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Relevant bibliographies by topics / Vision-based defect detection

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Academic literature on the topic 'Vision-based defect detection'

Author: Grafiati

Published: 4 June 2025

Last updated: 6 July 2025

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Journal articles on the topic "Vision-based defect detection"

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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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Dissertations / Theses on the topic "Vision-based defect detection"

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Widoyoko, Agus. "Evaluation of color-based machine vision for lumber processing in furniture rough mills." Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-08222008-063556/.

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Frauenthal, Jay Matthew. "Design and Exploration of a Computer Vision Based Unmanned Aerial Vehicle for Railroad Health Applications." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/56559.

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Railroad tracks require consistent and periodic monitoring to ensure safety and reliability. Unmanned Aerial Vehicles (UAVs) have great potential because they are not constrained to the track, allowing trains to continue running while the UAV is inspecting. Also, they can be quickly deployed without human intervention. For these reasons, the first steps towards creating a track-monitoring UAV system have been completed. This thesis focuses on the design of algorithms to be deployed on a UAV for the purpose of monitoring the health of railroad tracks. Before designing the algorithms, the first steps were to design a rough physical structure of the final product. A small multirotor or fixed-wing UAV will be used with a gimbaled camera mounted on the belly. The camera will take images of the tracks while the onboard computer processes the images. The computer will locate the tracks in the image as well as perform defect detection on those tracks. Algorithms were implemented once a rough physical structure of the product was completed. These algorithms detect and follow rails through a video feed and detect defects in the rails. The rail following algorithm is based on a custom-designed masking technique that locates rails in images. A defect detection algorithm was also created. This algorithm detect defects by analyzing gradient data on the rail surface.<br>Master of Science

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DIMAS, FIRMANDA AL RIZA. "Potato surface defect detection using machine vision systems based on spectral reflection and fluorescence characteristics in the UV-NIR region." Kyoto University, 2019. http://hdl.handle.net/2433/244556.

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Kyoto University (京都大学)<br>0048<br>新制・課程博士<br>博士(農学)<br>甲第22075号<br>農博第2367号<br>新制||農||1072(附属図書館)<br>学位論文||R1||N5229(農学部図書室)<br>京都大学大学院農学研究科地域環境科学専攻<br>(主査)教授近藤直, 准教授小川雄一, 教授清水浩<br>学位規則第4条第1項該当

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Ngan, Yuk-tung Henry. "Motif-based method for patterned texture defect detection." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/b40203608.

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Ngan, Yuk-tung Henry, and 顏旭東. "Motif-based method for patterned texture defect detection." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40203608.

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Wu, Shih-Chieh, and 吳世杰. "Machine vision-based defect detection of solar cells/modulesin electroluminescence images." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/24265075299846710310.

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碩士<br>元智大學<br>工業工程與管理學系<br>99<br>Finger interrupt, micro-crack and breakage are severer defects in the multicrystalline solar cell and cannot be observed by the naked eyes or the conventional CCD camera. The Electroluminescence (EL) imaging technique can be used instead to highlight these defects. In this study, a machine vision scheme is proposed to detect the defects of solar cells and solar modules in EL images. The EL image of a multicrystalline solar cell presents a heterogeneously textured pattern, which makes the defect detection task extremely difficult. The first topic in this research focuses on defect detection of the multicrystalline solar cells in EL images. Since the finger interrupt and crack are line- or bar-shaped, the Fourier transform is used to eliminate suspected defects and results in a defect-free surface in the reconstructed image. By subtracting the reconstructed image from the original image, the defects can be distinctly enhanced in the difference image. Then, the defect is effectively segmented by a simple statistical control limit. The second topic of this research aims at defect inspection in the solar module, which is formed by a matrix of solar cells through series and parallel combinations. The Independent component analysis (ICA) is used to generate the basis images from defect-free solar cells. Each test image is reconstructed by a linear combination of the basis images. The accumulated gray-level difference between the test image and the reconstructed image is effectively used as a discrimination to detect the presence of defect in the solar cell subimages. In the experimental results of solar cell EL images, the Fourier transform reconstruction scheme can effectively detect fingers interrupt, micro-crack, and breakage. The average computation time is 0.29 seconds for a solar cell image of size 550×550 pixels. The experimental results of solar module inspection show that the ICA image reconstruction method can provide up to 98.7% of correct classification. The average computation time is 1.08 seconds for a solar module image (containing 36 solar cells) of size 1250×1250 pixels.

