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Journal articles on the topic 'Vision-based defect detection'
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1
Nguyen, Thanh-Hung, Huu-Long Nguyen, Ngoc-Tam Bui, et al. "Vision-Based System for Black Rubber Roller Surface Inspection." Applied Sciences 13, no. 15 (2023): 8999. http://dx.doi.org/10.3390/app13158999.
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This paper proposes a machine vision system for the surface inspection of black rubber rollers in manufacturing processes. The system aims to enhance the surface quality of the rollers by detecting and classifying defects. A lighting system is installed to highlight surface defects. Two algorithms are proposed for defect detection: a traditional-based method and a deep learning-based method. The former is fast but limited to surface defect detection, while the latter is slower but capable of detecting and classifying defects. The accuracy of the algorithms is verified through experiments, with the traditional-based method achieving near-perfect accuracy of approximately 98% for defect detection, and the deep learning-based method achieving an accuracy of approximately 95.2% for defect detection and 96% for defect classification. The proposed machine vision system can significantly improve the surface inspection of black rubber rollers, thereby ensuring high-quality production.
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Li, Qiu Shi. "Artwork Defect Detection Based on Computer Vision." Advanced Materials Research 846-847 (November 2013): 1234–38. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1234.
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Art identification becomes more and more sophisticated today, so the conventional testing or visual inspection is difficult to ensure accurate detection of small defects. This paper presents a computer-based defect detection method for artworks. It adopts small components removing method to remove image noise and any parts interfering measurements, and maximum variance method as threshold method of the detection system. Pixel and sub-pixel level edge detection methods are used to overcome the shortcomings of traditional methods to complete the defect detection. Experiments show that the scheme is feasible, and has high accuracy and shorter detection time.
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Xiaoning Bo, Xiaoning Bo, Jin Wang Xiaoning Bo, Honglan Li Jin Wang, Guoqin Li Honglan Li, and Feng Lu Guoqin Li. "Machine Vision Based Defect Detection Method for Electronic Component Solder Pads." 電腦學刊 34, no. 4 (2023): 175–83. http://dx.doi.org/10.53106/199115992023083404015.
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<p>This paper proposes a machine vision based solder pad detection method to improve the detection accuracy and efficiency of PCB solder pad defects in electronic components due to missed detection and low detection efficiency. Firstly, preprocess the electronic component pad images collected by the visual system, then use threshold segmentation method to perform preliminary segmentation of the pad images. Then, the coarse segmented images are finely segmented using mean clustering method, and the fine segmented images are pixel edge extracted. Finally, the matrix subpixel edge detection method is used to improve the edge detection accuracy. Simulation experiments have shown that the proposed method can significantly improve the accuracy and speed of defect recognition.</p> <p>&nbsp;</p>
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Barua, Shyam, Frank Liou, Joseph Newkirk, and Todd Sparks. "Vision-based defect detection in laser metal deposition process." Rapid Prototyping Journal 20, no. 1 (2014): 77–85. http://dx.doi.org/10.1108/rpj-04-2012-0036.
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Purpose – Laser metal deposition (LMD) is a type of additive manufacturing process in which the laser is used to create a melt pool on a substrate to which metal powder is added. The powder is melted within the melt pool and solidified to form a deposited track. These deposited tracks may contain porosities or cracks which affect the functionality of the part. When these defects go undetected, they may cause failure of the part or below par performance in their applications. An on demand vision system is required to detect defects in the track as and when they are formed. This is especially crucial in LMD applications as the part being repaired is typically expensive. Using a defect detection system, it is possible to complete the LMD process in one run, thus minimizing cost. The purpose of this paper is to summarize the research on a low-cost vision system to study the deposition process and detect any thermal abnormalities which might signify the presence of a defect. Design/methodology/approach – During the LMD process, the track of deposited material behind the laser is incandescent due to heating by the laser; also, there is radiant heat distribution and flow on the surfaces of the track. An SLR camera is used to obtain images of the deposited track behind the melt pool. Using calibrated RGB values and radiant surface temperature, it is possible to approximate the temperature of each pixel in the image. The deposited track loses heat gradually through conduction, convection and radiation. A defect-free deposit should show a gradual decrease in temperature which enables the authors to obtain a reference cooling curve using standard deposition parameters. A defect, such as a crack or porosity, leads to an increase in temperature around the defective region due to interruption of heat flow. This leads to deviation from the reference cooling curve which alerts the authors to the presence of a defect. Findings – The temperature gradient was obtained across the deposited track during LMD. Linear least squares curve fitting was performed and residual values were calculated between experimental temperature values and line of best fit. Porosity defects and cracks were simulated on the substrate during LMD and irregularities in the temperature gradients were used to develop a defect detection model. Originality/value – Previous approaches to defect detection in LMD typically concentrate on the melt pool temperature and dimensions. Due to the dynamic and violent nature of the melt pool, consistent and reliable defect detection is difficult. An alternative method of defect detection is discussed which does not involve the melt pool and therefore presents a novel method of detecting a defect in LMD.
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Chen, Chao, Shuai Li, and Y. Frank Chen. "An Accurate Detection and Location of Weld Surface Defect Based on Laser Vision." Key Engineering Materials 963 (October 13, 2023): 197–207. http://dx.doi.org/10.4028/p-vaqqo3.
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In order to effectively improve the efficiency of automatic detection and subsequent processing of welding defects in the construction field, this paper proposes a method for detecting and locating weld surface defects based on machine vision and laser vision. YOLOv5 is used for the initial detection and identification of weld hole defects to obtain the approximate location of the defect. Subsequently, the detailed features of the defect sites are extracted by scanning the approximate range of defect locations with a line laser 3D sensor based on the identification of weld defect holes. Finally, the defect location and depth are accurately located based on the extracted features. Experimental results show that the proposed method is capable of identifying weld surface hole defects with an accuracy rate of over 94%. Furthermore, the combination of the system with the line laser 3D sensor detection can significantly improve the accuracy compared to pure 2D visual inspection, while the manual measurement is neither convenient nor accurate. This indicates that the proposed system can be used for rapid and accurate feature information extraction of weld hole defects, making subsequent remedial welding in actual engineering more automatic and efficient.
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Li, Wei, Guangjun Liu, and Yunfei Wang. "Machine Vision-Based Hook Defect Detection." Journal of Physics: Conference Series 2435, no. 1 (2023): 012002. http://dx.doi.org/10.1088/1742-6596/2435/1/012002.
