Computer Vision-Based Detection of Patterns in Textile Microscopy Images
Keywords:
computer vision, textile microscopy images, small batch training
Abstract
The analysis and detection of patterns in textile materials are a critical challenge in modern manufacturing, quality control, and automation processes. Traditional inspection methods, often reliant on manual observation, are not only time-consuming but also prone to inconsistencies. The need for a more precise, efficient, and scalable approach has driven interest in leveraging computer vision for this task. Computer vision systems, powered by advanced algorithms and machine learning models, offer the ability to process and interpret visual data from textile images at high speeds and with greater accuracy.
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References
[1]. Hanbay K., Talu M. F., Özgüven Ö. F., Fabric defect detection systems and methods - A systematic literature review, Optik, vol. 127, p. 11960-11973, 2016.
[2]. Kumar A., Computer-Vision-Based Fabric Defect Detection: A Survey, IEEE Transactions on Industrial Electronics, vol. 55, no. 1, p. 348-363, Jan. 2008.
[3]. Abdel-Azim G., Nasri S., Textile defects identification based on neural networks and mutual information, International Conference on Computer Applications Technology (ICCAT), 2013.
[4]. Sujee R., Padmavathi S., Image enhancement through pyramid histogram matching, International Conference on Computer Communication and Informatics (ICCCI), 2017.
[5]. Rebhi A., Abid S., Fnaiech F., Fabric defect detection using local homogeneity and morphological image processing, International Image Processing, Applications and Systems (IPAS), Hammamet, 2016.
[6]. Jmali M., Zitouni B., Sakli F., Fabrics defects detecting using image processing and neural networks, Information and Communication Technologies Innovation and Application, 2014.
[7]. Jeong H., Park K., Ha Y., Image Preprocessing for Efficient Training of YOLO Deep Learning Networks, IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, 2018.
[8]. Latif A. Rasheed, Sajid U., et al., Content-based image retrieval and feature extraction: a comprehensive review, Mathematical Problems in Engineering, vol. 2019, Article ID 9658350, 2019.
[8]. Kumar A., Computer-vision-based fabric defect detection: a survey, IEEE Transactions on Industrial Electronics, vol. 55, no. 1, p. 348-363, 2008.
[9]. Boshan Shi, et al., Fabric defect detection via lowrank decomposition with gradient information and structured graph algorithm, Information Sciences, vol. 546, 2021.
[10]. Junli Luo, et al., Cashmere and wool identification based on convolutional neural network, Sage, 2023.
[11] Ngai W. T., et al., Decision support and intelligent systems in the textile and apparel supply chain: an academic review of research articles, Expert Systems with Applications, vol. 41, no. 1, p. 81-91, 2014.
[12]. Guohua Liu, Xiangtong Zheng, Fabric defect detection based on information entropy and frequency domain saliency, The Visual Computer, vol. 37, 2020.
[13]. Hong-wei Zhang, et al., Yarn-dyed Fabric Defect Detection using U- shaped De-noising Convolutional Auto-Encoder, IEEE 9th Data Driven Control and Learning S Reystems Conference (DDCLS), Liuzhou, China, 2020.
[2]. Kumar A., Computer-Vision-Based Fabric Defect Detection: A Survey, IEEE Transactions on Industrial Electronics, vol. 55, no. 1, p. 348-363, Jan. 2008.
[3]. Abdel-Azim G., Nasri S., Textile defects identification based on neural networks and mutual information, International Conference on Computer Applications Technology (ICCAT), 2013.
[4]. Sujee R., Padmavathi S., Image enhancement through pyramid histogram matching, International Conference on Computer Communication and Informatics (ICCCI), 2017.
[5]. Rebhi A., Abid S., Fnaiech F., Fabric defect detection using local homogeneity and morphological image processing, International Image Processing, Applications and Systems (IPAS), Hammamet, 2016.
[6]. Jmali M., Zitouni B., Sakli F., Fabrics defects detecting using image processing and neural networks, Information and Communication Technologies Innovation and Application, 2014.
[7]. Jeong H., Park K., Ha Y., Image Preprocessing for Efficient Training of YOLO Deep Learning Networks, IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, 2018.
[8]. Latif A. Rasheed, Sajid U., et al., Content-based image retrieval and feature extraction: a comprehensive review, Mathematical Problems in Engineering, vol. 2019, Article ID 9658350, 2019.
[8]. Kumar A., Computer-vision-based fabric defect detection: a survey, IEEE Transactions on Industrial Electronics, vol. 55, no. 1, p. 348-363, 2008.
[9]. Boshan Shi, et al., Fabric defect detection via lowrank decomposition with gradient information and structured graph algorithm, Information Sciences, vol. 546, 2021.
[10]. Junli Luo, et al., Cashmere and wool identification based on convolutional neural network, Sage, 2023.
[11] Ngai W. T., et al., Decision support and intelligent systems in the textile and apparel supply chain: an academic review of research articles, Expert Systems with Applications, vol. 41, no. 1, p. 81-91, 2014.
[12]. Guohua Liu, Xiangtong Zheng, Fabric defect detection based on information entropy and frequency domain saliency, The Visual Computer, vol. 37, 2020.
[13]. Hong-wei Zhang, et al., Yarn-dyed Fabric Defect Detection using U- shaped De-noising Convolutional Auto-Encoder, IEEE 9th Data Driven Control and Learning S Reystems Conference (DDCLS), Liuzhou, China, 2020.
Published
2025-06-15
How to Cite
1.
MARIN M, MARIN F-B. Computer Vision-Based Detection of Patterns in Textile Microscopy Images. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15Jun.2025 [cited 23Oct.2025];48(2):5-. Available from: https://gup.ugal.ro/ugaljournals/index.php/mms/article/view/9233
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