Abstract
Technology and digital imaging have a variety of uses in automated production processes and other applicable disciplines. A novel subject of inquiry in the present era is the detection of flaws in the textile industry utilizing digital image processing methods and other learning methods. The identification of flaws in the fabric must be ensured through a quality control method. The product quality is enhanced via a mechanism for detecting fabric defects. Detection of fabric flaw becoming more and more popular in the production of high-quality textile products. This system works by using image processing, video processing and classic learning methods to recognize defects in the fabric surface.
References
J. Wen and W. Wong, “Fundamentals of common computer vision techniques for fashion textile modeling, recognition, and retrieval,” in Applications of Computer Vision in Fashion and Textiles, Elsevier, Amsterdam, Netherlands, 2018.
K. Hanbay, M. F. Talu, and Ö. F. Ö F.ay/, “Fabric defect detection systems and methods-A systematic literature review,” Optik, vol. 127, no. 24, pp. 11960–11973, 2016.
Aqsa Rasheed, Bushra Zafar, Amina Rasheed, Nouman Ali, Muhammad Sajid, Saadat Hanif Dar, Usman Habib, Tehmina Shehryar, Muhammad Tariq Mahmood” Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review” Volume 2020 ,Article ID 8189403 , https://doi.org/10.1155/2020/8189403
Kazım Hanbay, Muhammed Fatih Talu, Ömer Faruk Özgüven,Fabric defect detection systems and methods—A systematic literature review,Optik,Volume 127, Issue 24,2016,Pages 11960-11973,ISSN 0030-4026,https://doi.org/10.1016/j.ijleo.2016.09.110.
Y. Li and Z. Wang, "Research on Textile Defect Detection Based on Improved Cascade R-CNN," 2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), 2021, pp. 43-46, doi: 10.1109/AIEA53260.2021.00017.
Yasser M. Fouda,Integral images-based approach for fabric defect detection, Optics & Laser Technology,Volume 147,2022,107608,ISSN 0030 3992,https://doi.org/10.1016/ j.optlastec.2021.107608.
Jiaqi Zhang, Junfeng Jing, Pengwen Lu, Shaojun Song,Improved MobileNetV2-SSDLite for automatic fabric defect detection system based on cloud-edge computing, Measurement,2022,111665,ISSN 02632241,https://doi.org/10.1016/j.measurement. 2022.111665.
Jing J, Zhuo D, Zhang H, Liang Y, Zheng M. Fabric defect detection using the improved YOLOv3 model. Journal of Engineered Fibers and Fabrics. January 2020. doi:10.1177/1558925020908268.
A. A. Hamdi, M. S. Sayed, M. M. Fouad and M. M. Hadhoud, "Unsupervised patterned fabric defect detection using texture filtering and K-means clustering," 2018 International Conference on Innovative Trends in Computer Engineering (ITCE), 2018, pp. 130-144, doi: 10.1109/ITCE.2018.8316611.
Jing, Junfeng & Fan, Xiaoting & Li, Pengfei. (2016). Automated Fabric Defect Detection Based on Multiple Gabor Filters and KPCA. International Journal of Multimedia and Ubiquitous Engineering. 11. 93-106. 10.14257/ijmue.2016.11.6.09.
Mei, S.; Wang, Y.; Wen, G. Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model. Sensors 2018, 18, 1064. https://doi.org/10.3390/s18041064
V. V. Karlekar, M. S. Biradar and K. B. Bhangale, "Fabric Defect Detection Using Wavelet Filter," 2015 International Conference on Computing Communication Control and Automation, 2015, pp. 712-715, doi: 10.1109/ICCUBEA.2015.145.
J. Liu, B. G. Zhang and L. Li, "Defect detection of fabrics With Generative Adversarial Network Based flaws modeling," 2020 Chinese Automation Congress (CAC), 2020, pp. 3334-3338, doi: 10.1109/CAC51589.2020.9327368.
Jing, J.-F., Ma, H. and Zhang, H.-H. (2019), Automatic fabric defect detection using a deep convolutional neural network. Coloration Technol, 135: 213-223. https://doi.org/10.1111/cote.12394
Jing J, Wang Z, Rätsch M, Zhang H. Mobile-Unet: An efficient convolutional neural network for fabric defect detection. Textile Research Journal. 2022;92(1-2):30-42. doi:10.1177/0040517520928604
Henry Y.T. Ngan, Grantham K.H. Pang, Nelson H.C. Yung,Automated fabric defect detection—A review,Image and Vision Computing, Volume 29, Issue 7,2011,Pages 442-458,ISSN 0262-8856,https://doi.org/10.1016/j.imavis.2011.02.002.
W. Ouyang, B. Xu, J. Hou and X. Yuan, "Fabric Defect Detection Using Activation Layer Embedded Convolutional Neural Network," in IEEE Access, vol. 7, pp. 70130-70140, 2019, doi: 10.1109/ACCESS.2019.2913620.
Yuyuan Li, Dong Zhang, Dah-Jye Lee,Automatic fabric defect detection with a wide-and-compact network, Neurocomputing,Volume 329,2019,Pages 329-338,ISSN 0925-2312,https://doi.org/10.1016/j.neucom.2018.10.070.
J. Liu, C. Wang, H. Su, B. Du and D. Tao, "Multistage GAN for Fabric Defect Detection," in IEEE Transactions on Image Processing, vol. 29, pp. 3388-3400, 2020, doi: 10.1109/TIP.2019.2959741.
Zhou, XiaokangLi, ChaoLi, JunLi, YafeiHe, LingminFu, XiaokangChen, Jingjing20212021/05/10Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art” Volume 2021 |Article ID 9948808 | https://doi.org/10.1155/2021/9948808