Relevant bibliographies by topics / Lines detection and segmentation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Lines detection and segmentation'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Lines detection and segmentation"

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Wang, Shengli, Zhangpeng Zhou, and Wenbin Zhao. "Semantic Segmentation and Defect Detection of Aerial Insulators of Transmission Lines." Journal of Physics: Conference Series 2185, no. 1 (2022): 012086. http://dx.doi.org/10.1088/1742-6596/2185/1/012086.

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Abstract Aiming at the problems of low accuracy and poor generalization ability of insulator defect detection in complex aerial images by existing insulator defect detection algorithms, the possibility of using semantic segmentation technology to simplify insulator features in complex images is explored. The semantic segmentation model DeepLabv3 is cascaded with the target detector yolov3 to realize the semantic segmentation of insulators in aerial images and the detection of defects. The experimental results show that the use of the strategy of semantic segmentation and target detection can increase the accuracy of insulator defect detection by 12.58%, which effectively improves the performance of the detection model.
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Tao, Zhen, Shiwei Ren, Yueting Shi, Xiaohua Wang, and Weijiang Wang. "Accurate and Lightweight RailNet for Real-Time Rail Line Detection." Electronics 10, no. 16 (2021): 2038. http://dx.doi.org/10.3390/electronics10162038.

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Railway transportation has always occupied an important position in daily life and social progress. In recent years, computer vision has made promising breakthroughs in intelligent transportation, providing new ideas for detecting rail lines. Yet the majority of rail line detection algorithms use traditional image processing to extract features, and their detection accuracy and instantaneity remain to be improved. This paper goes beyond the aforementioned limitations and proposes a rail line detection algorithm based on deep learning. First, an accurate and lightweight RailNet is designed, which takes full advantage of the powerful advanced semantic information extraction capabilities of deep convolutional neural networks to obtain high-level features of rail lines. The Segmentation Soul (SS) module is creatively added to the RailNet structure, which improves segmentation performance without any additional inference time. The Depth Wise Convolution (DWconv) is introduced in the RailNet to reduce the number of network parameters and eventually ensure real-time detection. Afterward, according to the binary segmentation maps of RailNet output, we propose the rail line fitting algorithm based on sliding window detection and apply the inverse perspective transformation. Thus the polynomial functions and curvature of the rail lines are calculated, and rail lines are identified in the original images. Furthermore, we collect a real-world rail lines dataset, named RAWRail. The proposed algorithm has been fully validated on the RAWRail dataset, running at 74 FPS, and the accuracy reaches 98.6%, which is superior to the current rail line detection algorithms and shows powerful potential in real applications.
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Song, Xiang, Xiaoyu Che, Huilin Jiang, et al. "A Robust Detection Method for Multilane Lines in Complex Traffic Scenes." Mathematical Problems in Engineering 2022 (March 8, 2022): 1–14. http://dx.doi.org/10.1155/2022/7919875.

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The robustness and stability of lane detection is vital for advanced driver assistance vehicle technology and even autonomous driving technology. To meet the challenges of real-time lane detection in complex traffic scenes, a simple but robust multilane detection method is proposed in this paper. The proposed method breaks down the lane detection task into two stages, that is, lane line detection algorithm based on instance segmentation and lane modeling algorithm based on adaptive perspective transform. Firstly, the lane line detection algorithm based on instance segmentation is decomposed into two tasks, and a multitask network based on MobileNet is designed. This algorithm includes two parts: lane line semantic segmentation branch and lane line Id embedding branch. The lane line semantic segmentation branch is mainly used to obtain the segmentation results of lane pixels and reconstruct the lane line binary image. The lane line Id embedding branch mainly determines which pixels belong to the same lane line, thereby classifying different lane lines into different categories and then clustering these different categories. Secondly, the adaptive perspective transformation model is adopted. In this model, the motion information is used to accurately convert the original image into a bird’s-eye view image, and then the least-squares second-order polynomial fitting is performed on the lane line pixels. Finally, experiments on the CULane dataset show that the proposed method achieved similar or better performance compared with several state-of-the-art methods, the F1 score of the proposed method in the normal test set and most challenge test sets is better than other algorithms, which verifies the effectiveness of the proposed method, and then the field experiments results show that the proposed method has good practical application value in various complex traffic scenes.
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Yan, Jichen, Xiaoguang Zhang, Siyang Shen, et al. "A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs." Drones 7, no. 9 (2023): 574. http://dx.doi.org/10.3390/drones7090574.

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Power lines are critical infrastructure components in power grid systems. Strand breakage is a kind of serious defect of power lines that can directly impact the reliability and safety of power supply. Due to the slender morphology of power lines and the difficulty in acquiring sufficient sample data, strand breakage detection remains a challenging task. Moreover, power grid corporations prefer to detect these defects on-site during power line inspection using unmanned aerial vehicles (UAVs), rather than transmitting all of the inspection data to the central server for offline processing which causes sluggish response and huge communication burden. According to the above challenges and requirements, this paper proposes a novel method for detecting broken strands on power lines in images captured by UAVs. The method features a multi-stage light-weight pipeline that includes power line segmentation, power line local image patch cropping, and patch classification. A power line segmentation network is designed to segment power lines from the background; thus, local image patches can be cropped along the power lines which preserve the detailed features of power lines. Subsequently, the patch classification network recognizes broken strands in the image patches. Both the power line segmentation network and the patch classification network are designed to be light-weight, enabling efficient online processing. Since the power line segmentation network can be trained with normal power line images that are easy to obtain and the compact patch classification network can be trained with relatively few positive samples using a multi-task learning strategy, the proposed method is relatively data efficient. Experimental results show that, trained on limited sample data, the proposed method can achieve an F1-score of 0.8, which is superior to current state-of-the-art object detectors. The average inference speed on an embedded computer is about 11.5 images per second. Therefore, the proposed method offers a promising solution for conducting real-time on-site power line defect detection with computing sources carried by UAVs.
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Xing, Junyao, Xiaojun Bi, and Yu Weng. "A Multi-Scale Hybrid Attention Network for Sentence Segmentation Line Detection in Dongba Scripture." Mathematics 11, no. 15 (2023): 3392. http://dx.doi.org/10.3390/math11153392.