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Abstract Due to the harsh working environment of crane equipment, regular safety inspection is essential. The hook is one of the most frequently used parts of crane equipment. Therefore, if the hook lacks regular and standardized safety inspection, it can easily cause casualties. However, traditional machine vision techniques still face many challenges, making it difficult to extract the target object from the complex scene. In this paper, we propose a combination of machine vision techniques based on deep learning and traditional methods to extract hooks from complex construction site scenes and detect whether the hooks meet the criteria for proper operation.
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Mohammed Abdelalim, Ahmed, Yasmin Shalaby, Gamal A. Ebrahim, and Mohamed Badawy. "An Article Review on Vision-Based Defect Detection Technologies for Reinforced Concrete Bridges." Annals of Civil Engineering and Management 1, no. 1 (2024): 01–17. https://doi.org/10.33140/acem.01.01.08.
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Bridges are crucial and the most vulnerable element in the infrastructure systems. A major challenge is to maintain bridge structures at a sufficient level of safety. Scheduled inspections in these structures are important to prevent any failure. The requirement of periodic inspection is urgently needed to maintain the bridges in safe operating condition for the public. Visual inspection is currently the main form for the flaw’s inspection. Nevertheless, it is suffering from time consuming and some limitations related to subjectivity and uncertainty. Due to the complexity of bridge structure, automatic defect detection is an urgent requirement for reinforced concrete bridges. In view of this, the creation and utilization of computer vision method has received considerable attention in several applications of civil engineering. Thus, this paper introduces a comprehensive study in computer vision-based defect detection related to concrete bridges. In this study, a detailed survey is undertaken to identify the research problems and the accomplishments to date in this field. Accordingly, 50 studies between peer-reviewed publications and conference papers Scopus found in are reviewed. Through the analysis, the current review divided the image technology into three groups based on: 1) image processing; 2) machine learning; and 3) quantifying the severity of defects by identifying their parameters. This article highlights the difference and the advantages and disadvantages of applying image processing techniques and machine learning. The paper identifies the types of defects detected by image technology in previous studies and their shortcomings in determining some parameters related to those defects. Finally, this research addresses issues related to the efficiency of detection and the main factors to be considered that may help further research in image-based approaches for defect detection effectively in concrete bridges.
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Guo, Kunxiang, and Yuan Sun. "Crankshaft defect detection based on machine vision." Journal of Physics: Conference Series 2562, no. 1 (2023): 012021. http://dx.doi.org/10.1088/1742-6596/2562/1/012021.
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Abstract As a core component of the Marine engine, the crankshaft crank is required to have a high surface quality to ensure that the ship does not fail during the voyage. After analysing the defect characteristics, aiming at the problems such as the inaccurate detection of single defect feature in the crankshaft bend defect, which leads to poor detection effect, a crankshaft bend defect detection classification method based on machine vision was proposed. The recognition accuracy can reach 97.8%. Compared with the defect identification by texture and geometric features alone, the classification and recognition accuracy can be improved, and the requirements of industrial detection can be better met.
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Li, Mengkun, Junying Jia, Xin Lu, and Yue Zhang. "A Method of Surface Defect Detection of Irregular Industrial Products Based on Machine Vision." Wireless Communications and Mobile Computing 2021 (May 8, 2021): 1–10. http://dx.doi.org/10.1155/2021/6630802.
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In recent years, the surface defect detection technology of irregular industrial products based on machine vision has been widely used in various industrial scenarios. This paper takes Bluetooth headsets as an example, proposes a Bluetooth headset surface defect detection algorithm based on machine vision to quickly and accurately detect defects on the headset surface. After analyzing the surface characteristics and defect types of Bluetooth headsets, we proposed a surface scratch detection algorithm and a surface glue-overflowed detection algorithm. The result of the experiment shows that the detection algorithm can detect the surface defect of Bluetooth headsets fast as well as effectively, and the accuracy of defect recognition reaches 98%. The experiment verifies the correctness of the theory analysis and detection algorithm; therefore, the detection algorithm can be used in the recognition and detection of surface defect of Bluetooth headsets.
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Deng, Dexiang, and Lingjiang Zhang. "Simultaneous Detection of Sculpture Defects Based on Vision and Positioning Method." Journal of Nanoelectronics and Optoelectronics 17, no. 8 (2022): 1134–43. http://dx.doi.org/10.1166/jno.2022.3295.
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The crack detection and effective maintenance of sculpture cultural relics have attracted more and more attention. However, for surface defects. Autonomous detection is mostly based on vision. The detection range of this type of method is limited to a common defect with a large crack width and easy identification. The conditions are too harsh. In reality, various types of defects usually appear, and they only occupy a small part of the inspection image. In addition, the difference between the parameters and the surrounding image parameters is small, which can easily lead to missed detection and false detection. In addition, most of the current researches only focus on defect detection. Little attention is paid to defect positioning, and this is the indispensable information for repairing and protecting sculptures. Part of the research proposed GPS positioning, but GPS signals are easily lost in a relatively complex geographic environment, and its infrastructure is not reliable and will increase Positioning costs. In this regard, this paper proposes a vision-based defect detection and positioning network method, which can be used in harsh conditions Detect, and locate defects, Which also set A supervised Deep Convolutional Neural Network is calculated. This paper also creates a training method to optimize its performance on the neural network. Experiments show the detection accuracy of this method is 80.7%, and the positioning accuracy of each image is 86% at 0.41 s (in the field. In the test experiment, it is 1200 pixels).
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Wang, Chengyan, Weiping Shi, and Yuling Li. "Machine Vision Product Appearance Defect Detection Based on Deep Learning." Journal of Physics: Conference Series 2405, no. 1 (2022): 012025. http://dx.doi.org/10.1088/1742-6596/2405/1/012025.
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Abstract Due to the influence of objective environmental conditions in the image acquisition stage, the image quality of the existing methods is poor, which makes the effect of defect detection not ideal. In this paper, research on product appearance defect detection based on deep learning machine vision is proposed. A product appearance model is constructed by using a Gaussian mixture, and multiple sub-images with different sizes are used as the data support of the multi-scale expression of the Gaussian model to calculate the Gaussian difference features of the image. A Faster R-CNN network algorithm in deep learning is used to recognize defects. The multi-task mechanism is introduced in the setting of the loss function to realize the concurrent function of defect detection. The mean pooling and maximum pooling operations are introduced in the setting of the classification regression network to realize the comprehensive detection of target images. Judge whether that product has a defect according to the output result of the algorithm and the DOG characteristics of the image. The test results show that the accuracy of the designed method for various types of defect detection is more than 90. 00% and it has a high detection efficiency.
