[1] Su S, Nie S J, Yan B S et al. Automatic detection of pin-connected tin defects in SMT components based on machine vision[J]. Modular Machine Tool & Automatic Manufacturing Technique, 63-65, 69(2018).

[2] Hua G L, Huang W P, Liu H. Accurate image registration method for PCB defects detection[J]. The Journal of Engineering, 1662-1667(2018).

[3] Liu B, Wang T T, Liu J. Surface defect detection of mobile phone covers based on improved BiSeNet V2[J]. Acta Optica Sinica, 44, 1615001(2024).

[4] Jeon M, Choi H S, Lee J et al. Multi-scale prediction for fire detection using convolutional neural network[J]. Fire Technology, 57, 2533-2551(2021).

[5] Bai X J, Wang Z X. Research on forest fire detection technology based on deep learning[C], 85-90(2021).

[6] Feng S, Wen J W, Zhang Y et al. Design of PCB defect detection system based on YOLOv5-Ghost[J]. Machinery Design & Manufacture, 354-357, 361(2025).

[7] Zhang J T, Huang Y, Wang P Y et al. Research on PCB defect detection based on improved YOLOv3[J/OL]. Machinery Design & Manufacture, 1-8. https://doi.org/10.19356/j.cnki.1001-3997.20241230.001

[8] Ma G, Deng K H, Li G Z et al. Metal surface defect dectection method based on an improved YOLOv5s model[J]. Journal of Guangzhou University(Natural Science Edition), 23, 9-19(2024).

[9] Hu J, He J J, Gong Y et al. Bill detection based on improved YOLOv5 algorithm[J]. Journal of Hefei University of Technology(Natural Science), 47, 1459-1464(2024).

[10] Xu P H, Ren J, Wang R Y. PCB surface defect detection system based on improved YOLOv5[J]. Information Technology and Informatization, 114-117(2024).

[11] Li J, Xu X H, Wang Y H. Rail surface defect detection algorithm based on improved YOLOv5s[J]. Journal of Chongqing University of Technology(Natural Science), 38, 130-137(2024).

[12] Qu Y, Wang C, Yu J B. Optimized algorithm for surface defect detection based on YOLOv5[J]. Journal of Air Force Engineering University, 24, 80-87(2023).

[13] Xu H J, Tang Z Q, Zhang J D et al. Research on optimization of YOLOv5s detection algorithm for steel surface defect[J]. Computer Engineering and Applications, 60, 306-314(2024).

[14] Wang S Q, Zhang Z Y, Zhu W X. Surface defect detection of PCB based on improved YOLOv5[J]. Instrument Technique and Sensor, 106-111(2023).

[15] Zeng Y, Gao F Q. Surface defect detection algorithm of electronic components based on improved YOLOv5[J]. Journal of Zhejiang University(Engineering Science), 57, 455-465(2023).

[16] Xue Y, Qu Y, Yan H L et al. Attention mechanism-based improved YOLOv5 algorithm for insulator defect detection[J]. Electric Engineering, 185-189(2024).

[17] Lei Z T, Zhu X L, Sun J et al. YOLOv5 ceramic film defect detection method incorporating coordinate attention and adaptive features[J]. Electronic Measurement Technology, 46, 133-137(2023).

[18] Li X X, Li B N, Zhang X et al. Image fire detection algorithm based on YOLOv5 with CA-Res attention mechanism[J]. Fire Science and Technology, 1113-1116(2023).

[19] Yang S Q, Ding F, Wen H X et al. Hot-rolled steel strip surface defects detection based on CA-YOLOv5[J]. Journal of Optoelectronics·Laser, 35, 21-28(2024).

[20] Liu Y, Gong Z H, Li Z F et al. Tomato ripeness detection method based on improved YOLOv5[J]. Chinese Journal of Agrometeorology, 45, 1521-1532(2024).

[21] Wu J G, Liang M, Cao H et al. Research on PCB small target defect detection based on improved YOLOv5[J]. Journal of Optoelectronics·Laser, 35, 155-163(2024).