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Dongba scripture sentence segmentation is an important and basic work in the digitization and machine translation of Dongba scripture. Dongba scripture sentence segmentation line detection (DS-SSLD) as a core technology of Dongba scripture sentence segmentation is a challenging task due to its own distinctiveness, such as high inherent noise interference and nonstandard sentence segmentation lines. Recently, projection-based methods have been adopted. However, these methods are difficult when dealing with the following two problems. The first is the noisy problem, where a large number of noise in the Dongba scripture image interference detection results. The second is the Dongba scripture inherent characteristics, where many vertical lines in Dongba hieroglyphs are easily confused with the vertical sentence segmentation lines. Therefore, this paper aims to propose a module based on the convolutional neural network (CNN) to improve the accuracy of DS-SSLD. To achieve this, we first construct a tagged dataset for training and testing DS-SSLD, including 2504 real images collected from Dongba scripture books and sentence segmentation targets. Then, we propose a multi-scale hybrid attention network (Multi-HAN) based on YOLOv5s, where a multiple hybrid attention unit (MHAU) is used to enhance the distinction between important features and redundant features, and the multi-scale cross-stage partial unit (Multi-CSPU) is used to realize multi-scale and richer feature representation. The experiment is carried out on the Dongba scripture sentence segmentation dataset we built. The experimental results show that the proposed method exhibits excellent detection performance and outperforms several state-of-the-art methods.
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Chen, Yong, Yun-hui Wang, Song Li, and Meng Li. "Transmission Line Instance Segmentation Algorithm Based on YOLACT." Journal of Physics: Conference Series 2562, no. 1 (2023): 012018. http://dx.doi.org/10.1088/1742-6596/2562/1/012018.

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Abstract In the field of intelligent power patrol inspection, the transmission line is an important identification and detection target. The measurement of line spacing and ground distance are key technologies in the field of inspection. Therefore, it is necessary to quickly and accurately segment transmission lines. The transmission lines occupy a large span and vary widely in length. To improve the segmentation rate and accuracy of the transmission lines, we adopted EfficientNet as the main network. With the same accuracy, the number of parameters is reduced by 80% compared with ResNet 101. The network training cost is reduced, and the detection rate of the model is improved. To deal with the influence caused by the large change in transmission line length, we introduce adaptive anchor box calculation and the FPN + PAN structure. At the same time, multiple transmission lines are often overlapped, so the traditional NMS makes it easy to cause the omission or confusion of lines. We improve the NMS. Finally, we adopted the modified CIoU loss function to optimize the loss function. From the experimental results, our model has good performance for instance in segmenting transmission lines.
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Lee, Jaehyun, Keunwoo Lee, Jaewon Yang, Young-Jin Kim, and Seung-Woo Kim. "Comb segmentation spectroscopy for rapid detection of molecular absorption lines." Optics Express 27, no. 6 (2019): 9088. http://dx.doi.org/10.1364/oe.27.009088.

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Zhu, Yuhang, Zhezhuang Xu, Ye Lin, Dan Chen, Zhijie Ai, and Hongchuan Zhang. "A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation." Sensors 24, no. 5 (2024): 1635. http://dx.doi.org/10.3390/s24051635.

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Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently extract various semantic information of defects, an improved ResNet34 is designed to, respectively, generate multi-level features of the image and depth data, in which the depthwise separable convolution (DSC) and dilated convolution (DC) are introduced to decrease the computational expense and feature redundancy. To take full advantages of two types of data, an adaptive interacting fusion module (AIF) is designed to adaptively integrate them, thereby generating accurate feature representation of the broken defects. The experiments demonstrate that the multi-source data fusion network can effectively improve the detection accuracy of wood broken defects and reduce the false detections of interference, such as stains and mineral lines.
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Cheng, Wangfeng, Xuanyao Wang, and Bangguo Mao. "Research on Lane Line Detection Algorithm Based on Instance Segmentation." Sensors 23, no. 2 (2023): 789. http://dx.doi.org/10.3390/s23020789.

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Aiming at the current lane line detection algorithm in complex traffic scenes, such as lane lines being blocked by shadows, blurred roads, and road sparseness, which lead to low lane line detection accuracy and poor real-time detection speed, this paper proposes a lane line detection algorithm based on instance segmentation. Firstly, the improved lightweight network RepVgg-A0 is used to encode road images, which expands the receptive field of the network; secondly, a multi-size asymmetric shuffling convolution model is proposed for the characteristics of sparse and slender lane lines, which enhances the ability to extract lane line features; an adaptive upsampling model is further proposed as a decoder, which upsamples the feature map to the original resolution for pixel-level classification and detection, and adds the lane line prediction branch to output the confidence of the lane line; and finally, the instance segmentation-based lane line detection algorithm is successfully deployed on the embedded platform Jetson Nano, and half-precision acceleration is performed using NVDIA’s TensorRT framework. The experimental results show that the Acc value of the lane line detection algorithm based on instance segmentation is 96.7%, and the FPS is 77.5 fps/s. The detection speed deployed on the embedded platform Jetson Nano reaches 27 fps/s.
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Tang, Yang Shan, Dao Hua Xia, Gui Yang Zhang, Li Na Ge, and Xin Yang Yan. "The Detection Method of Lane Line Based on the Improved Otsu Threshold Segmentation." Applied Mechanics and Materials 741 (March 2015): 354–58. http://dx.doi.org/10.4028/www.scientific.net/amm.741.354.

