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Relevant bibliographies by topics / Fabric defect detection

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Academic literature on the topic 'Fabric defect detection'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Fabric defect detection"

1

Abou-Taleb, Hemdan A., and Aya Tallah M. Sallam. "ON-LINE FABRIC DEFECT DETECTION AND FULL CONTROL IN A CIRCULAR KNITTING MACHINE." AUTEX Research Journal 8, no. 1 (2008): 21–29. http://dx.doi.org/10.1515/aut-2008-080105.

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Abstract This study has shown that image analysis has great potential to provide reliable measurements for detecting defects in knitted fabrics. Using the principles of image analysis, an automatic fabric evaluation system, which enables automatic computerised defect detection -(analysis of knitted fabrics) was developed. On-line fabric defect detection was tested automatically by analysing fabric images captured by a digital camera. The results of the automatic fabric defect detection correspond well with the experimental values. Therefore, it is shown that the developed image capturing and a

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Xia, Rongfei, Yifei Chen, and Yangfeng Ji. "Detection of Microdefects in Fabric with Multifarious Patterns and Colors Using Deep Convolutional Neural Network." Advances in Polymer Technology 2024 (February 12, 2024): 1–10. http://dx.doi.org/10.1155/2024/5926658.

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Automatic detection of fabric defects is important in textile quality control, particularly in detecting fabrics with multifarious patterns and colors. This study proposes a fabric defect detection system for fabrics with complex patterns and colors. The proposed system comprises five convolutional layers designed to extract features from the original images effectively. In addition, three fully connected layers are designed to classify the fabric defects into four categories. Using this system, the detection accuracy is improved, and the depth of the model is shortened simultaneously. Optimal

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HAO, Bao Ming, Hai Feng Xu, and Huan Yin Guo. "Fabric Defect Detection Based on Cross-Entropy." Advanced Materials Research 760-762 (September 2013): 1233–36. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1233.

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The core of fabric defects detection is the collection and processing of fabrics image. A scheme for fabric defect detection based on cross-entropy is proposed in this paper.The crossentropy value illuminates the information difference between the template image and the realtime image on the average.So can take advantage of cross-entropy criteria to use for defect detection and identification. Results have confirmed the usefulness of this scheme for fabric defect detection.

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Peng, Peiran, Ying Wang, Can Hao, Zhizhong Zhu, Tong Liu, and Weihu Zhou. "Automatic Fabric Defect Detection Method Using PRAN-Net." Applied Sciences 10, no. 23 (2020): 8434. http://dx.doi.org/10.3390/app10238434.

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Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sp

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Sun, Qin, Bernd Noche, Zongyi Xie, and Bingqiang Huang. "Research on Seamless Fabric Defect Detection Based on Improved YOLOv8n." Applied Sciences 15, no. 5 (2025): 2728. https://doi.org/10.3390/app15052728.

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An improved YOLOv8n seamless fabric defect detection model is proposed to solve the current issues in seamless fabric defects in factories in this paper. The improvement in this paper first introduces the SPPF_LSKA module, which not only optimizes the extraction of multi-scale features but also enhances the adaptability of the model in detecting defects of different sizes by improving the feature fusion mechanism, enabling efficient recognition of both large-sized and small-sized defects. Secondly, the CARAFE upsampling method is used to adaptively learn the relationship between pixels, which

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Li, Long, Qi Li, Zhiyuan Liu, and Lin Xue. "Effective Fabric Defect Detection Model for High-Resolution Images." Applied Sciences 13, no. 18 (2023): 10500. http://dx.doi.org/10.3390/app131810500.

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The generation of defects during fabric production impacts fabric quality, and fabric production factories can greatly benefit from the automatic detection of fabric defects. Although object detection algorithms based on convolutional neural networks can be quickly developed, fabric defect detection encounters several problems. Accordingly, a fabric defect detection model based on Cascade R-CNN was proposed in this study. We also proposed block recognition and detection box merging algorithms to achieve complete defect detection in high-resolution images. We implemented a Switchable Atrous Con

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Harshil, Sharma, and al et. "Enhanced YOLOv3 Model for Automated Fabric Defect Detection." International Journal of Human Computations and Intelligence 4, no. 3 (2025): 487–99. https://doi.org/10.5281/zenodo.15280482.

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The manufacture of high-quality fabrics depends on an accurate and efficient fault detection system that can analyze data in real-time. A sophisticated defect detection system based on an enhanced YOLOv3 architecture is presented in this work to improve detection accuracy and reduce false identifications. Using a hybrid technique that combines defect size analysis and k-means clustering, the proposed approach optimizes the number and size of anchor boxes for fabric imperfections, introducing two crucial breakthroughs. Second, a multi-scale feature improvement strategy is used, combining high-l

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Jing, Junfeng, Panpan Yang, and Pengfei Li. "Defect Detection on Patterned Fabrics Using Distance Matching Function and Regular Band." Journal of Engineered Fibers and Fabrics 10, no. 2 (2015): 155892501501000. http://dx.doi.org/10.1177/155892501501000210.