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Ming, Wuyi, Shengfei Zhang, Xuewen Liu, et al. "Survey of Mura Defect Detection in Liquid Crystal Displays Based on Machine Vision." Crystals 11, no. 12 (2021): 1444. http://dx.doi.org/10.3390/cryst11121444.
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Liquid crystal display (LCD) is a display device based on liquid crystal electro-optic effect, and LCDs have gradually appeared and have become an indispensable part of people’s lives. In the development of LCD technology, the detection of Mura defects is a key concern in the manufacturing process. The Mura defect is a kind of display defect with low contrast and an irregular shape. This study first explains the mechanism of Mura defects in the LCD manufacturing process and classifies typical Mura defects. Then, three main purposes for the defect detection of LCDs are compared, and the advantages and disadvantages are conducted. Following that, this research examines reviews the linked literature on image preprocessing, feature extraction, dimension reduction, and classifiers of Mura defects. Finally, the future development trend and research direction of Mura defect detection based on machine vision can be drawn by this study.
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Su, Weihao, Yutu Yang, Chenxin Zhou, Zilong Zhuang, and Ying Liu. "Multiple Defect Classification Method for Green Plum Surfaces Based on Vision Transformer." Forests 14, no. 7 (2023): 1323. http://dx.doi.org/10.3390/f14071323.
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Green plums have produced significant economic benefits because of their nutritional and medicinal value. However, green plums are affected by factors such as plant diseases and insect pests during their growth, picking, transportation, and storage, which seriously affect the quality of green plums and their products, reducing their economic and nutritional value. At present, in the detection of green plum defects, some researchers have applied deep learning to identify their surface defects. However, the recognition rate is not high, the types of defects identified are singular, and the classification of green plum defects is not detailed enough. In the actual production process, green plums often have more than one defect, and the existing detection methods ignore minor defects. Therefore, this study used the vision transformer network model to identify all defects on the surfaces of green plums. The dataset was classified into multiple defects based on the four types of defects in green plums (scars, flaws, rain spots, and rot) and one type of feature (stem). After the permutation and combination of these defects, a total of 18 categories were obtained after the screening, combined with the actual situation. Based on the VIT model, a fine-grained defect detection link was added to the network for the analysis layer of the major defect hazard level and the detection of secondary defects. The improved network model has an average recognition accuracy rate of 96.21% for multiple defect detection of green plums, which is better than that of the VGG16 network, the Desnet121 network, the Resnet18 network, and the WideResNet50 network.
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Xiang, Hui Yu, Jia Jun Huang, Baoan Han, and Zhe Li. "Research of Vision Detection System Based on Regional Morphology." Applied Mechanics and Materials 415 (September 2013): 357–60. http://dx.doi.org/10.4028/www.scientific.net/amm.415.357.
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With the gradual development of industrial technology, visual measuring technique has been widely used in industrial field. In the automatic production, it is used in the analysis of objects characteristics, the detection of working condition and the control of quality. This paper mainly studies how the machine vision theory is applied in the practical detections, and develops the overall visual detection platform. It uses the serial port communication between VC++6.0 and Single-Chip Microcomputer to control the experimental platform. In order to realize to test the production defects, it uses the knowledge of mathematical morphology to put forward a defect detection method of production image based on regional morphology. It can realize to simulate actual production line, and has a certain development prospects for real-time detections of product quality.
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Rasheed, Aqsa, Bushra Zafar, Amina Rasheed, et al. "Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review." Mathematical Problems in Engineering 2020 (November 16, 2020): 1–24. http://dx.doi.org/10.1155/2020/8189403.
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There are different applications of computer vision and digital image processing in various applied domains and automated production process. In textile industry, fabric defect detection is considered as a challenging task as the quality and the price of any textile product are dependent on the efficiency and effectiveness of the automatic defect detection. Previously, manual human efforts are applied in textile industry to detect the defects in the fabric production process. Lack of concentration, human fatigue, and time consumption are the main drawbacks associated with the manual fabric defect detection process. Applications based on computer vision and digital image processing can address the abovementioned limitations and drawbacks. Since the last two decades, various computer vision-based applications are proposed in various research articles to address these limitations. In this review article, we aim to present a detailed study about various computer vision-based approaches with application in textile industry to detect fabric defects. The proposed study presents a detailed overview of histogram-based approaches, color-based approaches, image segmentation-based approaches, frequency domain operations, texture-based defect detection, sparse feature-based operation, image morphology operations, and recent trends of deep learning. The performance evaluation criteria for automatic fabric defect detection is also presented and discussed. The drawbacks and limitations associated with the existing published research are discussed in detail, and possible future research directions are also mentioned. This research study provides comprehensive details about computer vision and digital image processing applications to detect different types of fabric defects.
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Wang, Jinmian, Jiping Qiao, and Meicen Guo. "Research on bearing surface defect detection system based on machine vision." Journal of Physics: Conference Series 2290, no. 1 (2022): 012061. http://dx.doi.org/10.1088/1742-6596/2290/1/012061.
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Abstract Aiming at the shortcomings of manual detection methods for surface defects of bearing outer rings, a surface defect detection method based on machine vision is proposed. The common defects on the bearing surface are scratches, cracks, and peeling. The defect images are analyzed by the LabVIEW image processing module. Make fast and accurate inspections. In this paper, a CMOS industrial camera is used to obtain the bearing image, and the gray histogram of the image is analyzed to determine whether it is a defective bearing. In order to solve the problem of uneven illumination in image segmentation, it is proposed to first use morphological processing for background. Then, the difference map containing defect information is obtained, and then the Otsu method is used for image segmentation; an edge detection method is designed based on morphology, which can achieve complete edge extraction and good denoising effect. Experiments show that the method has strong practicability and self-adaptability, and can accurately detect bearing surface defects.
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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.
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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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Wu, Yu, and Yanjie Lu. "An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine." Measurement and Control 52, no. 7-8 (2019): 1102–10. http://dx.doi.org/10.1177/0020294019858175.
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Defects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detection has become an inevitable trend because machine vision technology can greatly improve accuracy and efficiency; therefore, it is of great practical value to study automatic detection technology of the surface defects encountered in packaging boxes. In this study, machine vision and machine learning were combined to examine a surface defect detection method based on support vector machine where defective products are eliminated by a sorting robot system. After testing, the support vector machine training model using radial basis function kernel detects three kinds of defects at the same time under the ideal condition of parameter selection, and the effective detection rate is 98.0296%.