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Chen, Si-Ming, and 陳錫明. "Computer Vision Based Defect Detection of Die Bonding in IC Package Process." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/60353342686900804699.

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碩士<br>國立中興大學<br>資訊管理學系所<br>105<br>There are some inspection stages in integrated circuit packaging process for meeting the quality standards and keeping the quality stable. We need so many persons using a microscope to check whether the quality defects exist or not after wire bonding (3RD inspection stage). The Inspection by human’s eyes not only consumes so much manpower cost and wastes more production cycle-time but also causes the under-killed abnormal product moving to the next production stage because of visual fatigue. In this thesis, we purposed an inspection method “Defect Die Bonding Detector in IC Package Process” (DDB Detector). We collected the defect pictures of abnormal products at 3RD inspection stage and then recognized the abnormal products after die bonding process stage by computer vision methods. We hope that we can achieve automated inspection so that the manpower cost, the production cycle-time and the under-killed rate of abnormal products can be decreased. We divide the DDB Detector procedure of computer vision methods into two parts. In the first part, we focus on incoming PCB defects. Segment the input image into foreground and background by Otsu’s threshold selection method, label every block in the image by connected-component labeling and calculate the area of block image. We distinguish the defective PCB by comparing the value of block image area. In second part, we focus on the defects of die bonding, for instance, missing IC die, shifted and rotated IC. We find the corner point of lead finger on PCB by global searching method, segment IC image by horizontal projection method, find IC baseline by Canny edge detection and Hough line transform, determine IC rotation by calculate the angle between IC baseline and lead finger. In this study, we input good product images and abnormal product images into the two parts of defect recognition procedure respectively for calculating the recognition accuracy. From our experimental results, the recognition accuracy of abnormal PCB can reach 100% in first part of procedure, and the recognition accuracy of abnormal die bonding can reach 92.06% in second part of procedure.

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Lin, Chia-Tsung, and 林洽琮. "Vision-based Detection of Steel Billet Surface Defects." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/70303522988153904329.

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碩士<br>國立雲林科技大學<br>資訊工程系<br>103<br>Automatic inspection techniques have been widely employed to achieve high productivity while ensuring high-quality products in steelmaking industry.In this paper, a vision-based detection framework for automatically detecting different types of steel billet surface defects is proposed. The defects considered in this study include scratches, corner cracks, sponge cracks, slivers, and roll marks and without blocking artifacts, respectively. In the proposed framework, to improve the quality of image acquisition for billet surface, two preprocessing techniques, i.e., automatic identification of ROI (region of interest) and HDR (high dynamic range)-based image enhancement techniques, are proposed. Then, DWT (discrete wavelet transform)-based image feature is extracted from the image to be detected and fused with the other two extracted local features based on variance and illumination to identify each defect on the billet surface. Experimental results have verified the feasibility of the proposed method.

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Li, Po-Chieh, and 李柏桀. "Machine vision and deep residual learning based rubber gasket defects detection." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/27ys3k.

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碩士<br>國立臺北科技大學<br>機械工程系機電整合碩士班<br>106<br>In this essay, we discussed and conducted the experiment about the rubber waterproof gasket data set acquired by different methods and inspected through the deep learning. Auto-optical detection is necessary to illuminate through various light source, and various kinds of filters to suppress interference. Moreover, the inspection procedures are complicated and more difficult. The test object of this study is a rubber waterproof gasket of a mobile phone transmission wire plug with the dimension of 10 mm × 5 mm × 5 mm. It is difficult to detect the flaw due to its small size. In addition, the first advantage of deep learning is that the accuracy is over 90%; the second advantage is high detecting speed, it only spends 2.7 seconds to detect 100 images after learning. The deep learning application is based on the fact that the computer learns different categories and a large amount of detected object images, and then separately extracts the images. Images can be overwritten and distinguish them from other categories to generate weights, after that classifies through the output of the SoftMax layer. Deep learning method requires a large amount of data to achieve accurate and efficient results. To produce the massive data, we rotated the test object to generate more image data. After that, the images are then learned through Resnt_v1_50 in the Convolutional Neural Networks (CNN) and use the deep learning visual open source software i.e., TensorFlow developed by the Google Brain team. Since the samples in this study are not enough, and result in the inspected image data are insufficient. Therefore, we generated new images by rotating the samples at different angles and then using these large amounts of new images to achieve the result of deep learning, the computer determines the correct position rate of about 90%, and the accuracy of the category judgment can reach 98%. There is no overfitting phenomenon during the proposed training steps.

