLOGY THE FIRE DETECTION SYSTEM ON THE RAILWAY LINE IS BASED ON IMAGE PROCESSING WITH THE COMPUTER VISION METHOD." Jurnal Darma Agung 31, no. 1 (2023): 65. http://dx.doi.org/10.46930/ojsuda.v31i1.2939.
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The rail transportation system plays a role important as a medium of transportation for the movement of goods and people in large quantities and is one of the modes of cheap transportation, this transportation model evolved from a steam train until it evolves to become more sophisticated using electric and magnetic technology, trains system works well with support from several the first factor is human resources, supporting technology and facilities. The problem with the rail system is that damage to the rails can cause result in the transportation route being blocked and can cause a very fatal accident, Based on these problems, besides the feasibility of the rail system, rail maintenance is a very important key, the railroad track maintenance system is usually carried out periodically. The solution to handle this problem is to use a computer vision method by utilizing surveillance cameras or CCTV installed on railroad crossings able to detect damage, the detection of damage is categorized into a few types first is track damage, due to shifts, fires on rail lines and finally structural damage due to earthquakes or landslides in the railway line area, this technology has been applied to several developed countries. and can monitor a large area and can provide a quick response if a malfunction occurs, the system will be a smart monitoring system but doesn't replace the role of a railway officer but as a tool that will provide complete information where the location of the damage is so that prompt and more effective repair action can be taken.
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Guo, Long, Jun Zhang, Zhe Chen, et al. "Automatic Detection for Defects of Railroad Track Surface." Applied Mechanics and Materials 278-280 (January 2013): 856–60. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.856.
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Rail track surface defects detection is an important part of the monitoring of railroad safety. In this paper, rail track images obtained by detection system of rail track surface image are processed. Firstly, the Hough transform is applied to process the images of the track surface to locate and extract the image of the track surface, which overcomes the influence of incline and unfixed width of track surface images caused by vehicle vibration. Secondly, improved Sobel operator and area filter are used together to extract track surface defects from the original images. Finally, the defects images are classified based on circularity and length-width ratio of minimum enclosing rectangular of defects images.Results of experiments show that the algorithm can identify and classify the defects images of track surface. The minimum detection region in rail track surface is 0.0068 cm2.
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Bersenev, S. P., and E. M. Slobtsova. "Status of nondestructive control of transport function metal products at JSC EVRAZ NTMK." Ferrous Metallurgy. Bulletin of Scientific , Technical and Economic Information 76, no. 6 (2020): 586–90. http://dx.doi.org/10.32339/0135-5910-2020-6-586-590.
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Achievements in the area of automated ultrasonic control of quality of rails, solid-rolled wheels and tyres, wheels magnetic powder crack detection, carried out at JSC EVRAZ NTMK. The 100% nondestructive control is accomplished by automated control in series at two ultrasonic facilities RWI-01 and four facilities УМКК-1 of magnetic powder control, installed into the exit control line in the wheel-tyre shop. Diagram of location, converters displacement and control operations in the process of control at the facility RWI-01 presented, as well as the structural diagram of the facility УМКК-1. The automated ultrasonic control of rough tyres is made in the tyres control line of the wheel-tyre shop at the facility УКБ-1Д. The facility enables to control internal defects of tyres in radial, axis and circular directions of radiation. Possibilities of the facility УКБ-1Д software were shown. Nondestructive control of railway rails is made at two facilities, comprising the automated control line of the rail and structural shop. The УКР-64Э facility of automated ultrasonic rails control is intended to reveal defects in the area of head, web and middle part of rail foot by pulse echo-method with a immersion acoustic contact. The diagram of rail P65 at the facility УКР-64Э control presented. To reveal defects of the macrostructure in the area of rail head and web by mirror-shadow method, an ultrasonic noncontact electromagnetic-acoustic facility is used. It was noted, that implementation of the 100% nondestructive control into the technology of rolled stuff production enabled to increase the quality of products supplied to customers and to increase their competiveness.
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Zhang, Qiang, Fei Yan, Weina Song, Rui Wang, and Gen Li. "Automatic Obstacle Detection Method for the Train Based on Deep Learning." Sustainability 15, no. 2 (2023): 1184. http://dx.doi.org/10.3390/su15021184.
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Automatic obstacle detection is of great significance for improving the safety of train operation. However, the existing autonomous operation of trains mainly depends on the signaling control system and lacks the extra equipment to perceive the environment. To further enhance the efficiency and safety of the widely deployed fully automatic operation (FAO) systems of the train, this study proposes an intelligent obstacle detection system based on deep learning. It collects perceptual information from industrial cameras and light detection and ranging (LiDAR), and mainly implements the functionality including rail region detection, obstacle detection, and visual–LiDAR fusion. Specifically, the first two parts adopt deep convolutional neural network (CNN) algorithms for semantic segmentation and object detection to pixel-wisely identify the rail track area ahead and detect the potential obstacles on the rail track, respectively. The visual–LiDAR fusion part integrates the visual data with the LiDAR data to achieve environmental perception for all weather conditions. It can also determine the geometric relationship between the rail track and obstacles to decide whether to trigger a warning alarm. Experimental results show that the system proposed in this study has strong performance and robustness. The system perception rate (precision) is 99.994% and the recall rate reaches 100%. The system, applied to the metro Hong Kong Tsuen Wan line, effectively improves the safety of urban rail train operation.
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Savitha, AC, Kumar KM Madhu, V. Prathap, et al. "Automatic Detection of Obstacle in Railway Track." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Universities Refereed Multidisciplinary Research Journal 4, no. 5 (2025): 1–5. https://doi.org/10.5281/zenodo.15385583.
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The main aim of the work is to enhance the safety and efficiency of train operations. The system focuses on detecting obstacles on the railway track and automatically applying the train's brakes to prevent accidents. With the growing concerns over train collisions and safety incidents, the introduction of an automated system that can detect obstructions and take immediate corrective actions is crucial. The proposed work utilizes sensors like ultrasonic sensors, cameras, and advanced algorithms for obstacle detection, coupled with automated braking mechanisms.