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Lu, Manhuai, and Chin-Ling Chen. "Detection and Classification of Bearing Surface Defects Based on Machine Vision." Applied Sciences 11, no. 4 (2021): 1825. http://dx.doi.org/10.3390/app11041825.
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Surface defects on bearings can directly affect the service life and reduce the performance of equipment. At present, the detection of bearing surface defects is mostly done manually, which is labor-intensive and results in poor stability. To improve the inspection speed and the defect recognition rate, we proposed a bearing surface defect detection and classification method using machine vision technology. The method makes two main contributions. It proposes a local multi-neural network (Lc-MNN) image segmentation algorithm with the wavelet transform as the classification feature. The precision segmentation of the defect image is accomplished in three steps: wavelet feature extraction, Lc-MNN region division, and Lc-MNN classification. It also proposes a feature selection algorithm (SCV) that makes comprehensive use of scalar feature selection, correlation analysis, and vector feature selection to first remove similar features through correlation analysis, further screen the results with a scalar feature selection algorithm, and finally select the classification features using a feature vector selection algorithm. Using 600 test samples with three types of defect in the experiment, an identification rate of 99.5% was achieved without the need for large-scale calculation. The comparison tests indicated that the proposed method can achieve efficient feature selection and defect classification.
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Hou, Jiatong, Bo You, Jiazhong Xu, Tao Wang, and Moran Cao. "Surface Defect Detection of Preform Based on Improved YOLOv5." Applied Sciences 13, no. 13 (2023): 7860. http://dx.doi.org/10.3390/app13137860.
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This paper proposes a lightweight detection model based on machine vision, YOLOv5-GC, to improve the efficiency and accuracy of detecting and classifying surface defects in preforming materials. During this process, clear images of the entire surface are difficult to obtain due to the stickiness, high reflectivity, and black resin of the thermosetting plain woven prepreg. To address this challenge, we built a machine vision platform equipped with a linescan camera and high-intensity linear light source that captures surface images of the material during the preforming process. To solve the problem of defect detection in the case of extremely small and imbalanced samples, we adopt a transfer learning approach based on the YOLOv5 neural network for defect recognition and introduce a coordinate attention and Ghost Bottleneck module to improve recognition accuracy and speed. Experimental results demonstrate that the proposed approach achieves rapid and high-precision identification of surface defects in preforming materials, outperforming other state-of-the-art methods. This work provides a promising solution for surface defect detection in preforming materials, contributing to the improvement of composite material quality.
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Luo, Kui, Xuan Kong, Jie Zhang, Jiexuan Hu, Jinzhao Li, and Hao Tang. "Computer Vision-Based Bridge Inspection and Monitoring: A Review." Sensors 23, no. 18 (2023): 7863. http://dx.doi.org/10.3390/s23187863.
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Bridge inspection and monitoring are usually used to evaluate the status and integrity of bridge structures to ensure their safety and reliability. Computer vision (CV)-based methods have the advantages of being low cost, simple to operate, remote, and non-contact, and have been widely used in bridge inspection and monitoring in recent years. Therefore, this paper reviews three significant aspects of CV-based methods, including surface defect detection, vibration measurement, and vehicle parameter identification. Firstly, the general procedure for CV-based surface defect detection is introduced, and its application for the detection of cracks, concrete spalling, steel corrosion, and multi-defects is reviewed, followed by the robot platforms for surface defect detection. Secondly, the basic principle of CV-based vibration measurement is introduced, followed by the application of displacement measurement, modal identification, and damage identification. Finally, the CV-based vehicle parameter identification methods are introduced and their application for the identification of temporal and spatial parameters, weight parameters, and multi-parameters are summarized. This comprehensive literature review aims to provide guidance for selecting appropriate CV-based methods for bridge inspection and monitoring.
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Dou, Xiaohan, Chengqi Xue, Gengpei Zhang, and Zhihao Jiang. "Internal thread defect detection system based on multi-vision." PLOS ONE 19, no. 5 (2024): e0304224. http://dx.doi.org/10.1371/journal.pone.0304224.
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In the realm of industrial inspection, the precise assessment of internal thread quality is crucial for ensuring mechanical integrity and safety. However, challenges such as limited internal space, inadequate lighting, and complex geometry significantly hinder high-precision inspection. In this study, we propose an innovative automated internal thread detection scheme based on machine vision, aimed at addressing the time-consuming and inefficient issues of traditional manual inspection methods. Compared with other existing technologies, this research significantly improves the speed of internal thread image acquisition through the optimization of lighting and image capturing devices. To effectively tackle the challenge of image stitching for complex thread textures, an internal thread image stitching technique based on a cylindrical model is proposed, generating a full-view thread image. The use of the YOLOv8 model for precise defect localization in threads enhances the accuracy and efficiency of detection. This system provides an efficient and intuitive artificial intelligence solution for detecting surface defects on geometric bodies in confined spaces.
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Guo, Jian. "Computer vision-based algorithm for precise defect detection and classification in photovoltaic modules." PeerJ Computer Science 10 (October 15, 2024): e2148. http://dx.doi.org/10.7717/peerj-cs.2148.
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In recent years, driven by advancements in the photovoltaic industry, solar power generation has emerged as a crucial energy source in China and the globe. A progressive annotation approach is employed to pinpoint and label defect samples to enhance the precision of automated detection technology for minor defects within photovoltaic modules. Subsequently, computer vision techniques are harnessed to segment photovoltaic modules and defect samples amidst intricate backgrounds accurately. Finally, a transfer learning training model is deployed to classify and identify defects effectively. The results indicate that the mask-region convolutional neural network model achieves remarkable accuracy and recall rates of 98.7% and 0.913, respectively. Furthermore, the detection speed and inference time are 280.69 frames per second and 3.53 ms, respectively. In essence, the defect detection and classification algorithm utilizing computer vision techniques significantly enhances the precision of automated detection technology in identifying minor defects within complex environments. This advancement holds profound practical significance in ensuring photovoltaic modules’ quality and operational reliability.
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Zhukov, Alexey, Alain Rivero, Jenny Benois-Pineau, Akka Zemmari, and Mohamed Mosbah. "A Hybrid System for Defect Detection on Rail Lines through the Fusion of Object and Context Information." Sensors 24, no. 4 (2024): 1171. http://dx.doi.org/10.3390/s24041171.