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Ho, Duan-Cheng, and 何端成. "A New Detection Method Based on Discrete Cosine Transform for Pinhole Defects Applied to Computer Vision Systems." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/m9qxyy.

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碩士<br>朝陽科技大學<br>工業工程與管理系碩士班<br>92<br>Due to rising developments of high technology and precision manufacturing industries, product components are become smaller gradually and accuracy and precision of the product inspection are relatively enhanced. Even though a defect is pinhole size, it needs to be accurately detected. Tiny flaws are common defects arising in most industry parts, such as the surface pinhole defects on casting, wafer, steel, and so on. These defects not only influence appearances of parts but also have negative effects on functions and security of the products. It is difficult to precisely inspect the pinhole defects out by human eyes or machine vision equipment due to judgment errors of expansion and shrink of the defect areas resulting from uneven light of environmental illumination and complex texture background of the products. This research proposes applying cumulative sum algorithm and discrete cosine transform to flaw detection of pinhole defects in electronic components. An image with pinhole defects is transferred to frequency domain by discrete cosine transform from spatial domain. A method with two stage decomposition rules followed by cumulative sum algorithm is proposed to find the best cutting radius based on detecting turning points from gradual curves in decomposed frequency domain. After energy values within the best cutting radius are removed, the frequency domain image is transferred back to spatial domain and the pinhole defects are apparently enhanced. Then, an image segmentation method can be applied to separate the defects and defect features and locations can be obtained. Experimental results show the proposed pinhole defect detection method compared with traditional pinhole detection entropy method can reduce 70%∼80% type I error and decrease 95% deviation of defect areas.

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Book chapters on the topic "Vision-based defect detection"

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Wang, Zilong, Lixin Lu, Guiqin Li, and Peter Mitrouchev. "Lightweight Vision-Based Wafer Defect Detection." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2625-0_39.

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Sun, Jing, and Zhiyu Zhou. "Fabric Defect Detection Based on Computer Vision." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23896-3_11.

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Qiao, Xindan, Ting Chen, Wanjing Zhuang, and Jinyi Wu. "A Chip Defect Detection System Based on Machine Vision." In Proceedings of IncoME-VI and TEPEN 2021. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99075-6_45.

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Zhang, S., Z. Chen, K. Granland, Y. Tang, and C. Chen. "Machine Vision-Based Scanning Strategy for Defect Detection in Post-Additive Manufacturing." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3330-3_28.

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AbstractThe surge in 3D printer availability, and its applications over the past decade as an alternative to industry-standard subtractive manufacturing, has revealed a lack of post-manufacturing quality control. Developers have looked towards automated machine learning (ML) and machine-vision algorithms, which can be effective in developing such additive manufacturing (AM) technologies for industry-wide adoption. Currently, most research has explored in-situ monitoring methods, which aim to detect printing errors during manufacturing. A significant limitation is the single, fixed monitoring angle and low resolution, which fail to identify small or hidden defects due to part geometry. Therefore, we investigated a novel ex-situ scanning strategy that combines the advantages of robotics and machine vision to address the limitations; specifically, the viability of image-recognition algorithms in the context of post-fabrication defect detection, and how such algorithms can be integrated into current infrastructure by automatically classifying surface faults in printed parts. A state-of-the-art and widely accepted ML-based vision model, YOLO, was adapted and trained by scanning for prescribed defect categories in a sample of simple parts to identify the strengths of this method over in-situ monitoring. An automated scanning algorithm that uses a KUKA robotic arm and high-definition camera is proposed and its performance was assessed according to the percentage of accurate defect predictions, in comparison with a typical in-situ model.