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Kou, Lei, Mykola Sysyn, and Jianxing Liu. "INFLUENCE OF CROSSING WEAR ON ROLLING CONTACT FATIGUE DAMAGE OF FROG RAIL." Facta Universitatis, Series: Mechanical Engineering 22, no. 1 (2024): 025. http://dx.doi.org/10.22190/fume220106024k.
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The damage of the frog rail significantly affects the wear of the crossing rail and restricts the passing speed of the train. A geometric 3D modeling of the vehicle passing through the crossing center is particularly concerned with the cumulative wheel-rail contact of the traffic volume. The frog rail wear is simulated to obtain the dynamic change of the impact force of the wheel on the frog rail as the rail wears. By summarizing the existing experimental results of other scholars, it is clear that the important factors, that cause the damage of the frog rail, are vehicle load, friction coefficient, slip roll ratio and shear stress. This paper combines the theoretical analysis of mechanics and 3D simulation to obtain the position change of the wheel-rail contact point with the wear of the frog rail, and finally compares it with the actual measurement results. It can more accurately predict the area where the maximum damage occurs after a certain amount of traffic for a certain fixed model, the change of wheel-rail contact point at frog rail is simulated with the wear of each component. Through theoretical analysis, the main factors determining frog rail damage were determined. Then evaluate the possible damage area of the frog track and control the prediction range to 5-10 cm, which reduces the detection time and cost. The worst state of distraction will be detected in time to facilitate replacement or polishing. Through further research in this area, the service life of the frog rail can be predicted.
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Lü, Kun Lin, Jun Zhang, Guang Yu Dai, et al. "Track Surface Image Collecting System Base on Area-Array Camera." Applied Mechanics and Materials 321-324 (June 2013): 1145–49. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1145.
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The first step of track surface detection based on contactless optical detection is to obtain the surface image. This paper introduces a vehicle-mounted image collecting system base on area-array camera. This system avoids image morphing and edge deformation of system base on linear-array camera.Camera could works in both frequently flash and persistent bright without influence image collecting and will not miss information of the rail track surface when vehicle keeps the speed of 342Km/h. The exposure time should be chosen according to railcar speed and maximum speed.
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Phrommahakul, Nichapa, Manwika Kongpuang, Suhaidee Sani, Anas Katib, and Fittriya Sulong. "Detection of Rail Defects Using Phased Array Ultrasonic Technique." E3S Web of Conferences 602 (2025): 01009. https://doi.org/10.1051/e3sconf/202560201009.
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The Phased Array Ultrasonic Technique (PAUT) is an advanced non-destructive inspection method that utilizes an array of ultrasonic testing (UT) probes consisting of several small elements. The laboratory used an artificial crack on the rail steel grade 900A/ R260 test block to study the principles of PAUT and how to measure the defect’s reliability and accuracy. On-site studies were done on railroad tracks with different structures. Rail steel grade 900A/ R260, Hadfield steel, and thermite welded joints. No defects were found during the inspection at the crossing nose of the two positions. Only a darker shade of color in the area has an impact load from the wheel applied when a train changes to another direction. Inspection was also performed for the Thermit welded rail joint with two position defects. The inspection results found defects such as porosity, with an echo (A%) value exceeding 80%, but it does not require rejection according to the BART standard.
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Fang, Bo, Cheng Qiu, Ming Feng, Wei Liang, and Ximing Zhang. "Collision Avoidance Strategy for Multivehicle Conflict on Common Rail." Mathematical Problems in Engineering 2022 (May 3, 2022): 1–14. http://dx.doi.org/10.1155/2022/9388092.
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With the rapid expansion and application of bright logistics technology in the field of metallurgical production, in recent years, a type of intermediate granary storage and transportation system, signified by multiple child-parent rail car system, has gradually looked in steel plants, which effectively solves the drawbacks of crane craning in the intermediate warehouse area and improves the transportation competence of shop-level logistics. However, there are not many studies on conflict avoidance strategies for such multivehicle cooperative common rail transportation system in order to improve the ability of conflict detection and active avoidance in operation of such multivehicle transportation system. This paper draws on and improves the banker antideadlock conflict detection algorithm widely used in computer process management to detect conflicts in multivehicle on common rail system and effectively evades collision conflicts detected in the system by combining dynamic priority transfer with multiobjective time window offset. Finally, the validity of the improved banker algorithm conflict detection and priority transfer time window adjustment collision avoidance strategy is confirmed through an example analysis and comparison, providing a security guarantee for the efficient and intelligent operation of such common rail multivehicle logistics system, particularly for the exploration of the logistics common rail multivehicle collision avoidance strategy in metallurgical heavy-duty workshop.
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Zhang, Ziwen, Mangui Liang, and Zhiyu Liu. "A Novel Decomposition Model for Visual Rail Surface Inspection." Electronics 10, no. 11 (2021): 1271. http://dx.doi.org/10.3390/electronics10111271.
Full textAbstract:
Rail surface inspection plays a pivotal role in large-scale railway construction and development. However, accurately identifying possible defects involving a large variety of visual appearances and their dynamic illuminations remains challenging. In this paper, we fully explore and use the essential attributes of our defect structure data and the inherent temporal and spatial characteristics of the track to establish a general theoretical framework for practical applications. As such, our framework can overcome the bottleneck associated with machine vision inspection technology in complex rail environments. In particular, we consider a differential regular term for background rather than a traditional low-rank constraint to ensure that the model can tolerate dynamic background changes without losing sensitivity when detecting defects. To better capture the compactness and completeness of a defect, we introduce a tree-shaped hierarchical structure of sparse induction norms to encode the spatial structure of the defect area. The proposed model is evaluated with respect to two newly released Type-I/II rail surfaces discrete defects (RSDD) data sets and a practical rail line. Qualitative and quantitative evaluations show that the decomposition model can handle the dynamics of the track surface well and that the model can be used for structural detection of the defect area.