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For overcoming the shortage of Otsu method, proposed an improved Otsu threshold segmentation algorithm. On the basis of Otsu threshold segmentation algorithm, the gray level was divided into two classes according to the image segmentation, to determine the best threshold by comparing their center distance, so as to achieve peak line recognition under the condition of multiple gray levels. Then did experiments on image segmentation of the lane line with MATLAB by traditional Otsu threshold segmentation algorithm and the improved algorithm, the threshold of traditional Otsu threshold segmentation algorithm is 144 and the threshold of the improved Otsu threshold segmentation algorithm is 131, the processing time is within 0.453 s. Test results show that the white part markings appear more, the intersection place of white lines and the background is more clear, so this method can identify lane markings well and meet the real-time requirements.
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Dissertations / Theses on the topic "Lines detection and segmentation"

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Li, Yaqian. "Image segmentation and stereo vision matching based on declivity line : application for vehicle detection." Thesis, Rouen, INSA, 2010. http://www.theses.fr/2010ISAM0010.

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Dans le cadre de systèmes d’aide à la conduite, nous avons contribué aux approches de stéréovision pour l’extraction de contour, la mise en correspondance des images stéréoscopiques et la détection de véhicules. L’extraction de contour réalisée est basée sur le concept declivity line que nous avons proposé. La declivity line est construite en liant des déclivités selon leur position relative et similarité d’intensité. L’extraction de contour est obtenue en filtrant les declivity lines construites basées sur leurs caractéristiques. Les résultats expérimentaux montrent que la declivity lines méthode extrait plus de l’informations utiles comparées à l’opérateur déclivité qui les a filtrées. Des points de contour sont ensuite mis en correspondance en utilisant la programmation dynamique et les caractéristiques de declivity lines pour réduire le nombre de faux appariements. Dans notre méthode de mise en correspondance, la declivity lines contribue à la reconstruction détaillée de la scène 3D. Finalement, la caractéristique symétrie des véhicules sont exploitées comme critère pour la détection de véhicule. Pour ce faire, nous étendons le concept de carte de symétrie monoculaire à la stéréovision. En conséquence, en effectuant la détection de véhicule sur la carte de disparité, une carte de symétrie (axe; largeur; disparity) est construite au lieu d’une carte de symétrie (axe; largeur). Dans notre concept, des obstacles sont examinés à différentes profondeurs pour éviter la perturbation de la scène complexe dont le concept monoculaire souffre<br>In the framework of driving assistance systems, we contributed to stereo vision approaches for edge extraction, matching of stereoscopic pair of images and vehicles detection. Edge extraction is performed based on the concept of declivity line we introduced. Declivity line is constructed by connecting declivities according to their relative position and intensity similarity. Edge extraction is obtained by filtering constructed declivity lines based on their characteristics. Experimental results show that declivity line method extracts additional useful information compared to declivity operator which filtered them out. Edge points of declivity lines are then matched using dynamic programming, and characteristics of declivity line reduce the number of false matching. In our matching method, declivity line contributes to detailed reconstruction of 3D scene. Finally, symmetrical characteristic of vehicles are exploited as a criterion for their detection. To do so, we extend the monocular concept of symmetry map to stereo concept. Consequently, by performing vehicle detection on disparity map, a (axis; width; disparity) symmetry map is constructed instead of an (axis; width) symmetry map. In our stereo concept, obstacles are examined at different depths thus avoiding disturbance of complex scene from which monocular concept suffers
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Bonakdar, Sakhi Omid. "Segmentation of heterogeneous document images : an approach based on machine learning, connected components analysis, and texture analysis." Phd thesis, Université Paris-Est, 2012. http://tel.archives-ouvertes.fr/tel-00912566.

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Document page segmentation is one of the most crucial steps in document image analysis. It ideally aims to explain the full structure of any document page, distinguishing text zones, graphics, photographs, halftones, figures, tables, etc. Although to date, there have been made several attempts of achieving correct page segmentation results, there are still many difficulties. The leader of the project in the framework of which this PhD work has been funded (*) uses a complete processing chain in which page segmentation mistakes are manually corrected by human operators. Aside of the costs it represents, this demands tuning of a large number of parameters; moreover, some segmentation mistakes sometimes escape the vigilance of the operators. Current automated page segmentation methods are well accepted for clean printed documents; but, they often fail to separate regions in handwritten documents when the document layout structure is loosely defined or when side notes are present inside the page. Moreover, tables and advertisements bring additional challenges for region segmentation algorithms. Our method addresses these problems. The method is divided into four parts:1. Unlike most of popular page segmentation methods, we first separate text and graphics components of the page using a boosted decision tree classifier.2. The separated text and graphics components are used among other features to separate columns of text in a two-dimensional conditional random fields framework.3. A text line detection method, based on piecewise projection profiles is then applied to detect text lines with respect to text region boundaries.4. Finally, a new paragraph detection method, which is trained on the common models of paragraphs, is applied on text lines to find paragraphs based on geometric appearance of text lines and their indentations. Our contribution over existing work lies in essence in the use, or adaptation, of algorithms borrowed from machine learning literature, to solve difficult cases. Indeed, we demonstrate a number of improvements : on separating text columns when one is situated very close to the other; on preventing the contents of a cell in a table to be merged with the contents of other adjacent cells; on preventing regions inside a frame to be merged with other text regions around, especially side notes, even when the latter are written using a font similar to that the text body. Quantitative assessment, and comparison of the performances of our method with competitive algorithms using widely acknowledged metrics and evaluation methodologies, is also provided to a large extend.(*) This PhD thesis has been funded by Conseil Général de Seine-Saint-Denis, through the FUI6 project Demat-Factory, lead by Safig SA
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Khairallah, Mahmoud. "Flow-Based Visual-Inertial Odometry for Neuromorphic Vision Sensors." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST117.

