[1] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[2] Liu W, Anguelov D, Erhan D, Leibe B, Matas J, Sebe N et al. SSD: single shot MultiBox detector[M]. Computer vision-ECCV 2016, 9905, 21-37(2016).

[3] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

[4] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

[5] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. https://arxiv.org/abs/1804.02767

[6] Lin J P, Haberstroh F, Karsch S et al. Applications of object detection networks in high-power laser systems and experiments[J]. High Power Laser Science and Engineering, 11, e7(2023).

[7] Shen L Y, Lang B H, Song Z X. CA-YOLO: model optimization for remote sensing image object detection[J]. IEEE Access, 11, 64769-64781(2023).

[8] Khan S A, Lee H J, Lim H. Enhancing object detection in self-driving cars using a hybrid approach[J]. Electronics, 12, 2768(2023).

[9] Ou J H, Wang J G, Xue J et al. Infrared image target detection of substation electrical equipment using an improved Faster R-CNN[J]. IEEE Transactions on Power Delivery, 38, 387-396(2023).

[10] Chen M Q, Yu L J, Zhi C et al. Improved Faster R-CNN for fabric defect detection based on Gabor filter with Genetic Algorithm optimization[J]. Computers in Industry, 134, 103551(2022).

[11] Zhao C, Shu X, Yan X et al. RDD-YOLO: a modified YOLO for detection of steel surface defects[J]. Measurement, 214, 112776(2023).

[12] Tian R S, Jia M P. DCC-CenterNet: a rapid detection method for steel surface defects[J]. Measurement, 187, 110211(2022).

[13] Sun L S, Wei J X, Zhu D M et al. Surface defect detection algorithm of aluminum profile based on AM-YOLOv3 model[J]. Laser & Optoelectronics Progress, 58, 2415007(2021).

[14] Sun Y Z, Gao J W. Wheel tread defect detection based on improved YOLOv5[J]. Laser & Optoelectronics Progress, 59, 2215003(2022).

[15] Wang Y X, Chen B. Printing defect detection method based on depth contrast network[J]. Journal of Computer Applications, 43, 250-258(2023).

[16] Wang H T, Gao Y D, Hou J X et al. A method of printing defect detection based on DCGAN[J]. Journal of Harbin University of Science and Technology, 26, 24-32(2021).

[17] Lou H J, Zheng Y L, Liao K Y et al. Defect target detection for printed matter based on Siamese-YOLOv4[J]. Journal of Computer Applications, 41, 3206-3212(2021).

[18] Lou H J. Research on detection method of small defects in printed matter based on small sample learning[D](2022).

[19] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C], 936-944(2017).

[20] Liu S, Qi L, Qin H F et al. Path aggregation network for instance segmentation[C], 8759-8768(2018).

[21] Li C, Zhou A J, Yao A B. Omni-dimensional dynamic convolution[EB/OL]. https://arxiv.org/abs/2209.07947

[22] Han K, Wang Y H, Tian Q et al. GhostNet: more features from cheap operations[C], 1577-1586(2020).

[23] Wang B W, Huang F. A lightweight deep network for defect detection of insert molding based on X-ray imaging[J]. Sensors, 21, 5612(2021).