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Kuzmin, Egor V., Oleg E. Gorbunov, Petr O. Plotnikov, Vadim A. Tyukin, and Vladimir A. Bashkin. "Application of Neural Networks for Recognizing Rail Structural Elements in Magnetic and Eddy Current Defectograms." Modeling and Analysis of Information Systems 25, no. 6 (2018): 667–79. http://dx.doi.org/10.18255/1818-1015-2018-6-667-679.
Full textAbstract:
To ensure traffic safety of railway transport, non-destructive test of rails is regularly carried out by using various approaches and methods, including magnetic and eddy current flaw detection methods. An automatic analysis of large data sets (defectgrams) that come from the corresponding equipment is an actual problem. The analysis means a process of determining the presence of defective sections along with identifying structural elements of railway tracks on defectograms. This article is devoted to the problem of recognition of rail structural element images in magnetic and eddy current defectograms. Three classes of rail track structural elements are considered: 1) a bolted joint with straight or beveled connection of rails, 2) a butt weld of rails, and 3) an aluminothermic weld of rails. Images that cannot be assigned to these three classes are conditionally considered as defects and are placed in a separate fourth class. For image recognition of structural elements in defectograms a neural network is applied. The neural network is implemented by using the open library TensorFlow. To this purpose each selected (picked out) area of a defectogram is converted into a graphic image in a grayscale with size of 20 x 39 pixels.
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Sakthivel, V. "Advanced IoT Solution for Early Detection of Railway Hazards." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 4873–76. https://doi.org/10.22214/ijraset.2025.69363.
Full textAbstract:
The expanded development in the railroad area has brought about an expansion in the train activity thickness over the world. This has brought about the expansion in the quantity of mischances including trains. In this paper, the proposed framework which deals with the problems related to the railways this system monitors the track, platforms and trains regularly This framework makes utilization of Ultrasonic sensors IR sensors, fire sensor, GSM, GPS and other inserted frameworks Rail mischances have been expanded because of the surge streaming over the Railway tracks. We are proposing a system which is capable of object detection, fire detection, crack detection and platform barrier system to avoid accidents on platforms and we are also automating the rail crossing gates.
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Chandran, Praneeth, Johnny Asber, Florian Thiery, Johan Odelius, and Matti Rantatalo. "An Investigation of Railway Fastener Detection Using Image Processing and Augmented Deep Learning." Sustainability 13, no. 21 (2021): 12051. http://dx.doi.org/10.3390/su132112051.
Full textAbstract:
The rail fastening system forms an indispensable part of the rail tracks and needs to be periodically inspected to ensure safe, reliable and sustainable rail operations. Automated visual inspection has gained significant importance for fastener inspection in recent years. Position accuracy, robustness, and practical limitations due to the complex environment are some of the major concerns associated with this method. This study investigates the combined use of image processing and deep learning algorithms for detecting missing clamps within a rail fastening system. The images used for this study was acquired during field inspections carried out along the Borlänge-Avesta line in Sweden. The image processing techniques proposed in this study enabled the improvement of the fastener position and removal of redundant information from the fastener images. In addition, image augmentation was carried out to enhance the data set, ensure experimental reliability and replicate practical challenges associated with such visual inspection. Convolutional neural network and ResNet-50 algorithms are used for classification purposes, and both the algorithms achieved over 98% accuracy during training and validation and over 94% accuracy during the test stage. Both the algorithms also maintained a good balance between the precision and recall scores during the test stage. CNN and ResNet-50 algorithms were also tested to analyse their performances when the clamp areas were covered. CNN was able to accurately predict the fastener state up to 70% of clamp area occlusion, and ResNet-50 was able to achieve accurate predictions up to 75% of clamp area occlusion.
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Simonović, Miloš, Milan Banić, Dušan Stamenković, et al. "Toward the Enhancement of Rail Sustainability: Demonstration of a Holistic Approach to Obstacle Detection in Operational Railway Environments." Sustainability 16, no. 7 (2024): 2613. http://dx.doi.org/10.3390/su16072613.
Full textAbstract:
Rail transport plays a crucial role in promoting sustainability and reducing the environmental impact of transport. Ongoing efforts to improve the sustainability of rail transport through technological advancements and operational improvements are further enhancing its reputation as a sustainable mode of transport. Autonomous obstacle detection in railways is a critical aspect of railway safety and operation. While the widespread deployment of autonomous obstacle detection systems is still under consideration, the ongoing advancements in technology and infrastructure are paving the way for their full implementation. The SMART2 project developed a holistic obstacle detection (OD) system consisting of three sub-systems: long-range on-board, trackside (TS), and Unmanned Aerial Vehicle (UAV)-based OD sub-systems. All three sub-systems are integrated into a holistic OD system via interfaces to a central Decision Support System (DSS) that analyzes the inputs of all three sub-systems and makes decision about locations of possible hazardous obstacles with respect to trains. A holistic approach to autonomous obstacle detection for railways increases the detection area, including areas behind a curve, a slope, tunnels, and other elements blocking the train’s view on the rail tracks, in addition to providing long-range straight rail track OD. This paper presents a demonstration of the SMART2 holistic OD performed during the operational cargo haul with in-service trains. This paper defines the demonstration setup and scenario and shows the performance of the developed holistic OD system in a real environment.
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Cao, Xiangang, Mengzhen Zuo, Guoyin Chen, Xudong Wu, Peng Wang, and Yizhe Liu. "Visual Localization Method for Fastener-Nut Disassembly and Assembly Robot Based on Improved Canny and HOG-SED." Applied Sciences 15, no. 3 (2025): 1645. https://doi.org/10.3390/app15031645.