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Plutôt que de générer des images de manière constante et synchrone, les capteurs neuromorphiques de vision -également connus sous le nom de caméras événementielles, permettent à chaque pixel de fournir des informations de manière indépendante et asynchrone chaque fois qu'un changement de luminosité est détecté. Par conséquent, les capteurs de vision neuromorphiques n'ont pas les problèmes des caméras conventionnelles telles que les artefacts d'image et le Flou cinétique. De plus, ils peuvent fournir une compression sans perte de donné avec une résolution temporelle et une plage dynamique plus élevée. Par conséquent, les caméras événmentielles remplacent commodément les caméras conventionelles dans les applications robotiques nécessitant une grande maniabilité et des conditions environnementales variables. Dans cette thèse, nous abordons le problème de l'odométrie visio-inertielle à l'aide de caméras événementielles et d'une centrale inertielle. En exploitant la cohérence des caméras événementielles avec les conditions de constance de la luminosité, nous discutons de la possibilité de construire un système d'odométrie visuelle basé sur l'estimation du flot optique. Nous développons notre approche basée sur l'hypothèse que ces caméras fournissent des informations des contours des objets de la scène et appliquons un algorithme de détection de ligne pour la réduction des données. Le suivi de ligne nous permet de gagner plus de temps pour les calculs et fournit une meilleure représentation de l'environnement que les points d'intérêt. Dans cette thèse, nous ne montrons pas seulement une approche pour l'odométrie visio-inertielle basée sur les événements, mais également des algorithmes qui peuvent être utilisés comme algorithmes des caméras événementielles autonomes ou intégrés dans d'autres approches si nécessaire<br>Rather than generating images constantly and synchronously, neuromorphic vision sensors -also known as event-based cameras- permit each pixel to provide information independently and asynchronously whenever brightness change is detected. Consequently, neuromorphic vision sensors do not encounter the problems of conventional frame-based cameras like image artifacts and motion blur. Furthermore, they can provide lossless data compression, higher temporal resolution and higher dynamic range. Hence, event-based cameras conveniently replace frame-based cameras in robotic applications requiring high maneuverability and varying environmental conditions. In this thesis, we address the problem of visual-inertial odometry using event-based cameras and an inertial measurement unit. Exploiting the consistency of event-based cameras with the brightness constancy conditions, we discuss the availability of building a visual odometry system based on optical flow estimation. We develop our approach based on the assumption that event-based cameras provide edge-like information about the objects in the scene and apply a line detection algorithm for data reduction. Line tracking allows us to gain more time for computations and provides a better representation of the environment than feature points. In this thesis, we do not only show an approach for event-based visual-inertial odometry but also event-based algorithms that can be used as stand-alone algorithms or integrated into other approaches if needed
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Wigington, Curtis Michael. "End-to-End Full-Page Handwriting Recognition." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7099.

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Despite decades of research, offline handwriting recognition (HWR) of historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives. Historical documents are plagued with noise, degradation, ink bleed-through, overlapping strokes, variation in slope and slant of the writing, and inconsistent layouts. Often the documents in a collection have been written by thousands of authors, all of whom have significantly different writing styles. In order to better capture the variations in writing styles we introduce a novel data augmentation technique. This methods achieves state-of-the-art results on modern datasets written in English and French and a historical dataset written in German.HWR models are often limited by the accuracy of the preceding steps of text detection and segmentation.Motivated by this, we present a deep learning model that jointly learns text detection, segmentation, and recognition using mostly images without detection or segmentation annotations.Our Start, Follow, Read (SFR) model is composed of a Region Proposal Network to find the start position of handwriting lines, a novel line follower network that incrementally follows and preprocesses lines of (perhaps curved) handwriting into dewarped images, and a CNN-LSTM network to read the characters. SFR exceeds the performance of the winner of the ICDAR2017 handwriting recognition competition, even when not using the provided competition region annotations.
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Torr, Philip Hilaire Sean. "Motion segmentation and outlier detection." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308173.

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Deng, Jingjing (Eddy). "Adaptive learning for segmentation and detection." Thesis, Swansea University, 2017. https://cronfa.swan.ac.uk/Record/cronfa36297.

