Laser Journal, Volume. 45, Issue 10, 47(2024)
Real-time fabric defect detection algorithm based on YOLOv5s
[1] [1] Tiwari V, Sharma G. Automatic fabric fault detection using morphological operations on bit plane[J]. International Journal of Computer Science and Network Security (IJCSNS), 2015, 15(10): 30.
[2] [2] Cheng B, Wei Y, Shi H, et al. Revisiting rcnn: On awakening the classification power of faster rcnn[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 453-468.
[3] [3] Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28: 45-50.
[5] [5] Jiang P, Ergu D, Liu F, et al. A Review of Yolo algorithm developments[J]. Procedia Computer Science, 2022, 199: 1066-1073.
[6] [6] Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector[C]//Computer Vision - ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part I 14. Springer International Publishing, 2016: 21-37.
[7] [7] Jing J, Zhuo D, Zhang H, et al. Fabric defect detection using the improved YOLOv3 model[J]. Journal of engineered fibers and fabrics, 2020, 15: 1558925020908268.
[9] [9] Li Y, Huang H, Chen Q, et al. Research on a product quality monitoring method based on multi scale PP-YOLO[J]. IEEE Access, 2021, 9: 80373-80387.
[11] [11] Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]//International conference on machine learning. pmlr, 2015: 448-456.
[12] [12] Chen X, Yang C, Mo J, et al. CSPNeXt: A new efficient token hybrid backbone[J]. Engineering Applications of Artificial Intelligence, 2024, 132: 107886.
[13] [13] He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9): 1904-1916.
[15] [15] Yu H, Li X, Feng Y, et al. Multiple attentional path aggregation network for marine object detection[J]. Applied Intelligence, 2023, 53(2): 2434-2451.
[16] [16] Yue X, Wang Q, He L, et al. Research on tiny target detection technology of fabric defects based on improved Yolo[J]. Applied Sciences, 2022, 12(13): 6823.
[17] [17] Gao S H, Han Q, Li D, et al. Representative batch normalization with feature calibration[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 8669-8679.
[18] [18] Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 13713-13722.
[19] [19] Yanyu L, Jinbao L I. Small objects detection method based on multi-scale non-local attention network[J]. Journal of Frontiers of Computer Science & Technology, 2020, 14(10): 1744.
[20] [20] Yanyu L, Jinbao L I. Small objects detection method based on multi-scale non-local attention network[J]. Journal of Frontiers of Computer Science & Technology, 2020, 14(10): 1744.
[21] [21] Miao L, Li N, Zhou M, et al. CBAM-Yolov5: improved Yolov5 based on attention model for infrared ship detection[C]//International conference on computer graphics, artificial intelligence, and data processing (ICCAID 2021). SPIE, 2022, 12168: 564-571.
Get Citation
Copy Citation Text
JI Xunsheng, QIAN Fu, DONG Yue. Real-time fabric defect detection algorithm based on YOLOv5s[J]. Laser Journal, 2024, 45(10): 47
Category:
Received: Mar. 12, 2023
Accepted: Jan. 2, 2025
Published Online: Jan. 2, 2025
The Author Email: Fu QIAN (643696927@qq.com)