Full textAbstract:
Visual positioning accuracy is crucial for ensuring the successful execution of nut disassembly and assembly tasks by a fastener-nut disassembly and assembly robot. However, disturbances such as on-site lighting changes, abnormal surface conditions of nuts, and complex backgrounds formed by ballast in complex railway environments can lead to poor visual positioning accuracy of the fastener nuts, thereby affecting the success rate of the robot’s continuous disassembly and assembly operations. Additionally, the existing method of detecting fasteners first and then positioning nuts has poor applicability in the field. A direct positioning algorithm for spiral rail spikes that combines an improved Canny algorithm with shape feature similarity determination is proposed in response to these issues. Firstly, CLAHE enhances the image, reducing the impact of varying lighting conditions in outdoor work environments on image details. Then, to address the difficulties in extracting the edges of rail spikes caused by abnormal conditions such as water stains, rust, and oil stains on the nuts themselves, the Canny algorithm is improved through three stages, filtering optimization, gradient boosting, and adaptive thresholding, to reduce the impact of edge loss on subsequent rail spike positioning results. Finally, considering the issue of false fitting due to background interference, such as ballast in gradient Hough transformations, the differences in texture and shape features between the rail spike and interference areas are analyzed. The HOG is used to describe the shape features of the area to be screened, and the similarity between the screened area and the standard rail spike template features is compared based on the standard Euclidean distance to determine the rail spike area. Spiral rail spikes are discriminated based on shape features, and the center coordinates of the rail spike are obtained. Experiments were conducted using images collected from the field, and the results showed that the proposed algorithm, when faced with complex environments with multiple interferences, has a correct detection rate higher than 98% and a positioning error mean of 0.9 mm. It exhibits excellent interference resistance and meets the visual positioning accuracy requirements for robot nut disassembly and assembly operations in actual working environments.
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Hanum, Arrosida, Susanto Agus, Ciptaningrum Adiratna, Rudianti Tyan, Nazar Surya Kencana Masayu, and Mahmud Rizal. "Rail Line Surfaces Defect Monitoring using YOLO Architecture: Case Study on Madiun-Magetan Track, East Java." Rail Line Surfaces Defect Monitoring using YOLO Architecture: Case Study on Madiun-Magetan Track, East Java 8, no. 12 (2023): 15. https://doi.org/10.5281/zenodo.10432573.
Full textAbstract:
In Indonesia, trains are one of the most popular means of transportation for Indonesians to help with the mobility of passengers and goods. However, train derailment is also something that happens quite frequently. The train derailment was caused by several factors, the rail line damage is one of the biggest possible causes. For this reason, it is necessary to carry out inspections on it to detect and find defects on the rails for subsequent repairs. Manual inspections, as is still often done by officers in Indonesia, have shortcomings such as low efficiency, human error, and danger. Automatic inspection can shorten inspection time, reduce maintenance costs, and data can be real time. The aim of this research is to create an automatic inspection system using You Only Look Once (YOLO) algorithm to rail line detect in Indonesia by taking case studies in train operational areas along tracks that pass through in two cities, namely from the Madiun Station to West Station, in East Java Province. This area is known as Daerah Operasi 7 (DAOP 7) with the 14 km in distance. The result showed that the detection system using the YOLO model had mAP value of 99.41%, a precision value of 99%, a recall value of 99%, an f-score value of 99%, and an average IoU value of 85.84%. The YOLO model can detect railway track surface abnormalities accurately and optimally. Therefore, it can be used an automatic inspection for monitoring rail line in Indonesia generally and rail line in East Java Province, especially. Keywords:- Train; popular means of transportation; rail line damage monitoring; DAOP 7 rail track; YOLO.
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Wang, Yi, Yuhui Wang, Ping Wang, et al. "Rail Magnetic Flux Leakage Detection and Data Analysis Based on Double-Track Flaw Detection Vehicle." Processes 11, no. 4 (2023): 1024. http://dx.doi.org/10.3390/pr11041024.
Full textAbstract:
The rapid development of the railway industry has brought convenience to people’s lives. However, with the high speed, high frequency and heavy load characteristics of rail use, the safety of rail is seriously threatened. In this paper, a magnetic flux leakage testing (MFL) detection technology of rail based on a double-track flaw detection vehicle is introduced in detail, which can effectively detect the damage of rail top surface, which is the blind area of ultrasonic detection. The magnetic dipole model is used to analyze that the leakage magnetic field in the direction of Bx and Bz above the damage is related to the depth and width of the damage. The relationship between the depth of the damage and the leakage magnetic field is quantitatively studied for the damage with fixed width but varying depth. The finite element simulation tool is used to model and simulate the damage at different depths. After analyzing the different characteristic values, it is found that the peak value of magnetic leakage signal has a certain correlation with the depth of damage, and the natural logarithm function is fitted out—VBx = 0.1451ln(b) + 0.2705, VBz = 2.7787ln(b) + 0.0087. In order to verify the prediction function of the injury depth fitted by the simulation data, the human injury with different depths was processed and the dual-track flaw detector was used to carry out the experiment of high-speed detection environment. The peak-to-peak fitting of the magnetic leakage signals in the direction of Bx and Bz of the experimental results shows that the peak-to-peak variation rule is roughly in line with the natural logarithm function in the simulation. The correlation between the fitting results of the experimental data and the simulation fitting function is analyzed using the Pearson coefficient. The Pearson coefficient in the direction of Bx is ρx = 0.91386. The Pearson coefficient of the Bz direction is ρz = 0.98597, the peak-to-peak value of Bx and Bz direction is positively correlated with the depth of damage and the fitting effect of the Bz direction is better than that of the Bx direction.
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Bai, Tangbo, Jialin Gao, Jianwei Yang, and Dechen Yao. "A Study on Railway Surface Defects Detection Based on Machine Vision." Entropy 23, no. 11 (2021): 1437. http://dx.doi.org/10.3390/e23111437.