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Segmentation and detection are two fundamental problems in computer vision and medical image analysis, they are intrinsically interlinked by the nature of machine learning based classification, especially supervised learning methods. Many automatic segmentation methods have been proposed which heavily rely on hand-crafted discriminative features for specific geometry and powerful classifier for delinearating the foreground object and background region. The aimof this thesis is to investigate the adaptive schemes that can be used to derive efficient interactive segmentation methods for medical imaging applications, and adaptive detection methods for addressing generic computer vision problems. In this thesis, we consider adaptive learning as a progressive learning process that gradually builds the model given sequential supervision from user interactions. The learning process could be either adaptive re-training for smallscale models and datasets or adaptive fine-tuning for medium-large scale. In addition, adaptive learning is considered as a progressive learning process that gradually subdivides a big and difficult problem into a set of smaller but easier problems, where a final solution can be found via combining individual solvers consecutively. We first show that when discriminative features are readily available, the adaptive learning scheme can lead to an efficient interactive method for segmenting the coronary artery, where promising segmentation results can be achieved with limited user intervention. We then present a more general interactive segmentation method that integrates a CNN based cascade classifier and a parametric implicit shape representation. The features are self-learnt during the supervised training process, no hand-crafting is required. Then, the segmentation can be obtained via imposing a piecewise constant constraint to thedetection result through the proposed shape representation using region based deformation. Finally, we show the adaptive learning scheme can also be used to address the face detection problem in an unconstrained environment, where two CNN based cascade detectors are proposed. Qualitative and quantitative evaluations of proposed methods are reported, and show theefficiency of adaptive schemes for addressing segmentation and detection problems in general.
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Hastings, Joseph R. 1980. "Incremental Bayesian segmentation for intrusion detection." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/28399.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2004.<br>Includes bibliographical references (leaves 131-133).<br>This thesis describes an attempt to monitor patterns of system calls generated by a Unix host in order to detect potential intrusion attacks. Sequences of system calls generated by privileged processes are analyzed using incremental Bayesian segmentation in order to detect anomalous activity. Theoretical analysis of various aspects of the algorithm and empirical analysis of performance on synthetic data sets are used to tune the algorithm for use as an Intrusion Detection System.<br>by Joseph R. Hastings.<br>M.Eng.
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Nedilko, Bohdan. "Seismic detection of rockfalls on railway lines." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58097.

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Railway operators mitigate the risk of derailments caused by hazardous rocks falling onto the track by installing slide detector fences (SDF). These consist of electrical sensing wires strung on poles located uphill of the track; falling rocks snap these wires and trigger an alarm. Rocks of non-threatening size and migrating animals frequently break the wires causing prolonged false alarms and delaying rail traffic until the SDF is manually repaired, often in a hazardous environment. This thesis is concerned with the development of a prototype of the autonomous Seismic Rockfall Detection System (SRFDS) as a potential replacement for the SDF. Analysis and classification of natural and anthropogenic seismic signals which have been observed at the SRFDS field installations, is presented. A method for identification of hazardous rocks (>0.028 m³) using an empirical peak ground velocity attenuation model is outlined. Pattern recognition techniques which are based on cross-correlation and on variations in the short-term / long term averages of the ground vibrations are introduced for rail traffic identification and rockfall detection. The techniques allow the SRFDS to eliminate false activations by rail traffic, report hazardous rocks with minimum (< 3 s) delay, and rearm automatically when a false alarm is revealed. Performance of the SRFDS field installations was modeled using continuous seismic data recorded at two locations where the SRFDS and the SDF operate in parallel. The SRFDS computer model detected all major rock slides; it was significantly less likely than the SDF to be triggered by animal migration, but may be susceptible to thermal noise in very specific situations. A comparison of the actual number of the train delays caused by the existing SDF with those of the SRFDS computer model, shows that the use of the SRFDS will reduce the average number of delayed trains. The actual reduction of the number of delayed trains is between 3 and 8 times, depending on the location. Train delays caused by false triggers induced by construction activities and track maintenance could still exist; however, they can be eliminated by the adoption of the appropriate track management procedures.<br>Science, Faculty of<br>Earth, Ocean and Atmospheric Sciences, Department of<br>Graduate
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Torrent, Palomeras Albert. "Simultaneous detection and segmentation for generic objects." Doctoral thesis, Universitat de Girona, 2013. http://hdl.handle.net/10803/117736.

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This thesis deals with the simultaneous detection and segmentation for generic objects in images. The proposed approach is based on building a dictionary of patches, which defines the object and allows the extraction of the detection and segmentation features used to train the classifier. Moreover, we include in the boosting training the ability of crossing information between detection and segmentation with the aim that good detections may help to better segment and vice versa. We adapt also the detection proposal to deal with specific problems of object recognition in medical and astronomical images. This point stresses one of the objectives of this thesis; proposing a generic approach able to deal with objects of a very different nature<br>En aquesta tesi s'estudia la detecció i segmentació simultània d'objectes genèrics en imatges. La proposta està basada en un diccionari de parts de l'objecte que el defineixen i, alhora, ens permet extreure les característiques de detecció i segmentació per entrenar el classificador. A més, dins l'entrenament del classificador s'inclou la possibilitat de creuar informació entre la detecció i la segmentació, de tal manera que una bona detecció pugui ajudar a segmentar i viceversa. L'algorisme s'ha validat adaptant-lo al reconeixement d'objectes en imatge mèdica i imatge astronòmica. Aquest punt reforça el principal objectiu de la tesi: proposar un sistema genèric capaç de tractar amb objectes de qualsevol tipus de naturalesa
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HEGSTAM, BJÖRN. "Defect detection and segmentation inmultivariate image streams." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-142069.