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Defect detection on rail lines is essential for ensuring safe and efficient transportation. Current image analysis methods with deep neural networks (DNNs) for defect detection often focus on the defects themselves while ignoring the related context. In this work, we propose a fusion model that combines both a targeted defect search and a context analysis, which is seen as a multimodal fusion task. Our model performs rule-based decision-level fusion, merging the confidence scores of multiple individual models to classify rail-line defects. We call the model “hybrid” in the sense that it is composed of supervised learning components and rule-based fusion. We first propose an improvement to existing vision-based defect detection methods by incorporating a convolutional block attention module (CBAM) in the you only look once (YOLO) versions 5 (YOLOv5) and 8 (YOLOv8) architectures for the detection of defects and contextual image elements. This attention module is applied at different detection scales. The domain-knowledge rules are applied to fuse the detection results. Our method demonstrates improvements over baseline models in vision-based defect detection. The model is open for the integration of modalities other than an image, e.g., sound and accelerometer data.
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Woo, Jimyeong, and Heoncheol Lee. "Nonlinear and Dotted Defect Detection with CNN for Multi-Vision-Based Mask Inspection." Sensors 22, no. 22 (2022): 8945. http://dx.doi.org/10.3390/s22228945.
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This paper addresses the problem of nonlinear and dotted defect detection for multi-vision-based mask inspection systems in mask manufacturing lines. As the mask production amounts increased due to the spread of COVID-19 around the world, the mask inspection systems require more efficient defect detection algorithms. However, the traditional computer vision detection algorithms suffer from various types and very small sizes of the nonlinear and dotted defects on masks. This paper proposes a deep learning-based mask defect detection method, which includes a convolutional neural network (CNN) and efficient preprocessing. The proposed method was developed to be applied to real manufacturing systems, and thus all the training and inference processes were conducted with real data produced by real mask manufacturing systems. Experimental results show that the nonlinear and dotted defects were successfully detected by the proposed method, and its performance was higher than the previous method.
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Dong, Guanping, Shanwei Sun, Zixi Wang, et al. "Application of machine vision-based NDT technology in ceramic surface defect detection – a review." Materials Testing 64, no. 2 (2022): 202–19. http://dx.doi.org/10.1515/mt-2021-2012.
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Abstract For its good mechanical, thermal, and chemical property, ceramic materials are widely used in construction, chemical industry, electric power, communication and other fields. However, due to its particularity and complex production process, quality problems usually occur, of which the most common one is surface defects. For ceramic products, the defects are usually small and complicated, and manual methods are difficult to ensure the accuracy and speed of detection. Relevant researchers have proposed a variety of machine vision-based ceramic defect detection methods, but these methods still need to break through in solving the key problems of ceramic surface glaze reflection, complex detection environment, low algorithm efficiency and low real-time performance. To this end, this article reviews the application status of machine vision on ceramic surface defect detection in recent years, summarizes and analyzes the existing non-destructive testing (NDT) technology method, and points out the main factors that affect the development of ceramic surfaces defect detection technology and puts forward the corresponding solutions.
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Bhavanasi, Geethika, Davy Neven, Manuel Arteaga, Sina Ditzel, Sam Dehaeck, and Abdellatif Bey-Temsamani. "Enhanced Vision-Based Quality Inspection: A Multiview Artificial Intelligence Framework for Defect Detection." Sensors 25, no. 6 (2025): 1703. https://doi.org/10.3390/s25061703.
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Automated defect detection is a critical component of modern industrial quality control. However, it is particularly difficult to identify subtle defects such as scratches on metallic surfaces. Therefore, this paper investigates the effectiveness of multiview deep learning approaches for improved defect detection by implementing and comparing early and late fusion methodologies. We propose MV-UNet, a novel early fusion architecture that aligns and aggregates multiview features using a transformation block to enhance detection accuracy. To evaluate performance, we conduct our experiments on a recorded metallic plates dataset, comparing the traditional single-view inspection to both late and early fusion methods. Our results demonstrate that both the early and late fusion methods improve detection accuracy over the mono-view baseline, with our MV-UNet achieving the hightest F1-score (0.942). Additionally, we introduce adapted precision–recall metrics designed for segmentation-based defect detection, addressing the limitations of traditional IoU-based evaluations. These tailored metrics more accurately reflect defect localization performance, particularly for thin, elongated scratches. Our findings highlight the advantages of early fusion for industrial defect detection, providing a robust and scalable approach to multiview analysis.
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Chen, Zhiqiang, Jiehang Deng, Qiuqin Zhu, Hailun Wang, and Yi Chen. "A Systematic Review of Machine-Vision-Based Leather Surface Defect Inspection." Electronics 11, no. 15 (2022): 2383. http://dx.doi.org/10.3390/electronics11152383.
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Machine-vision-based surface defect inspection is one of the key technologies to realize intelligent manufacturing. This paper provides a systematic review on leather surface defect inspections based on machine vision. Leather products are regarded as the most traded products all over the world. Automatic detection, location, and recognition of leather surface defects are very important for the intelligent manufacturing of leather products, and are challenging but noteworthy tasks. This work investigates a large amount of literature related to leather surface defect inspection. In addition, we also investigate and evaluate the performance of some edge detectors and threshold detectors for leather defect detection, and the identification accuracy of the classical machine learning method SVM for leather surface defect identification. A detailed and methodical review of leather surface defect inspection with image analysis and machine learning is presented. Main challenges and future development trends are discussed for leather surface defect inspection, which can be used as a source of guidelines for designing and developing new solutions in this field.
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Deng, Xinlan, Min He, Jingwen Zheng, Liang Qin, and Kaipei Liu. "Research Progress on Power Visual Detection of Overhead Line Bolt Defects Based on UAV Images." Drones 8, no. 9 (2024): 442. http://dx.doi.org/10.3390/drones8090442.
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In natural environments, the connecting bolts of overhead lines and power towers are prone to loosening and missing, posing potential risks to the safe and stable operation of the power system. This paper reviews the challenges in bolt defect detection using power vision technology, with a particular focus on unmanned aerial vehicle (UAV) images. These UAV images offer a cost-effective and flexible solution for detecting bolt defects. However, challenges remain, including missed detection due to the small size of bolts, false detection caused by dense and occluded bolts, and underfitting resulting from imbalanced bolt defect datasets. To address these issues, this paper summarizes solutions that leverage deep learning algorithms. An experimental analysis is conducted on a dataset derived from UAV inspections, comparing the detection characteristics and visualizing the results of various algorithms. The paper also discusses future trends in the application of UAV-based power vision technology for bolt defect detection, providing insights for the advancement of intelligent power inspection.