Full textAbstract:
The detection of rail surface defects is an important tool to ensure the safe operation of rail transit. Due to the complex diversity of track surface defect features and the small size of the defect area, it is difficult to obtain satisfying detection results by traditional machine vision methods. The existing deep learning-based methods have the problems of large model sizes, excessive parameters, low accuracy and slow speed. Therefore, this paper proposes a new method based on an improved YOLOv4 (You Only Look Once, YOLO) for railway surface defect detection. In this method, MobileNetv3 is used as the backbone network of YOLOv4 to extract image features, and at the same time, deep separable convolution is applied on the PANet layer in YOLOv4, which realizes the lightweight network and real-time detection of the railway surface. The test results show that, compared with YOLOv4, the study can reduce the amount of the parameters by 78.04%, speed up the detection by 10.36 frames per second and decrease the model volume by 78%. Compared with other methods, the proposed method can achieve a higher detection accuracy, making it suitable for the fast and accurate detection of railway surface defects.
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Wang, Nan, Tao Hou, and Tianming Zhang. "Research on railway track edge detection based on BM3D and Zernike moments." Archives of Transport 68, no. 4 (2023): 7–20. http://dx.doi.org/10.61089/aot2023.fz9g6c16.
Full textAbstract:
With the rapid development of intelligent rail transportation, the realization of intelligent detection of railroad foreign body intrusion has become an important topic of current research. Accurate detection of rail edge location, and then delineate the danger area is the premise and basis for railroad track foreign object intrusion detection. The application of a single edge detection algorithm in the process of rail identification is likely to cause the problem of missing important edges and weak gradient change edges of railroad tracks. It will affect the subsequent detection of track foreign objects. A combined global and local edge detection method is proposed to detect the edges of railroad tracks. In the global pixel-level edge detection, an improved blok-matching and 3D filtering (BM3D) algorithm combined with bilateral filtering is used for denoising to eliminate the interference information in the complex environment. Then the gradient direction is added to the Canny operator, the computational template is increased to achieve non-extreme value suppression, and the Otsu thresholding segmentation algorithm is used for thresholding improvement. It can effectively suppress noise while preserving image details, and improve the accuracy and efficiency of detection at the pixel level. For local subpixel-level edge detection, the improved Zernike moment algorithm is used to extract the edges of the obtained pixel-level images and obtain the corresponding subpixel-level images. It can enhance the extraction of tiny feature edges, effectively reduce the computational effort and obtain the subpixel edges of the orbit images. The experimental results show that compared with other improved algorithms, the method proposed in this paper can effectively extract the track edges of the detected images with higher accuracy, better preserve the track edge features, reduce the appearance of pseudo-edges, and shorten the edge detection time with certain noise immunity, which provides a reliable basis for subsequent track detection and analysis.
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Ye, Xuan-Yu, Yan-Yun Luo, Zai-Wei Li, and Xiao-Zhou Liu. "A Quantitative Detection Method for Surface Cracks on Slab Track Based on Infrared Thermography." Applied Sciences 13, no. 11 (2023): 6681. http://dx.doi.org/10.3390/app13116681.
Full textAbstract:
Surface cracks are typical defects in high-speed rail (HSR) slab tracks, which can cause structural deterioration and reduce the service reliability of the track system. However, the question of how to effectively detect and quantify the surface cracks remains unsolved at present. In this paper, a novel crack-detection method based on infrared thermography is adopted to quantify surface cracks on rail-track slabs. In this method, the thermogram of a track slab acquired by an infrared camera is first processed with the non-subsampled contourlet transform (NSCT)-based image-enhancement algorithm, and the crack is located via an edge-detection algorithm. Next, to quantitatively detect the surface crack, a pixel-locating method is proposed, whereby the crack width, length, and area can be obtained. Lastly, the detection accuracy of the proposed method at different temperatures is verified against a laboratory test, in which a scale model of the slab is poured and a temperature-controlled cabinet is used to control the temperature-change process. The results show that the proposed method can effectively enhance the edge details of the surface cracks in the image and that the crack area can be effectively extracted; the accuracy of the quantification of the crack width can reach 99%, whilst the accuracy of the quantification of the crack length and area is 85%, which essentially meets the requirements of HSR-slab-track inspection. This research could open the possibility of the application of IRT-based track slab inspection in HSR operations to enhance the efficiency of defect detection.
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Esteves, Gonçalo, Filipe Fidalgo, Nuno Cruz, and José Simão. "Long-Range Wide Area Network Intrusion Detection at the Edge." IoT 5, no. 4 (2024): 871–900. https://doi.org/10.3390/iot5040040.
Full textAbstract:
Internet of Things (IoT) devices are ubiquitous in various applications, such as smart homes, asset and people tracking, and city management systems. However, their deployment in adverse conditions, including unstable internet connectivity and power sources, present new cybersecurity challenges through new attack vectors. The LoRaWAN protocol, with its open and distributed network architecture, has gained prominence as a leading LPWAN solution, presenting novel security challenges. This paper proposes the implementation of machine learning algorithms, specifically the K-Nearest Neighbours (KNN) algorithm, within an Intrusion Detection System (IDS) for LoRaWAN networks. Through behavioural analysis based on previously observed packet patterns, the system can detect potential intrusions that may disrupt critical tracking services. Initial simulated packet classification attained over 90% accuracy. By integrating the Suricata IDS and extending it through a custom toolset, sophisticated rule sets are incorporated to generate confidence metrics to classify packets as either presenting an abnormal or normal behaviour. The current work uses third-party multi-vendor sensor data obtained in the city of Lisbon for training and validating the models. The results show the efficacy of the proposed technique in evaluating received packets, logging relevant parameters in the database, and accurately identifying intrusions or expected device behaviours. We considered two use cases for evaluating our work: one with a more traditional approach where the devices and network are static, and another where we assume that both the devices and the network are mobile; for example, when we need to report data back from sensors on a rail infrastructure to a mobile LoRaWAN gateway onboard a train.
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Pan, Yucheng, Jiasi Chen, Peiwen Wu, Hongsheng Zhong, Zihao Deng, and Daozong Sun. "Enhanced Rail Surface Defect Segmentation Using Polarization Imaging and Dual-Stream Feature Fusion." Sensors 25, no. 11 (2025): 3546. https://doi.org/10.3390/s25113546.