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OptoNova is a world leading producer of inspection systems for quality control of surfaces and edges at high rates. They develop their own sensor systems and software and have taken an interest in investigating the possibility of using methods from machine learning to make better use of the available sensor data. The purpose of this project was to develop a method for finding surface defects based on multivariate images. A previous Master’s project done at OptoNova had shown promising results when applying machine learning methods to inspect the sides of kitchen cabinet doors. The model developed for that project was based around using a Difference of Gaussians scale-space. That was used as a starting ground for the work presented here, with changes made in order to focus on texture defects on flat surfaces. The final model works by creating a Laplacian image pyramid from a source image. Each pyramid level is processed by a trained image model that, given a multivariate image, produces a greyscale image indicating defect areas. The outputs of all image models are scaled to the same size and averaged together. This gives the final probability map indicating what parts of the sample are defective. The image models consists of a feature extractor, extracting one feature per pixel, and a feature model, which in this project was a Gaussian mixture model. The model was built in a modular fashion, making it easy to use different features and feature models. Tests showed the pyramid model to perform better than the previous model. Defects characterised by noticeable differences in surface texture gave excellent results, while defects only indicated by slight changes in intensity of the normal texture were generally not found. It was concluded that the developed model shows potential, but more work needs to be done. More tests need to be run using larger data sets and samples with different texture types, such as wooden surfaces.<br>OptoNova är en världsledande leverantör av inspektionssystem for kvalitetskontroll av ytor och kanter i hög hastighet. Företaget utvecklar egna sensorsystem och mjukvara, och är intresserade av att undersöka möjligheten att bättre utnyttja tillgänglig sensordata genom att använda metoder baserade på maskininlärning. Syftet med det här projektet var att utveckla en metod för att upptäcka ytdefekter i multivariata bilder. Ett tidigare examensarbete gjort hos OptoNova visade på lovande resultat vid inspektion av kanter på köksluckor. Modellen som utvecklades i det projektet använde sig av ett Difference of Gaussians-skalrum. Den modellen användes som utgångspunkt för det här arbetet med vissa förändringar gjorda för att lägga fokus på texturdefekter i plana ytor. Den utvecklade modellen tar in en multivariat bild och genererar en Laplacepyramid. Varje nivå i pyramiden skickas sedan igenom en tränad bildmodell som i sin tur producerar en gråskalebild där möjliga defekter är markerade. Samtliga bildmodellers resultat skalas upp till samma storlek som ursprungsbilden och en medelvärdesbild beräknas. Detta ger den slutliga defektbilden som visar vilka delar av det inlästa provet som är defekta. Varje bildmodell består dels av en modul som extraherar särdragsvektorer och dels av en modul som modellerar hur vektorer från oskadade ytor är fördelade i rummet av särdragsvektorer. För det senare användes en Gaussian mixture model (GMM). Modellens modullära design gör det enkelt att använda olika typer av särdragsvektorer och modeller för dessa. Tester visade att pyramidmodellen kan prestera bättre än den tidigare utvecklade modellen. Utmärkta resultat uppnåddes vid detektion av defekter som karaktäriserades av tydliga avvikelser i textur. Defekter som däremot endast utgjordes av mindre variationer i intensitet hittades generellt sett inte. Det konstaterades att den nya modellen visar på potential till att fungera väl, men att mer arbete fortfarande behöver göras. Framförallt måste fler tester göras med fler prover, samt prover med varierande ytmönster, såsom träytor.
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Books on the topic "Lines detection and segmentation"

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Weiss, John. Automatic jet contrail detection and segmentation. National Aeronautics and Space Administration, 1997.

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Weiss, John. Automatic jet contrail detection and segmentation. National Aeronautics and Space Administration, 1997.

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Yang, Yi. Colour edge detection and segmentation using vector analysis. National Library of Canada, 1995.

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Rajalingam, Mallikka. Text Segmentation and Recognition for Enhanced Image Spam Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-53047-1.

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Liang, Kung-Hao. From uncertainty to adaptivity: Multiscale edge detection and image segmentation. typescript, 1997.

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Herout, Adam, Markéta Dubská, and Jiří Havel. Real-Time Detection of Lines and Grids. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4414-4.

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Peterson, Jeffrey Shawn. Detection of downed trolley lines using arc signature analysis. U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Pittsburgh Research Center, 1997.

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K, Kokula Krishna Hari, ed. An Image Segmentation and Classification for Brain Tumor Detection using Pillar K-Means Algorithm. Association of Scientists, Developers and Faculties, 2016.

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Don, Russell B., and IEEE Power Engineering Society. Power Engineering Education Committee., eds. Detection of downed conductors on utility distribution systems. Available from Publication Sales Dept., IEEE Service Center, 1989.

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Herout, Adam. Real-Time Detection of Lines and Grids: By PClines and Other Approaches. Springer London, 2013.

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Book chapters on the topic "Lines detection and segmentation"

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Abdelfattah, Rabab, Xiaofeng Wang, and Song Wang. "TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines." In Computer Vision – ACCV 2020. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69544-6_36.

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Russ, John C. "Segmentation of Edges and Lines." In Computer-Assisted Microscopy. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-0563-7_4.

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Martino, J. C., and Salvatore Tabbone. "Detection of Lofar lines." In Image Analysis and Processing. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_336.

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Lu, Tong, Shivakumara Palaiahnakote, Chew Lim Tan, and Wenyin Liu. "Character Segmentation and Recognition." In Video Text Detection. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6515-6_6.

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Gauch, John M. "Segmentation and edge detection." In The Colour Image Processing Handbook. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5779-1_9.

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Hariharan, Bharath, Pablo Arbeláez, Ross Girshick, and Jitendra Malik. "Simultaneous Detection and Segmentation." In Computer Vision – ECCV 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10584-0_20.

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Morel, Jean Michel, and Sergio Solimini. "Edge Detection and Segmentation." In Variational Methods in Image Segmentation. Birkhäuser Boston, 1995. http://dx.doi.org/10.1007/978-1-4684-0567-5_1.

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Hogan, Ciarán, and Ganesh Sistu. "Automatic Vehicle Ego Body Extraction for Reducing False Detections in Automated Driving Applications." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_21.