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Chen, Shuguang, Jingyang Gao, Di Zhao, Pinjie Xu, and Tian Zhang. "Detection of Chip Layered Defects Based on Dual Focus Mechanism." Journal of Physics: Conference Series 2216, no. 1 (2022): 012091. http://dx.doi.org/10.1088/1742-6596/2216/1/012091.
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Abstract Chip layering defects affect the performance of chips and lead to the failure of chips, so chip layering defects detection is an important step in the quality acceptance of chip production. Chip layering defects, which are characterized by insignificant color change in defect area, small defect area and difficult localization, bring challenges to traditional detection. In recent years, deep learning has shown its powerful ability to solve complex problems in computer vision. In this paper, semantic segmentation method is used to study the problem of chip hierarchical defect detection. Dual focus mechanism first applies whiteboard network structure to identify the true hierarchical area. Afterwards the defective layer area and the original map, the layered defect is recognized in the whiteboard attention. Since the contrast of the layered defect is not obvious, the precise layered defect tag extraction is another important factor affecting network performance. Based on the fuzzy-c-mean clustering algorithm and expert acceptance principle, obtaining the precise layered defect label, the practicality of this method is further enhanced. The effectiveness of the method for detecting the chip layering defects is verified by testing the chip image provided by Huawei.
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Zhang, Xinman, Weiyong Gong, and Xuebin Xu. "Magnetic Ring Multi-Defect Stereo Detection System Based on Multi-Camera Vision Technology." Sensors 20, no. 2 (2020): 392. http://dx.doi.org/10.3390/s20020392.
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Magnetic rings are the most widely used magnetic material product in industry. The existing manual defect detection method for magnetic rings has high cost, low efficiency and low precision. To address this issue, a magnetic ring multi-defect stereo detection system based on multi-camera vision technology is developed to complete the automatic inspection of magnetic rings. The system can detect surface defects and measure ring height simultaneously. Two image processing algorithms are proposed, namely, the image edge removal algorithm (IERA) and magnetic ring location algorithm (MRLA), separately. On the basis of these two algorithms, connected domain filtering methods for crack, fiber and large-area defects are established to complete defect inspection. This system achieves a recognition rate of 100% for defects such as crack, adhesion, hanger adhesion and pitting. Furthermore, the recognition rate for fiber and foreign matter defects attains 92.5% and 91.5%, respectively. The detection speed exceeds 120 magnetic rings per minutes, and the precision is within 0.05 mm. Both precision and speed meet the requirements of real-time quality inspection in actual production.
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Bao, Junmin, Junfeng Jing, and Yaohua Xie. "A defect detection system of glass tube yarn based on machine vision." Journal of Industrial Textiles 53 (January 2023): 152808372311528. http://dx.doi.org/10.1177/15280837231152878.
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Tube yarn is also called glass fiber spun yarn. Due to the excellent properties of glass fiber, various industrial products based on glass fiber are used in a variety of industries. As the most obvious factor affecting the quality of products, quality detection of glass fiber yarns is very important for companies. Due to traditional defect detection relies on the experience and subjective factors of workers, which makes many different views on the same defect. Some traditional methods limit the solution to specific types of defects and do not accurately detect various defects. In this paper, we propose a comprehensive defect detection system of tube yarn via combining machine vision and deep learning methods. In whole system, we inspect the weight through a weight sensor firstly. Then, we propose a multi-scale cross-fusion attention module to improve the MobileNetV2, and combine with machine vision image feature extraction method for hairiness detection. Finally, the modified MobileNetV2 network is used as the backbone of YOLOX network, making the YOLOX is lighter and achieve more efficiently stain detection of tube yarn. Then, the detection results are used to determine whether the glass tube yarn has passed. In addition, we establish an effective and sufficient amount of tube yarn defects dataset. The experimental results show that the proposed hairiness detection algorithm achieve 96% accuracy with 160+ FPS, and the surface stain detection algorithm achieve 0.89 mAP with 71+ FPS on the tube yarn dataset. The system is efficient, precise, and can be applied to actual production.
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Huang, Hanjie, Bin Zhou, Songxiao Cao, Tao Song, Zhipeng Xu, and Qing Jiang. "Aluminum Reservoir Welding Surface Defect Detection Method Based on Three-Dimensional Vision." Sensors 25, no. 3 (2025): 664. https://doi.org/10.3390/s25030664.
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Welding is an important process in the production of aluminum reservoirs for motor vehicles. The welding quality affects product performance. However, rapid and accurate detection of weld surface defects remains a huge challenge in the field of industrial automation. To address this problem, we proposed a 3D vision-based aluminum reservoir welding surface defect detection method. First of all, a scanning system based on laser line scanning camera was constructed to acquire the point cloud data of weld seams on the aluminum reservoir surface. Next, a planar correction algorithm was used to adjust the slope of the contour line according to the slope of the contour line in order to minimize the effect of systematic disturbances when acquiring weld data. Then, the surface features of the weld, including curvature and normal vector direction, were extracted to extract holes, craters, and undercut defects. For better extraction of the defect, a double-aligned template matching method was used to ensure comprehensive extraction and measurement of defect areas. Finally, the detected defects were categorized according to their morphology. Experimental results show that the proposed method using 3D laser scanning data can detect and classify typical welding defects with an accuracy of more than 97.1%. Furthermore, different types of defects, including holes, undercuts, and craters, can also be accurately detected with precision 98.9%.
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Gan, Wenlong. "Semiconductor Wafer Defect Detection Based on Machine Learning." Transactions on Computer Science and Intelligent Systems Research 6 (October 17, 2024): 128–33. http://dx.doi.org/10.62051/wr098130.
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Semiconductor wafers are widely used in integrated circuit chips. Because of the complex production process, it is easy to cause various defects. Therefore, the defect detection of semiconductor wafers is an important means to ensure their yield and productivity. This paper mainly expounds on the detection method of wafer defects combined with the machine vision algorithm, including the CNN model, and the classification of the learning-based methods. Current problems and future prospects for total crystalline circle defect detection are discussed in the end.
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Kumar, A. "Computer-Vision-Based Fabric Defect Detection: A Survey." IEEE Transactions on Industrial Electronics 55, no. 1 (2008): 348–63. http://dx.doi.org/10.1109/tie.1930.896476.
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Li, Chunlei, Guangshuai Gao, Zhoufeng Liu, Miao Yu, and Di Huang. "Fabric Defect Detection Based on Biological Vision Modeling." IEEE Access 6 (2018): 27659–70. http://dx.doi.org/10.1109/access.2018.2841055.
