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SPIE. https:\/\/doi.org\/10.1117\/12.2680579.","DOI":"10.1117\/12.2680579"},{"key":"10.1016\/j.neucom.2026.134498_bib76","doi-asserted-by":"crossref","first-page":"70130","DOI":"10.1109\/ACCESS.2019.2913620","article-title":"Fabric defect detection using activation layer embedded convolutional neural network","volume":"7","author":"Ouyang","year":"2019","journal-title":"IEEE Access."},{"issue":"1-2","key":"10.1016\/j.neucom.2026.134498_bib77","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1177\/0040517520928604","article-title":"Mobile-Unet: An efficient convolutional neural network for fabric defect detection","volume":"92","author":"Jing","year":"2022","journal-title":"Text. Res. 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Cham: Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-31654-9_45.","DOI":"10.1007\/978-3-030-31654-9_45"},{"key":"10.1016\/j.neucom.2026.134498_bib91","doi-asserted-by":"crossref","unstructured":"Liu, Z., Cui, J., Li, C., Ding, S., & Xu, Q. (2019, October). Real-time fabric defect detection based on lightweight convolutional neural network. In Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition (pp. 122-127). https:\/\/doi.org\/10.1145\/3373509.3373550.","DOI":"10.1145\/3373509.3373550"},{"key":"10.1016\/j.neucom.2026.134498_bib92","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, Z., Li, C., Dong, Y., & Wei, M. (2020). SlimResNet: A Lightweight Convolutional Neural Network for Fabric Defect Detection. In Bio-inspired Computing: Theories and Applications: 14th International Conference, BIC-TA 2019, Zhengzhou, China, November 22\u201325, 2019, Revised Selected Papers, Part II 14 (pp. 597-606). 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