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AbstractFisheye cameras are extensively employed in autonomous vehicles due to their wider field of view, which produces a complete 360-degree image of the vehicle with a minimum number of sensors. The drawback of having a broader field of view is that it may include undesirable portions of the vehicle’s ego body in its perspective. Due to objects’ reflections on the car body, this may produce false positives in perception systems. Processing ego vehicle pixels also uses up unnecessary computing power. Unexpectedly, there is no literature on this relevant practical problem. To our knowledge, this is the first attempt to discuss the significance of autonomous ego body extraction for automobile applications that are crucial for safety. We also proposed a simple deep learning model for identifying the vehicle’s ego-body. This model would enable us to eliminate any pointless processing of the car’s bodywork, eliminate the potential for pedestrians or other objects to be mistakenly detected in the car’s ego-body reflection, and finally, check to see if the camera is mounted incorrectly. The proposed network is a U-Net model with a Res-Net50 encoder pre-trained on ImageNet and trained for binary semantic segmentation on vehicle ego-body data. Our training data is an internal Valeo dataset with 10K samples collected by three separate car lines across Europe. This proposed network could then be integrated into the vehicles existing perception system by extracting the ego-body contour data and supplying this to the other algorithms which then ignore the area outside the contour coordinates. The proposed network can run at set intervals to save computing power and to check if the camera is misaligned by comparing the new contour data to the previous data.
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Rajalingam, Mallikka. "Character Segmentation." In Text Segmentation and Recognition for Enhanced Image Spam Detection. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53047-1_4.

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Dharanipragada, S., M. Franz, J. S. McCarley, T. Ward, and W. J. Zhu. "Segmentation and Detection at IBM." In Topic Detection and Tracking. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0933-2_7.

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Conference papers on the topic "Lines detection and segmentation"

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Zhu, Donglin, Lei Li, Rui Guo, and Shifan Zhan. "Fault Detection by Using Instance Segmentation." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21249-ms.

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Abstract Fault detection is an important, but time-consuming task in seismic data interpretation. Traditionally, seismic attributes, such as coherency (Marfurt et al., 1998) and curvature (Al-Dossary et al., 2006) are used to detect faults. Recently, machine learning methods, such as convolution neural networks (CNNs) are used to detect faults, by applying various semantic segmentation algorithms to the seismic data (Wu et al., 2019). The most used algorithm is U-Net (Ronneberger et al., 2015), which can accurately and efficiently provide probability maps of faults. However, probabilities of faults generated by semantic segmentation algorithms are not sufficient for direct recognition of fault types and reconstruction of fault surfaces. To address this problem, we propose, for the first time, a workflow to use instance segmentation algorithm to detect different fault lines. Specifically, a modified CNN (LaneNet; Neven et al., 2018) is trained using automatically generated synthetic seismic images and corresponding labels. We then test the trained CNN using both synthetic and field collected seismic data. Results indicate that the proposed workflow is accurate and effective at detecting faults.
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Kumar, Rajiv, and Amardeep Singh. "Detection and segmentation of lines and words in Gurmukhi handwritten text." In 2010 IEEE 2nd International Advance Computing Conference (IACC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iadcc.2010.5422927.

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Xue, Chuhui, Shijian Lu, and Wei Zhang. "MSR: Multi-Scale Shape Regression for Scene Text Detection." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/139.

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State-of-the-art scene text detection techniques predict quadrilateral boxes that are prone to localization errors while dealing with straight or curved text lines of different orientations and lengths in scenes. This paper presents a novel multi-scale shape regression network (MSR) that is capable of locating text lines of different lengths, shapes and curvatures in scenes. The proposed MSR detects scene texts by predicting dense text boundary points that inherently capture the location and shape of text lines accurately and are also more tolerant to the variation of text line length as compared with the state of the arts using proposals or segmentation. Additionally, the multi-scale network extracts and fuses features at different scales which demonstrates superb tolerance to the text scale variation. Extensive experiments over several public datasets show that the proposed MSR obtains superior detection performance for both curved and straight text lines of different lengths and orientations.
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Kavallieratou, Ergina. "Text line detection and segmentation." In the 2010 ACM Symposium. ACM Press, 2010. http://dx.doi.org/10.1145/1774088.1774102.

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Jahan, Kanwal, Jeethesh Pai Umesh, and Michael Roth. "Anomaly Detection on the Rail Lines Using Semantic Segmentation and Self-supervised Learning." In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2021. http://dx.doi.org/10.1109/ssci50451.2021.9659920.

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Hota, Manjit, Sudarshan Rao B, and Uttam Kumar. "Power Lines Detection and Segmentation In Multi-Spectral Uav Images Using Convolutional Neural Network." In 2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS). IEEE, 2020. http://dx.doi.org/10.1109/ingarss48198.2020.9358967.

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El-merabet, Y., C. Meurie, Y. Ruichek, A. Sbihi, and R. Touahni. "Watershed regions and watershed lines based cooperation strategy for image segmentation. Application to roof detection." In 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2011. http://dx.doi.org/10.1109/isspit.2011.6151594.

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Peng, Yaqin, Xin Zhang, Dandan Li, Yifei Chen, and Yi Shen. "An Infusion Liquid Level Detection Method Based on Improved ROI Segmentation and Horizontal Lines Modification." In 2022 41st Chinese Control Conference (CCC). IEEE, 2022. http://dx.doi.org/10.23919/ccc55666.2022.9902495.

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Protschky, Valentin, Paul Seifert, and Stefan Feit. "Stop Line Detection Using Satellite-Image Segmentation." In 2015 IEEE 81st Vehicular Technology Conference (VTC Spring). IEEE, 2015. http://dx.doi.org/10.1109/vtcspring.2015.7146110.

