[8] [8] Feng Chao, Jiang Peng, Cao Xianhui, et al. Defects recognition of strain clamps based on attentional enhancement mechanism[J]. Journal of Physics: Conference Series, 2022, 2417(1): 98-102.

[9] [9] Qiliang D, Huangze Z, Fengrui Q, et al. A method based on rotated object detection to detect defects regarding distance in strain clamps[J]. Journal of Physics: Conference Series, 2023, 2479(1): 108-114.

[10] [10] Haoliang, Z., Zhiwei, J., Yuting, L., Rongjie, W., Wenguang, Z. Live line strain clamp's DR image anomaly detection based on unsupervised learning[J]. IET Gener. Transm, 2023, 17: 4717-4734.

[11] [11] Ultralytics: Ultralytics YOLOv8[EB/OL]. [2023-3.26].

[12] [12] Wang O, Wu B, Zhu P, et al. ECA-Nel: Efficient channel attention for deep convolutional neural networks[C]//2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2020.

[13] [13] Tong Z J, Chen Y H, Xu Z W, et al. Wise-IoU: Bounding box regression loss with dynamic focusing mechanism[J]. arxiv preprint arXiv: 2301.10051, 2023.

[14] [14] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 779-788.

[16] [16] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7132-7141.

[17] [17] Krizhevsky A, Sutskever I, Hinton G. Image Net classification with deep convolutional neural networks[C]//NIPS.2012.

[18] [18] Zhang H, Cisse M, Dauphin Y N, et al. Mixup: Beyond empirical risk minimization[J]. arXiv preprint arXiv: 1710.09412, 2017.

[19] [19] Bochkovskiy A, Wang C Y, Liao M H Y. YOLOV4: Optimal speed and accuracy of object detection[EB/OL]. [2022-11-16].

[20] [20] Brian D. Ripley. Pattern Recognition and Neural Networks[M]. Cambridge, England: Cambridge University Press, 1996.

[22] [22] Zaidi S, Ansari M S, Aslam A, et al. A survey of modern deep learning based object detection models[J]. Digital Signal Processing, 2022, 126: 103514-103530.

[23] [23] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). Ssd: Single shot multibox detector[C]//In Computer Vision-ECCV 2016 Springer International Publishing, 2016, 11: 21-37.

[24] [24] Ren S Q, He K M, Grishick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-114911.

[25] [25] He K M, Gkioxari G, DollR P, et al. Mask R-CNN[C]//2017 IEEE International Conference on Computer Vision, October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 2980-2988.

[26] [26] Redmon J, Farhadi A. YOLOv3: An incremental improvement[J]. arXiv: 1804.02767, 2018.

[27] [27] Dadboud F, Patel V Mehta V et al. Single-stage UAV Detection and Classification with YOLOV5: Mosaic Data and Augmentation PANet[C]//2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). 2021: 1-811

[28] [28] Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 7464-7475.

[29] [29] Woo S, Park J, Lee J Y, et al. CBAM: Convolutional Block Attention Module[C]//Computer vision-ECCV2018: 15th European Conference, Munich, Germany, Proceedings, VII.: Springer, 2018: 3-19.