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Zhao, Chundong, Xiaoyan Chen, Dongyang Zhang, Jianyong Chen, Kuifeng Zhu, and Yanjie Su. "Wafer defect detection method based on machine vision." Proceedings of International Conference on Artificial Life and Robotics 25 (January 13, 2020): 795–99. http://dx.doi.org/10.5954/icarob.2020.os15-2.
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Liu, Zhichao, and Baida Qu. "Machine vision based online detection of PCB defect." Microprocessors and Microsystems 82 (April 2021): 103807. http://dx.doi.org/10.1016/j.micpro.2020.103807.
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Yu, Naigong, Hongzheng Li, and Qiao Xu. "A full-flow inspection method based on machine vision to detect wafer surface defects." Mathematical Biosciences and Engineering 20, no. 7 (2023): 11821–46. http://dx.doi.org/10.3934/mbe.2023526.
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<abstract><p>The semiconductor manufacturing industry relies heavily on wafer surface defect detection for yield enhancement. Machine learning and digital image processing technologies have been used in the development of various detection algorithms. However, most wafer surface inspection algorithms are not be applied in industrial environments due to the difficulty in obtaining training samples, high computational requirements, and poor generalization. In order to overcome these difficulties, this paper introduces a full-flow inspection method based on machine vision to detect wafer surface defects. Starting with the die image segmentation stage, where a die segmentation algorithm based on candidate frame fitting and coordinate interpolation is proposed for die sample missing matching segmentation. The method can segment all the dies in the wafer, avoiding the problem of missing dies splitting. After that, in the defect detection stage, we propose a die defect anomaly detection method based on defect feature clustering by region, which can reduce the impact of noise in other regions when extracting defect features in a single region. The experiments show that the proposed inspection method can precisely position and segment die images, and find defective dies with an accuracy of more than 97%. The defect detection method proposed in this paper can be applied to inspect wafer manufacturing.</p></abstract>
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Gao, Xian. "Research on automated defect detection system based on computer vision." Applied and Computational Engineering 101, no. 1 (2024): 192–97. http://dx.doi.org/10.54254/2755-2721/101/20241010.
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Abstract. With the intensification of market competition and consumer demand for product quality enhancement, people's development of industrialization is also more and more in-depth, the production scale and complexity of a variety of products is also increasing. However, in the process of automated production, product defects are unavoidable due to various factors such as equipment, environment, and human influence. The traditional human inspection method is inefficient, costly, and subjective, making it difficult to meet the needs of modern industrial production. Therefore, the need for an efficient, accurate and automated inspection system is urgent. Based on the current social situation, this paper will introduce the automated defect detection system based on computer vision, which is widely used in many fields, and has a great guarantee for people in the production of products with high quality and low cost, based on the needs in these areas, this paper will firstly introduce the relevant algorithms and their main contributions to the application scenarios of computer vision technology, and then introduce the key technologies of computer vision in defect detection in the key technology.
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Wang, Fu Juan, and Yong Qiang Dong. "Surface Defect Detection of Chinese Dates Based on Machine Vision." Advanced Materials Research 403-408 (November 2011): 1356–59. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1356.
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In order to implement the accuracy and robust of Chinese dates surface defect detection based on machine vision techniques on line, the method of detection for Chinese dates was studied. The Chinese date is segmented from the background in RGB color space by analyzing respectively the histogram of R, G and B channel to make comparing among them and find an optimal one, resulting in good contrast between Chinese date and background in G channel. The brightness of the damaged area edge changed clearly on the whole Chinese dates area according to the gray image of R, G and B channel, especially in G channel. It shows the gray value of the defect area breaking obviously. So the damaged area could be detected by edge detect, through image thinning the defect edge was extracted. Furthermore, the geometry parameters of defect edge were calculated, these parameters could used to distinguish the defect area with the fruit area and the degree of the defect area. Experiments result proved the methods is effective to detect defect area of Chinese date.
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Han, Young-Joo, and Ha-Jin Yu. "Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data." Applied Sciences 10, no. 7 (2020): 2511. http://dx.doi.org/10.3390/app10072511.
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As defect detection using machine vision is diversifying and expanding, approaches using deep learning are increasing. Recently, there have been much research for detecting and classifying defects using image segmentation, image detection, and image classification. These methods are effective but require a large number of actual defect data. However, it is very difficult to get a large amount of actual defect data in industrial areas. To overcome this problem, we propose a method for defect detection using stacked convolutional autoencoders. The autoencoders we proposed are trained by using only non-defect data and synthetic defect data generated by using the characteristics of defect based on the knowledge of the experts. A key advantage of our approach is that actual defect data is not required, and we verified that the performance is comparable to the systems trained using real defect data.
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Huang, Liang, Qiongxia Shen, Chao Jiang, and You Yang. "Deep Neural Network-Based Cigarette Filter Defect Detection System with FPGA Acceleration for Online Recognition." Sensors 24, no. 20 (2024): 6752. http://dx.doi.org/10.3390/s24206752.
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In the cigarette manufacturing industry, machine vision and artificial intelligence algorithms have been employed to improve production efficiency by detecting product defects. However, achieving both high accuracy and real-time defect detection for cigarettes with complex patterns remains a challenge. To address these issues, this study proposes a model based on RESNET18, combined with a feature enhancement algorithm, to improve detection accuracy. Additionally, a method is designed to deploy the model on a field-programmable gate array (FPGA) with high parallel processing capabilities to achieve high-speed detection. Experimental results demonstrate that the proposed detection model achieves a detection accuracy of 95.88% on a cigarette filter defect dataset with an end-to-end detection speed of only 9.38 ms.
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Jiang, Zhihao, Xiaohan Dou, Xiaolong Liu, Chengqi Xue, Anqi Wang, and Gengpei Zhang. "Internal Thread Defect Generation Algorithm and Detection System Based on Generative Adversarial Networks and You Only Look Once." Sensors 24, no. 17 (2024): 5636. http://dx.doi.org/10.3390/s24175636.
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In the field of industrial inspection, accurate detection of thread quality is crucial for ensuring mechanical performance. Existing machine-vision-based methods for internal thread defect detection often face challenges in efficient detection and sufficient model training samples due to the influence of mechanical geometric features. This paper introduces a novel image acquisition structure, proposes a data augmentation algorithm based on Generative Adversarial Networks (GANs) to effectively construct high-quality training sets, and employs a YOLO algorithm to achieve internal thread defect detection. Through multi-metric evaluation and comparison with external threads, high-similarity internal thread image generation is achieved. The detection accuracy for internal and external threads reached 94.27% and 93.92%, respectively, effectively detecting internal thread defects.