Full textAbstract:
Rail surface defects pose significant risks to the operational efficiency and safety of industrial equipment. Traditional visual defect detection methods typically rely on high-quality RGB images; however, they struggle in low-light conditions due to small, low-contrast defects that blend into complex backgrounds. Therefore, this paper proposes a novel defect segmentation method leveraging a dual-stream feature fusion network that combines polarization images with DeepLabV3+. The approach utilizes the pruned MobileNetV3 as the backbone network, incorporating a coordinate attention mechanism for feature extraction. This reduces the number of model parameters and enhances computational efficiency. The dual-stream module implements cascade and addition strategies to effectively merge shallow and deep features from both the original and polarization images. This enhances the detection of low-contrast defects in complex backgrounds. Furthermore, the CBAM is integrated into the decoding area to refine feature fusion and mitigate the issue of missing small-target defects. Experimental results demonstrate that the enhanced DeepLabV3+ model outperforms existing models such as U-Net, PSPNet, and the original DeepLabV3+ in terms of MIoU and MPA metrics, achieving 73.00% and 80.59%, respectively. The comprehensive detection accuracy reaches 97.82%, meeting the demanding requirements for effective rail surface defect detection.
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Mogyla, V. I., M. O. Morneva, and M. V. Kovtanets. "The use of the multivariate antiskid sensor to gain maximum trailed load of the rolling stock." Вісник Східноукраїнського національного університету імені Володимира Даля, no. 5 (275) (December 10, 2022): 55–57. http://dx.doi.org/10.33216/1998-7927-2022-275-5-55-57.
Full textAbstract:
The article examines the use of the antiskid sensor to gain maximum trailed load.Skidding means the slip of wheels of a vehicle (tram, railway carriage) along the bearing surface (road, rails) where the linear speed of the wheel surface is lower than the speed of the bearing surface towards the vehicle.
 The wheel slip occurs during braking. It is caused by the excess braking force over the traction with the bearing surface. Skidding of rail vehicles leads to the wear-out of locked wheels in the place of their contact with the rail and to the flat area on the wheel tire. 
 To prevent skidding of railway vehicles, one should regulate the braking force, depending on the load, using the cargo automatic mode or apply systems and devices of antiskid and nonskid equipment of vehicle units. The system for gaining maximum trailed load by attaching rail vehicles to the electric drive should have a skidding detection device(tram, railway carriage). At specified parameters of the engine and traction converter, the coefficient of transmission and the time constant of the nonskid device are chosen in case of steadiness. 
 For this purpose, one linearizes the system and builds the stability area in plane of the specified parameters using the D-decomposition method.
 The final choice of the coefficient of transmission and the time constant is made so that the system will be less subject to fluctuations and the slip speed will be as resilient as possible.
 The system for gaining maximum trailed load by attaching rail vehicles to the electric drive will be optimized by its supplementing with corresponding technical means that can include the use of the multivariate antiskid sensor.
 Contemporary antiskid devices involving quick-response electronic equipment will allow not just preventing wheel failures but also increasing the adhesive coefficient in contaminated areas of the route.
 The use of the multivariate antiskid sensor will allow obtaining a more informative useful signal in order to expand the functional capacity of the sensor, increase the reliability of its operations, which will ensure maximum trailed load of the rolling stock.
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Chudzikiewicz, Andrzej, Jozef Drozdziel, and Bogdan Sowinski. "Practical Solution of Rail Vehicle and Track Dynamics Monitoring System." Key Engineering Materials 518 (July 2012): 271–80. http://dx.doi.org/10.4028/www.scientific.net/kem.518.271.
Full textAbstract:
The improvement of traveller comfort and safety, in parallel to an increase of passenger train speed, is a reason to implement monitoring systems. These systems are more often used to monitor components of railway infrastructure and vehicles. Studies in this area must be preceded by identification and a complex analysis of the railway vehicle - track system. This allows us to recognise the dynamic properties of the system and then select the appropriate place to install sensors on board the vehicle. This paper presents the results of model studies and a prototype monitoring system which is installed on a passenger electric traction unit. This system is aimed, inter alia, at monitoring the state of train suspension and the detection of some particular track geometric irregularities.
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Liu, Shuai, Yu-Hao Shi, Tian-Yu Lin, Yong-Peng Zhang, Zhi-Jian Lu, and Lan-Jun Yang. "Influence of operating parameters on discharge mode of parallel-rail accelerator." Acta Physica Sinica 70, no. 20 (2021): 205205. http://dx.doi.org/10.7498/aps.70.20210484.
Full textAbstract:
Electromagnetic plasma accelerators which can generate hypervelocity and high density plasma jets have been widely used in the fields of nuclear physics and astrophysics. In this paper, an experimental platform of parallel-rail accelerator electromagnetically driven plasma is established, and the discharge modes under different discharge currents and gas injection conditions are studied through using magnetic probes, a spectrometer and an ICCD. A fast gas valve is used to inject argon into the rail electrode area. The time delay between the fast valve discharge and the parallel-rail accelerator discharge is fixed to be 450 μs. The waveform of power supply of the parallel-rail accelerator is a sinusoidal wave. The total capacitance is 120 μF, the total inductance is about 400 nH, and the maximum current is 170 kA. The fast valve current waveform is a double exponential waveform with a maximum current of 2.5 kA. When the discharge current is 40 kA, a current sheet with a certain thickness is generated, and the current sheet moves through different detection positions along the rail electrode at a certain velocity. Therefore, the working mode of the parallel-rail accelerator is the snowplow mode. As the discharge current increases, the trailing edge of the current channel is fixed during the current rising phase, and starts to move to the end of the rail during the current falling phase. A diffuse distributed current channel is formed, and the parallel-rail accelerator operates in a deflagration mode. As the gas injection mass increases, the current channel front velocity decreases to form a more concentrated distributed current channel, and the discharge mode turns into the snowplow mode. The stationary current channel in the deflagration mode is maintained mainly by ablating the electrode. The operating parameters mainly affect the rail voltage, which in turn affects the discharge mode of the parallel-rail accelerator. The rail voltage increases when the discharge current or the current rate of change increases. If the rail gap behind the current channel cannot withstand the high rail voltage under large discharge current or large current rate of change, the breakdown occurs, which results in the deflagration mode discharge.