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In this paper, regular band is presented to detect defects on patterned fabrics. Patterned fabrics are firstly disposed by fabric average to form object images mixed with positive and negative pixels in this proposed method. Distance matching function is computed to determine the periodic distance of patterned fabrics. The obtained periodic distance would be the length and width of regular band on row and column. Two features are calculated with regular band. The threshold of defect segmentation is extracted from the training step. Two features of regular band negotiating the threshold are con

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Nasim, Mariam, Rafia Mumtaz, Muneer Ahmad, and Arshad Ali. "Fabric Defect Detection in Real World Manufacturing Using Deep Learning." Information 15, no. 8 (2024): 476. http://dx.doi.org/10.3390/info15080476.

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Defect detection is very important for guaranteeing the quality and pricing of fabric. A considerable amount of fabric is discarded as waste because of defects, leading to substantial annual losses. While manual inspection has traditionally been the norm for detection, adopting an automatic defect detection scheme based on a deep learning model offers a timely and efficient solution for assessing fabric quality. In real-time manufacturing scenarios, datasets lack high-quality, precisely positioned images. Moreover, both plain and printed fabrics are being manufactured in industries simultaneou

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Carrilho, Rui, Kailash A. Hambarde, and Hugo Proença. "A Novel Dataset for Fabric Defect Detection: Bridging Gaps in Anomaly Detection." Applied Sciences 14, no. 12 (2024): 5298. http://dx.doi.org/10.3390/app14125298.

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Detecting anomalies in texture has become a significant concern across various industrial processes. One prevalent application of this is in inspecting patterned textures, especially in the domain of fabric defect detection, which is a commonly encountered scenario. This task entails dealing with a wide array of colours and textile varieties, spanning a broad spectrum of fabrics. Due to the extensive diversity in colours, textures, and defect characteristics, fabric defect detection presents a complex and formidable challenge within the realm of patterned texture inspection. While recent trend

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Dissertations / Theses on the topic "Fabric defect detection"

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Ngan, Yuk-tung Henry, and 顏旭東. "Patterned Jacquard fabric defect detection." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30070880.

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Lee, Tin-chi, and 李天賜. "Fabric defect detection by wavelet transform and neural network." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29287285.

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Yang, Xuezhi, and 楊學志. "Discriminative fabric defect detection and classification using adaptive wavelet." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29913408.

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Schneider, Dorian Verfasser], Dorit [Akademischer Betreuer] [Merhof, and Peter [Akademischer Betreuer] Vary. "On-loom fabric defect detection : state-of-the-art and beyond / Dorian Schneider ; Dorit Merhof, Peter Vary." Aachen : Universitätsbibliothek der RWTH Aachen, 2015. http://d-nb.info/1129875636/34.

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Peng, Pai. "Automated defect detection for textile fabrics using Gabor wavelet networks." View the Table of Contents & Abstract, 2006. http://sunzi.lib.hku.hk/hkuto/record/B38025966.

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Peng, Pai, and 彭湃. "Automated defect detection for textile fabrics using Gabor wavelet networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B38766103.

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Malek, Abdel Salam. "Online fabric inspection by image processing technology." Phd thesis, Université de Haute Alsace - Mulhouse, 2012. http://tel.archives-ouvertes.fr/tel-00720041.

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The purpose of this thesis is to automate the online detection of weaving defects by a computerized system based on image processing software. Obviously, fabric inspection has an importance to prevent risk of delivering inferior quality product. Until recently, the visual defect detection is still under taken offline and manually by humans with many drawbacks such as tiredness, boredom, and, inattentiveness. Fortunately, the continuous development in computer technology introduces the online automated fabric inspection as an effective alternative. Because the defect-free fabric has a periodic

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Brenzovich, Joseph A. "Fabric defect detection using a GA tuned wavelet filter." 2003. http://www.lib.ncsu.edu/theses/available/etd-10302003-164651/unrestricted/etd.pdf.

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Wang, Wei-Ren, and 王韋仁. "Application of Automatic Optical Inspection to Recognition and Classification of Woven Fabric Defect Detection." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9e6aft.

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碩士<br>國立臺灣科技大學<br>材料科學與工程系<br>106<br>The present fabric defect detection depends on manual examination, implemented by professional inspectors with naked eye. However, the visual detection method results in time consumption, fatigue and sub-jective defect judgment. The inspection standard is not objective enough, and tiny defects are often missed. Only about 70% of defects can be detected by inspectors. The defect detection and classification cannot be perfect. Therefore, this study designs the software and hardware equipment, combined with the developed image processing procedure to build a d

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Chen, Wen-hua, and 陳文化. "Defect Detection System for Rrinted Fabrics." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/45mubn.

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碩士<br>國立臺灣科技大學<br>自動化及控制研究所<br>99<br>Printed fabric is a high value-added textile that has rich colors and variable patterns. Although colored fabrics are commonly used in daily life, image analysis technology for fabrics is limited to gray-scale fabrics. This study aimed to develop an automatic defect detection system for printed fabrics that detects the digital images of printed fabrics and identifies the defected images, in order to enhance the quality of printed fabric products. The images are scanned from printed fabrics and converted to digital data. After obtaining the RGB values of rep

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Book chapters on the topic "Fabric defect detection"

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Mahmud, Tanjim, Juel Sikder, Rana Jyoti Chakma, and Jannat Fardoush. "Fabric Defect Detection System." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68154-8_68.

