[3] [3] Souza B J, Stefenon S F, Singh G, et al. Hybrid-YOLO for classification of insulators defects in transmission lines based on UAV[J]. International Journal of Electrical Power & Energy Systems, 2023, 148: 108982.

[4] [4] Pu H, Chen X, Yang Y, et al. Tassel-YOLO: a new high-precision and real-time method for maize tassel detection and counting based on UAV aerial images[J]. Drones, 2023, 7(8): 492.

[5] [5] Shao Y, Yang Z, Li Z, et al. Aero-YOLO: an efficient vehicle and pedestrian detection algorithm based on unmanned aerial imagery[J]. Electronics, 2024, 13(7): 1190.

[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] Redmon J, Farhadi A. Yolov3: an incremental improvement[J/OL]. arXiv preprint 2018. arXiv: 1804.02767.

[8] [8] Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: optimal speed and accuracy of object detection[J/OL]. arXiv preprint 2020. arXiv: 2004.10934.

[9] [9] Ge Z, Liu S, Wang F, et al. YOLOX: exceeding YOLO series in 2021[J/OL].2021. arXiv preprint arXiv: 2107.08430.

[10] [10] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for re-al-time object detectors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 7464-7475.

[11] [11] Ren S, He K, 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, 2016, 39(6): 1137-1149.

[12] [12] Cai Z, Vasconcelos N. Cascade R-CNN: delving into high quality object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 6154-6162.

[13] [13] Liu M, Wang X, Zhou A, et al. Uav-yolo: small object detection on unmanned aerial vehicle perspective[J]. Sensors, 2020, 20(8): 2238.

[14] [14] Zhang T, Zhang Y, Xin M, et al. A light-weight network for small insulator and defect detection using UAV imaging based on improved YOLOv5[J]. Sensors, 2023, 23(11): 5249.

[15] [15] Li Y, Fan Q, Huang H, et al. A modified YOLOv8 detection network for UAV aerial image recognition[J]. Drones, 2023, 7(5): 304.

[16] [16] Chen J, Kao S, He H, et al. Run, don't walk: chasing higher FLOPS for faster neural networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 12021-12031.

[17] [17] Fang G, Ma X, Song M, et al. Depgraph: towards any structural pruning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 16091-16101.

[18] [18] Zhang R, Shao Z, Huang X, et al. Object detection in UAV images via global density fused convolutional network[J]. Remote Sensing, 2020, 12(19): 3140.

[19] [19] Zhu, C. He, Y. Savvides, M. Feature selective anchor-free module for single-shot object detection[C]//Proceedings of the 2019IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 16-20 June 2019: 840-849.