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Liu, Shuai, Yu-Hao Shi, Tian-Yu Lin, Yong-Peng Zhang, Zhi-Jian Lu, and Lan-Jun Yang. "Influence of operating parameters on discharge mode of parallel-rail accelerator." Acta Physica Sinica 70, no. 20 (2021): 205205. http://dx.doi.org/10.7498/aps.70.20210484.
Full textAbstract:
Electromagnetic plasma accelerators which can generate hypervelocity and high density plasma jets have been widely used in the fields of nuclear physics and astrophysics. In this paper, an experimental platform of parallel-rail accelerator electromagnetically driven plasma is established, and the discharge modes under different discharge currents and gas injection conditions are studied through using magnetic probes, a spectrometer and an ICCD. A fast gas valve is used to inject argon into the rail electrode area. The time delay between the fast valve discharge and the parallel-rail accelerator discharge is fixed to be 450 μs. The waveform of power supply of the parallel-rail accelerator is a sinusoidal wave. The total capacitance is 120 μF, the total inductance is about 400 nH, and the maximum current is 170 kA. The fast valve current waveform is a double exponential waveform with a maximum current of 2.5 kA. When the discharge current is 40 kA, a current sheet with a certain thickness is generated, and the current sheet moves through different detection positions along the rail electrode at a certain velocity. Therefore, the working mode of the parallel-rail accelerator is the snowplow mode. As the discharge current increases, the trailing edge of the current channel is fixed during the current rising phase, and starts to move to the end of the rail during the current falling phase. A diffuse distributed current channel is formed, and the parallel-rail accelerator operates in a deflagration mode. As the gas injection mass increases, the current channel front velocity decreases to form a more concentrated distributed current channel, and the discharge mode turns into the snowplow mode. The stationary current channel in the deflagration mode is maintained mainly by ablating the electrode. The operating parameters mainly affect the rail voltage, which in turn affects the discharge mode of the parallel-rail accelerator. The rail voltage increases when the discharge current or the current rate of change increases. If the rail gap behind the current channel cannot withstand the high rail voltage under large discharge current or large current rate of change, the breakdown occurs, which results in the deflagration mode discharge.
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Xiao, Tianwen, Yongneng Xu, and Huimin Yu. "Research on Obstacle Detection Method of Urban Rail Transit Based on Multisensor Technology." Journal of Artificial Intelligence and Technology 1, no. 1 (2021): 61–67. http://dx.doi.org/10.37965/jait.2020.0027.
Full textAbstract:
With the rapid development of urban rail transit, passenger traffic is increasing, and obstacle violations are more frequent, and the safety of train operation under high-density traffic conditions is becoming more and more thought-provoking. In order to monitor the train operating environment in real time, this paper first adopts multi-sensing technology based on machine vision and lidar, which is used to collect video images and ranging data of the track area in real time, and then it performs image preprocessing and division of regions of interest on the collected video. Then, the obstacles in the region of interest are detected to obtain the geometric characteristics and position information of the obstacles. Finally, according to the danger level of the obstacles, determine the degree of impact on train operation , the automatic response mode and manual response mode of the signal system are used to transmit the detection results to the corresponding train to control train operation. Through simulation analysis and experimental verification, the detection accuracy and control performance of the detection method are confirmed, which provides safety guarantee for the train operation.
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Yuan, Cheng, and Xin Chen. "Research on collision avoidance method based on millimeter wave radar." Highlights in Science, Engineering and Technology 37 (March 18, 2023): 137–41. http://dx.doi.org/10.54097/hset.v37i.6068.
Full textAbstract:
In view of the current production environment in the mining area during the transportation of the minecart, the minecart driver's obstruction of vision and dim light during the driving process, which is easy to cause the minecart to collide and rear-end the collision. Various accidents such as injuries occur. Therefore, in order to improve the safety of mining area operation, it is necessary to apply the target detection technology of the rail travel area to the train to further realize unmanned driving. Millimeter wave radar has a series of advantages such as small size, light weight, narrow beam, high spatial resolution, strong anti-interference ability, etc., which can be applied in bad weather and harsh mining area production environment, and can achieve uninterrupted 24-hour work
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Pamuła, Teresa, and Wiesław Pamuła. "Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning." Sensors 21, no. 18 (2021): 6281. http://dx.doi.org/10.3390/s21186281.
Full textAbstract:
The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensors located at the site increases the capability for reacting to dangerous situations. One such source is video from monitoring cameras. This paper presents a method for processing video data, using deep learning, for the determination of the state of the area (region of interest—ROI) vital for a safe passage of the train. The proposed approach is validated using video surveillance material from a number of RLC sites in Poland. The films include 24/7 observations in all weather conditions and in all seasons of the year. Results show that the recall values reach 0.98 using significantly reduced processing resources. The solution can be used as an auxiliary source of signals for train control systems, together with other sensor data, and the fused dataset can meet railway safety standards.
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Ma, Boyang, Shupeng Chen, Shulong Wang, et al. "A False Trigger-Strengthened and Area-Saving Power-Rail Clamp Circuit with High ESD Performance." Micromachines 14, no. 6 (2023): 1172. http://dx.doi.org/10.3390/mi14061172.