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Oliveira, Filipe, Davide Carneiro, Hugo Ferreira, and Miguel Guimarães. "Fabric Defect Detection and Localization." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57496-2_18.

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Jiang, Jielin, Yan Cui, Zilong Jin, and Chunnian Fan. "Fast Three-Phase Fabric Defect Detection." In Cloud Computing and Security. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00015-8_26.

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Natesan, P., R. Thamilselvan, E. Gothai, M. Harini, Chitrasena Nehru Kasthuri, and G. Deepankumar. "Fabric Defect Detection Using Deep Learning." In Intelligent Systems Design and Applications. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64836-6_24.

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Dong, Xiaoliang, Hao Liu, Yuexin Luo, Yubao Yan, and Jiuzhen Liang. "Semi-supervised Lightweight Fabric Defect Detection." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8505-6_8.

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Likith Kumar, V., A. Hari Priya, N. Jahnavi Chakravarthy, and Padarti Vijaya Kumar. "Fabric Defect Detection Using Computer Vision." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2109-3_4.

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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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Lv, Ying, Xiaodong Yue, Qiang Chen, and Meiqian Wang. "Fabric Defect Detection with Cartoon–Texture Decomposition." In Artificial Intelligence on Fashion and Textiles. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99695-0_33.

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Wei, Bing, Kuangrong Hao, Xue-song Tang, and Lihong Ren. "Fabric Defect Detection Based on Faster RCNN." In Artificial Intelligence on Fashion and Textiles. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99695-0_6.

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Mandawkar, Umakant, Makarand Shahade, Samruddhi Wadekar, Chetan Kachhava, Yash Patil, and Sakshi Mandwekar. "Real-time automated fabric defect detection system." In Advances in AI for Biomedical Instrumentation, Electronics and Computing. CRC Press, 2024. http://dx.doi.org/10.1201/9781032644752-61.

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Conference papers on the topic "Fabric defect detection"

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Sahani, Sushilkumar, Parth Sumbre, and Maheshwari Biradar. "Fabric Defect Detection Using YOLO V8." In 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 2024. https://doi.org/10.1109/iccubea61740.2024.10775212.

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Shehzad, Salman, Chunsheng Yang, Yuan-Gen Wang, and Bitang Zhu. "Fabric Defect Detection with Fine-tuned YOLOv7." In 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025. https://doi.org/10.1109/cscwd64889.2025.11033374.

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P, Yashini, Karthika G, Sunitha T, R. Renugadevi, and Berlin Magthalin R. "Machine Learning-Based Textile Fabric Defect Detection Network." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763088.

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Zhang, Siyuan, Jingwen Su, Zijun Gao, and Chuanchao Sun. "Fabric Defect Detection Algorithm Based on Improved YOLOv8." In 2024 4th International Conference on Electronic Information Engineering and Computer (EIECT). IEEE, 2024. https://doi.org/10.1109/eiect64462.2024.10866069.

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Liu, Shanshan. "Fabric defect detection algorithm based on improved YOLOv8s." In 2024 4th International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC). IEEE, 2024. https://doi.org/10.1109/icairc64177.2024.10900055.

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Powiwi, Reynhard, Tjokorda Agung Budi Wirayuda, and Febryanti Sthevanie. "Patterned Fabric Defect Detection Based on Separate Convolutional Unet." In 2024 International Conference on Data Science and Its Applications (ICoDSA). IEEE, 2024. http://dx.doi.org/10.1109/icodsa62899.2024.10651778.

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Sony, J., and Priya Badrinath. "Innovative Fabric Defect Detection Strategies for Enhanced Textile Quality." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923288.

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Mohammed, Swash Sami, and Hülya Gökalp Clarke. "Advanced Convolutional Neural Network Approach for Fabric Defect Detection." In 2024 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2024. https://doi.org/10.1109/asyu62119.2024.10757038.

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Prathima, Ch, Mahanandi Y, K.Madhumitha, K. Hari Haran, K.Manisha, and J. Hanshith. "Smart Fabric Inspection: Leveraging Deep Learning for Defect Detection." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011778.

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Zhou, Hao, Yixin Chen, David Troendle, and Byunghyun Jang. "One-Class Model for Fabric Defect Detection." In 10th International Conference on Natural Language Processing (NLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112314.

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An automated and accurate fabric defect inspection system is in high demand as a replacement for slow, inconsistent, error-prone, and expensive human operators in the textile industry. Previous efforts focused on certain types of fabrics or defects, which is not an ideal solution. In this paper, we propose a novel one-class model that is capable of detecting various defects on different fabric types. Our model takes advantage of a well designed Gabor filter bank to analyze fabric texture. We then leverage an advanced deep learning algorithm, autoencoder, to learn general feature representation

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