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Rajagounder, Rajamani. "A machine vision system for inspecting mechanical parts." Machine Graphics and Vision 34, no. 1 (2025): 75–86. https://doi.org/10.22630/mgv.2025.34.1.4.
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Computer vision-based inspection has become widely used in manufacturing industries for part identification, dimensional inspection, and guiding material handling systems. Defect-free production cannot be achieved with sampling inspection methods; therefore, a 100 percentage inspection approach is mandatory to meet the zero-defect goals of manufacturing industries. Achieving this is possible with advanced technologies, such as vision-based inspection systems. In this study, a vision-based inspection system is proposed for part identification, defect detection, and dimensional measurement. The system is validated using machined parts, including a Druck plate, Pressure plate, and Retainer. A part identification algorithm is developed based on a geometry search approach. The inspection algorithm classifies parts based on edge relationships, utilizing edge detection techniques to identify each part's geometric features. Surface defects are identified by analyzing the pixel intensity gradients within defective regions. The system measures part dimensions using a vision system, with results comparable to those obtained from a coordinate measuring machine.
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Tian, Hongzhi, Dongxing Wang, Jiangang Lin, Qilin Chen, and Zhaocai Liu. "Surface Defects Detection of Stamping and Grinding Flat Parts Based on Machine Vision." Sensors 20, no. 16 (2020): 4531. http://dx.doi.org/10.3390/s20164531.
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Currently, surface defect detection of stamping grinding flat parts is mainly undertaken through observation by the naked eye. In order to improve the automatic degree of surface defects detection in stamping grinding flat parts, a real-time detection system based on machine vision is designed. Under plane illumination mode, the whole region of the parts is clear and the outline is obvious, but the tiny defects are difficult to find; Under multi-angle illumination mode, the tiny defects of the parts can be highlighted. In view of the above situation, a lighting method combining plane illumination mode with multi-angle illumination mode is designed, and five kinds of defects are automatically detected by different detection methods. Firstly, the parts are located and segmented according to the plane light source image, and the defects are detected according to the gray anomaly. Secondly, according to the surface of the parts reflective characteristics, the influence of the reflection on the image is minimized by adjusting the exposure time of the camera, and the position and direction of the edge line of the gray anomaly region of the multi-angle light source image are used to determine whether the anomaly region is a defect. The experimental results demonstrate that the system has a high detection success rate, which can meet the real-time detection rEquation uirements of a factory.
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Ren, Chuan, Xiao Yu Xiu, and Guo Hui Zhou. "Research on Surface Defect Detection Technique of Rolling Element Based on Computer Vision." Advanced Materials Research 1006-1007 (August 2014): 773–78. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.773.
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This paper proposed a new method of surface defect detection of rolling element based on computer vision, which adopted CCD digital camera as image sensor, and used digital image processing techniques to defect the surface defects of rolling element. The main steps include collect image, use an improved median filter to reduce the noise, increase or decrease the exposure to achieve the image enhancement, create a binary image with threshold method and detect the edge of the image, and use subtraction method for surface defects identification. The experiment indicates that the above methods the advantages of simple, the capability of noise resistance, high speed processing and better real-time.
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Zhou, Cong Ling, Jun Qiang Wu, Yong Qiang Wang, and Zeng Pu Xu. "A Soldering Defect Inspection System of a Special Integrated Circuit Board Based on Computer Vision." Advanced Materials Research 650 (January 2013): 543–47. http://dx.doi.org/10.4028/www.scientific.net/amr.650.543.
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This paper introduces a soldering defect inspection system for a special integrated circuit board aided by the computer vision. Space occluder is fixed on this special integrated circuit board, which makes the light blocked from the CCD camera to the chip pins to be inspected. This system can inspect the light blocked soldering defects of the chip pins through the structure design of hardware system and the software system. It is a cheap but automatic soldering defect inspecting system, and can do the soldering defect detection instead of manual visual inspection, and improve the detection speed and stability.
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Choi, Youngwoon, Hyunseok Lee, and Sang Won Lee. "Defect Data Augmentation Method for Robust Image-based Product Inspection." PHM Society European Conference 8, no. 1 (2024): 8. http://dx.doi.org/10.36001/phme.2024.v8i1.4068.
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In this paper, we develop a model for detecting defects in fabric products based on an object segmentation algorithm, including a novel image data augmentation method to enhance the robustness. First, a vision-based inspection system is established to collect image data of the fabric products. The three types of fabric defects, such as a hole, a stain, and a dyeing defect, are considered. To enhance defect detection accuracy and robustness, a novel image data augmentation method, referred to as the defect-area cut-mix, is proposed. In this method, the shapes that are the same as each defect are extracted using the masks, and then they are added to non-defective fabric images. Second, an ensemble process is implemented by combining the results of two models, one with high sensitivity in defect diagnosis and the other with lower sensitivity. The results demonstrated that the model trained on the augmented dataset exhibits improved metrics such as intersection over union and classification accuracy in defect detection on the test dataset.
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Liao, Wenzhi, and Roeland De Geest. "Active learning for gear defect detection in gearboxes." PHM Society European Conference 8, no. 1 (2024): 10. http://dx.doi.org/10.36001/phme.2024.v8i1.4050.
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Condition monitoring of gears in gearboxes is crucial to ensure performance and minimizing downtime in many industrial applications including wind turbines and automotive. Monitoring techniques using indirect measurements (i.e. accelerometers, microphones, acoustic emission sensors and encoders, etc.) face challenges, including the defect interpretation and characterization. Vision-based gear condition monitoring, as a direct method to observe gear defects, has the capability to give a precise indication of the starting point of a potential surface failure, but suffers from the image annotations (to train a reliable vision model for automatic defect detection of gears). In this paper, we propose an active learning framework for vision-based condition monitoring, to reduce the human annotation effort by only labelling the most informative examples. In particular, we first train a deep learning model on limited training dataset (annotated randomly) to detect pitting defects. To select which samples have the highest priority to be annotated, we compute the model's uncertainty on all remaining unlabeled examples. Bayesian active learning by disagreement is exploited to estimate the uncertainty of the unlabeled samples. We select the samples with the highest values of uncertainty to be annotated first. Experimental results from defect detection of gears in gearboxes show that with less than 6 times image annotations, we can achieve similar performances.