SESC-YOLO: Enhanced YOLOV5 for Detecting Defects on Steel Surface
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-4577-1_17
Reference21 articles.
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3. He Y, Song K, Meng Q, Yan Y (2019) An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans Instrum Measur 69(4):1493–1504
4. Qing YAO, Jin F, Jian T, Xu W, Zhu X, Yang B, Jun LU et al (2020) Development of an automatic monitoring system for rice light-trap pests based on machine vision. J Integr Agricult 19(10):2500–2513
5. Gyimah NK, Girma A, Mahmoud MN, Nateghi S, Homaifar A, Opoku D (2021) A robust completed local binary pattern (RCLBP) for surface defect detection. In: Proceedings of the 2021 IEEE international conference on systems, man, and cybernetics (SMC), pp 1927–1934
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