Full textAbstract:
A power clamp circuit, which has good immunity to false trigger under fast power-on conditions with a 20 ns rising edge, is proposed in this paper. The proposed circuit has a separate detection component and an on-time control component which enable it to distinguish between electrostatic discharge (ESD) events and fast power-on events. As opposed to other on-time control techniques, instead of large resistors or capacitors, which can cause a large occupation of the layout area, we use a capacitive voltage-biased p-channel MOSFET in the on-time control part of the proposed circuit. The capacitive voltage-biased p-channel MOSFET is in the saturation region after the ESD event is detected, which can serve as a large equivalent resistance (~106 Ω) in the structure. The proposed power clamp circuit offers several advantages compared to the traditional circuit, such as having at least 70% area savings in the trigger circuit area (30% area savings in the whole circuit area), supporting a power supply ramp time as fast as 20 ns, dissipating the ESD energy more cleanly with little residual charge, and recovering faster from false triggers. The rail clamp circuit also offers robust performance in an industry-standard PVT (process, voltage, and temperature) space and has been verified by the simulation results. Showing good performance of human body model (HBM) endurance and high immunity to false trigger, the proposed power clamp circuit has great potential for application in ESD protection.
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Jia, Yajuan, Jianbo Zheng, and Hongfang Zhou. "Research on Airborne Electromagnetic Whole-area Apparent Resistivity Imaging Algorithm in the Detection of Goaf in Rail Transit." Journal of Physics: Conference Series 2083, no. 4 (2021): 042072. http://dx.doi.org/10.1088/1742-6596/2083/4/042072.
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Abstract Depth apparent resistivity imaging is an important process of data processing and analysis in the aviation transient electromagnetic method. It can provide reference value of conductor depth, vertical extension, and other information, and can accurately provide the measurement of each aviation transient electromagnetic measurement system. The structural section of the apparent conductivity of the one-dimensional layered medium on the line. As an advanced geophysical exploration technology, the aerial transient electromagnetic method has been applied significantly in the exploration of polymetallic minerals abroad in recent years. In this paper, based on the theory of ground-to-air transient electromagnetic method with multiple radiation sources, a corresponding multi-component global apparent resistivity definition method is established. The advantages of using the magnetic field strength to define the global apparent resistivity of the multi-radiation field source ground-air system are analysed. For each component of the magnetic field strength, respective global apparent resistivity algorithms are proposed to realize the multi-component, full-time, and full-space visual resistivity. The resistivity is calculated, and the influence of the offset on the global apparent resistivity is analysed. By adjusting the relative position of the source and the current direction and other parameters, the multi-radiation source transient electromagnetic ground-air system can not only strengthen the signal strength of different components, weaken random interference, but also better distinguish the location of underground anomalies
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Yaodong, Jiang. "Active Obstacle Detection System Based on Video Recognition and Lidar Information Fusion." New Metro 1, no. 1 (2020): 11–21. http://dx.doi.org/10.37819/nm.001.01.0073.
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In terms of the requirements for obstacle detection in the rail transit application field, an architecture and implementation method for active obstacle detection system based on the fusion of video recognition and lidar information is proposed. The studies on the video recognition algorithms based on deep learning neural network and lidar for orbit area recognition, pedestrian vehicle recognition, and small foreign object recognition are analyzed, and the necessity of the fusion of video recognition and lidar data and the related key technical points are discussed. Through the tests on domestic metro and tram lines, the feasibility of the scheme is verified, and the technical parameters are optimized, which can effectively reduce the probability of accidents caused by foreign object intrusion.
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Jiang, Aihui, Jie Dai, Sisi Yu, Baolei Zhang, Qiaoyun Xie, and Huanxue Zhang. "Unsupervised Change Detection around Subways Based on SAR Combined Difference Images." Remote Sensing 14, no. 17 (2022): 4419. http://dx.doi.org/10.3390/rs14174419.
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Prompt and precise acknowledgement of surface change around subways is of considerable significance in urban rail protection and local environmental management. Research has proven the considerable potential of synthetic aperture radar (SAR) images for detecting such information; however, previous studies have mostly focused on change intensity using single Difference images (DIs), e.g., difference value DI (DVDI) and mean value DI (MVDI). With the aim of more accurate information with respect to surface changes around subways, in this study, we proposed a novel SAR detection method that involved three steps: (1) the calculation of three single DIs, (2) the combination of the single DIs and (3) the delineation of the changed area. Compared to existing detection methods, the proposed method represents three major improvements. First, both the intensity information and phase information were applied by combining the DVDI, MVDI and coherent difference images (CDIs). Secondly, a local energy weight (LEW) approach was proposed to combine single DIs instead of the normally used equal weights. Because the changed area often comprises continuous rather than discrete pixels, a combined DI with the LEW (“CoDI-LEW” hereafter) fully considers the attributes of adjacent pixels and enhances the signal-to-noise ratio of SAR images. Thirdly, the FCM algorithm, instead of the widely used threshold methods, was applied to distinguish changed areas from unchanged areas. An experimental comparison with several existing detection methods showed that the proposed method could delineate changed areas with higher accuracy in terms of both quality and quantity. Furthermore, it can effectively execute detection under diverse surface change conditions with good feasibility and applicability.
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Beauchamp, A. J. "Banded rail (Gallirallus philippensis) detection at Ruakaka estuary before, during, and after mangrove (Avicennia marina) removal." Notornis 72, no. 3 (2025): 161. https://doi.org/10.63172/012836krmcgh.
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Banded rails (Hypotaenidia philippensis) were monitored using footprints before, during, and after the partial removal of 1.8 ha of mangroves (Avicennia marina) from a 2.4 ha area in the Ruakaka estuary. Mangrove removal occurred in two phases: adult trees in winter 2014 and juvenile plants and pneumatophores in winter 2015. Banded rails were only detected on the margins of mangroves during adult tree removal, and then throughout the cleared areas after seedling and pneumatophore removal. In 2016, 2018, and 2020, rails showed a similar use pattern in the uncleared and cleared areas to that used before mangrove removal. After mangrove seedling and pneumatophore removal, potential predators, including cats (Felis catus), were present most of the time, and mustelids (Mustela spp.) were present in summer.