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Peter, Rebekka, Yuduo Song, and Martin Lauer. "Efficient Ego Lane Detection for Various Lane Types." In Forum Bildverarbeitung 2020. KIT Scientific Publishing, 2020. http://dx.doi.org/10.58895/ksp/1000124383-33.

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In this work, we present an ego lane detector designed for the use in automotive vision systems for personal light electric vehicles like electric bicycles, tricycles or scooters. The approach is based on a combination of gradientbased line detection, color-based segmentation and geometrical rules, making the ego lane detector fast, but also robust to different scenes, including curves. Qualitative evaluation on over fifty traffic scenes show that the lane detector is able to find a suitable approximation of the road area with an IoU of 75.71%.
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Reports on the topic "Lines detection and segmentation"

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Hazi, A. Radiation Detection Center on the Front Lines. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/885122.

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Bajcsy, Ruzena, Sang W. Lee, and Ales Leonardis. Image Segmentation with Detection of Highlights and Inter-Reflections Using Color. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada218710.

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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform lighting conditions. In the second part of the framework, the context-based segmentation is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. Context based segmentation makes use of a cascade of pre-trained classifiers to search for regions that are not threats. The context based segmentation algorithm accelerates threat identification and improves object detection rates. The last phase of the framework is an efficient object detection model. Efficient object detection �follows a three-stage approach which includes extraction of the local phase in the image and the use of local phase characteristics to locate machinery threats. The local phase is an image feature extraction technique which partially removes the lighting variance and preserves the edge information of the object. Multiple orientations of the same object are matched and the correct orientation is selected using feature matching by histogram of local phase in a multi-scale framework. The classifier outputs locations of threats to pipeline.�The advanced automatic image analysis system is intended to be capable of detecting construction equipment along the ROW of pipelines with a very high degree of accuracy in comparison with manual threat identification by a human analyst. �
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Wang, Ting-Wei, Yun-Hsuan Tzeng, Jia-Sheng Hong, et al. Systematic Review and Meta-Analysis of Aortic Dissection Diagnosis via CT: Evaluating Deep Learning for Detection Against Expert Analysis and Its Application in Detection and Segmentation. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.3.0125.

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Klobucar, Blaz. Urban Tree Detection in Historical Aerial Imagery of Sweden : a test in automated detection with open source Deep Learning models. Faculty of Landscape Architecture, Horticulture and Crop Production Science, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.7kn4q7vikr.

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Urban trees are a key component of the urban environment. In Sweden, ambitious goals have been expressed by authorities regarding the retention and increase of urban tree cover, aiming to mitigate climate change and provide a healthy, livable urban environment in a highly contested space. Tracking urban tree cover through remote sensing serves as an indicator of how past urban planning has succeeded in retaining trees as part of the urban fabric, and historical imagery spanning back decades for such analysis is widely available. This short study examines the viability of automated detection using open-source Deep Learning methods for long-term change detection in urban tree cover, aiming to evaluate past practices in urban planning. Results indicate that preprocessing of old imagery is necessary to enhance the detection and segmentation of urban tree cover, as the currently available training models were found to be severely lacking upon visual inspection.
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Wang, Ting-Wei, Yun-Hsuan Tzeng, Jia-Sheng Hong, et al. The Role of Deep Learning in Aortic Aneurysm Segmentation and Detection from CT Scans: A Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.3.0126.

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Alhasson, Haifa F., and Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.

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Review question / Objective: A significant amount of research has been conducted to detect and recognize diabetic foot ulcers (DFUs) using computer vision methods, but there are still a number of challenges. DFUs detection frameworks based on machine learning/deep learning lack systematic reviews. With Machine Learning (ML) and Deep learning (DL), you can improve care for individuals at risk for DFUs, identify and synthesize evidence about its use in interventional care and management of DFUs, and suggest future research directions. Information sources: A thorough search of electronic databases such as Science Direct, PubMed (MIDLINE), arXiv.org, MDPI, Nature, Google Scholar, Scopus and Wiley Online Library was conducted to identify and select the literature for this study (January 2010-January 01, 2023). It was based on the most popular image-based diagnosis targets in DFu such as segmentation, detection and classification. Various keywords were used during the identification process, including artificial intelligence in DFu, deep learning, machine learning, ANNs, CNNs, DFu detection, DFu segmentation, DFu classification, and computer-aided diagnosis.
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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.
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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavement distress, particularly as seen by patch settling. This method was far more promising. In addition, algorithms were developed for segmentation of the GPR data and for classification of the ambient pavement and the locations and types of patches found in it. The results so far are promising but far from perfect, with a relatively high rate of false alarms. The two project parts were combined to produce a fused patching table. Several hundred miles of data was captured with the Waylink System to compare with a much more limited GPR dataset. The primary dataset was captured on I-74. A software application for MATLAB has been written to aid in automation of patch table creation.
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Rau, Jerry. PR-542-163745-R01 Defining Close Metal Object Detection Capabilities of MFL ILI Tools. Pipeline Research Council International, Inc. (PRCI), 2017. http://dx.doi.org/10.55274/r0011422.

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There is a need to understand Magnetic Flux Leakage (MFL) in-line inspection data and determine if it distinguishes whether a Close Metal Object (CMO) is an adjacent pipeline or independent metallic article. There have been failures associated with CMOs both in contact and in close proximity with the pipeline, specifically water lines. With the knowledge gained on the sensitivity of MFL technology to detect such objects, a process could be developed to identify those CMOs which may be a hazard to the pipeline and prioritize them for evaluation. This report has a related webinar. ?
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