[1] MING W Y, SHEN F, ZHANG H M, et al. Defect detection of LGP based on combined classifier with dynamic weights[J]. Measurement, 2019, 143: 211-225.
[2] LIU F, LI J F, DAI W Z. A defect detection method for light guide plates based on deep learning semantic segmentation[J]. Computer system applications, 2020, 29(6): 29-38. (in Chinese)
[3] HU J, LI J F. Research on the scratch detection method of light guide plate line based on residual convolutional automatic encoder[J]. Journal of optoelectronics·laser, 2020, 31(8): 825-833. (in Chinese)
[4] HU J L, LI J F. Defect detection of large-sized light guide plates based on improved YOLOv3[J]. Computer system application, 2022, 31(06): 279-286. (in Chinese)
[5] LIU X, ZHANG H Y. Defect detection of large-sized light guide plates based on improved YOLOv5s[J]. Computer system application, 2023, 32(02): 339-346. (in Chinese)
[6] GLENN J. Ultralytics/YOLOv5 v6.0[EB/OL]. (2021-10-21) [2023-5-20]. https://github.com/ultralytics/ yolov5/releases/tag/v6.0.
[7] XIAO J S, ZHAO T, YAO Y T, et al. Context augmentation and feature refinement network for tiny object detection[EB/OL].(2022-04-14) [2023-5-30]. https://openreview. net/forum?id=q2ZaVU6bEsT.
[8] CSDN. New XIoU loss function improved in YOLOv7 and YOLOv5[EB/OL]. (2022-04-18) [2023-5-30]. https://yoloair.blog.csdn.net/article/details/130215696.
[9] LI J F, LI M R. Research on defect detection methods for light guide plates based on machine vision[J]. Journal of optoelectronics·laser, 2019, 30(3): 256-265. (in Chinese)
[10] HE Y S. Research on quality visual inspection system of vehicle navigation light guide plate based on machine learning[D]. Hangzhou: Zhejiang Sci-Tech University,2021. (in Chinese)
[11] ZHU X K, LU S C, WANG X, et al. TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C]//Proceedings of the 2021 IEEE/CVF InternationalConference on Computer Vision, October 10-17, 2021, Montreal, QC, Canada. New York: IEEE, 2021: 2778-2788.
[12] TAO X, HOU W, XU D. Review of surface defect detection methods based on deep learning[J]. Journal of automation, 2021, 47(5): 1017-1034. (in Chinese)
[13] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, December 7-12, 2015, Montreal, Canada. Cambridge: MIT Press, 2015: 91-99.
[14] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//2017 IEEE International Conference on Computer Vision, October 24-27, 2017, Venice, Italy. New York: IEEE, 2017: 2999-3007.
[15] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision, October 11-14, 2016, Amsterdam, The Netherlands. Cham: Springer, 2016: 21-37.