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Tabernik, Domen, Samo Šela, Jure Skvarč, and Danijel Skočaj. "Deep-Learning-Based Computer Vision System for Surface-Defect Detection." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34995-0_44.

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Cao, Yanling. "Machine-Vision Based Defect Detection Technology for Industrial Fabric Alignment." In Proceedings of IncoME-V & CEPE Net-2020. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75793-9_55.

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Zhu, Min, Bingqing Shen, Yan Sun, et al. "Surface Defect Detection and Classification Based on Fusing Multiple Computer Vision Techniques." In Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08530-7_5.

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Deng, Limiao. "Carrot defect detection and grading based on computer vision and deep learning." In Cognitive and Neural Modelling for Visual Information Representation and Memorization. CRC Press, 2022. http://dx.doi.org/10.1201/9781003281641-9.

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Ren, Xinzhen, Wenju Zhou, Xiaogang Gu, and Qiang Liu. "A Defect Detection Method for Optical Fiber Preform Based on Machine Vision." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7210-1_32.

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Li, Ruizhi, Song Liu, Liming Tang, Shiqiang Chen, and Liu Qin. "Surface Defect Detection Method for the E-TPU Midsole Based on Machine Vision." In Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3867-4_17.

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Conference papers on the topic "Vision-based defect detection"

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Xi, Junjie, Zemin Fu, and Zhifan Zhao. "Machine Vision Based Paper Defect Detection." In 2024 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2024. https://doi.org/10.1109/iciibms62405.2024.10792670.

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Wang, Haoyu, Qun Peng, Yuhan Ge, Guijiang Du, Zhiwen Shi, and Kang Guo. "Vision-Based Surface Defect Detection for Precision Stamping Metal Parts." In 2024 2nd International Conference on Algorithm, Image Processing and Machine Vision (AIPMV). IEEE, 2024. http://dx.doi.org/10.1109/aipmv62663.2024.10692054.

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Lu, Meiqi, Li Geng, Rui Chen, et al. "Design of gear defect detection system based on machine vision." In 2025 IEEE 8th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2025. https://doi.org/10.1109/itoec63606.2025.10968809.

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Wang, Chao, Feiyan Li, and Zijuan Xu. "Design of Automatic Print Defect Detection System Based on Machine Vision Detection Algorithm." In 2025 IEEE 5th International Conference on Power, Electronics and Computer Applications (ICPECA). IEEE, 2025. https://doi.org/10.1109/icpeca63937.2025.10928866.

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Zheng, Bin, and Yunjin Yang. "Surface Defect Detection Technology for Solar Panels Based on Machine Vision." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733164.

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Chen, Yongbin, Long Yu, and Zhijun Zhang. "Surface defect detection of burr cylinder liner based on 3D vision." In 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), edited by Kelin Du and Azlan bin Mohd Zain. SPIE, 2024. http://dx.doi.org/10.1117/12.3038553.

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Liu, Xiaolong, Xinghua Wang, and Runxin Meng. "Design of Strip Surface Defect Detection System Based on Machine Vision." In 2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC). IEEE, 2024. https://doi.org/10.1109/eiecc64539.2024.10929540.

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Li, Dejian, Yajun Zhang, Jianing Xu, Zhibo Ding, and Shaoli Li. "Laser Weld Defect Detection Based on Laser Vision and Region Growth." In 2024 4th International Conference on Industrial Automation, Robotics and Control Engineering (IARCE). IEEE, 2024. https://doi.org/10.1109/iarce64300.2024.00024.

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Rongqiang, Yang, and Jiang Quanxin. "Research on Machine Vision Defect Detection Algorithm Based on Deep Learning." In 2024 International Conference on Electronics and Devices, Computational Science (ICEDCS). IEEE, 2024. https://doi.org/10.1109/icedcs64328.2024.00092.

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Bhanothu, Yakub, Malathy Jawahar, and J. Suriya Prakash. "Vision Based Leather Surface Defect Detection & Classification using Convolutional Neural Networks." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717129.

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Reports on the topic "Vision-based defect detection"

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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Abstract:

The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.

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