[1] [1] JIANG P Y, ERGU D, LIU F Y, et al. A review of YOLO algorithm developments[J]. Procedia Computer Science, 2022, 199: 1066-1073.

[2] [2] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multi box detector[C]//European Conference on Computer Vision. Cham: Springer, 2016: 21-37.

[3] [3] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2980-2988.

[4] [4] 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, 2017, 39(6): 1137-1149.

[5] [5] DAI J, LI Y, HE K M, et al. R-FCN: object detection via region-based fully convolutional networks[C]//30th International Conference on Neural Information Processing System. Red Hook: Curran Associates Inc., 2016: 379-387.

[8] [8] SHEN C, MA C W, GAO W. Multiple attention mechanism enhanced YOLOX for remote sensing object detection[J]. Sensors, 2023, 23(3): 1261.

[9] [9] LI Y T, FAN Q S, HUANG H S, et al. A modified YOLOv8 detection network for UAV aerial image recognition[J]. Drones, 2023, 7(5): 304.

[12] [12] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.

[13] [13] GAO S H, CHENG M M, ZHAO K, et al. Res2Net: a new multi-scale backbone architecture[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 43(2): 652-662.

[15] [15] YANG G, LEI J, ZHU Z, et al. AFPN: asymptotic feature pyramid network for object detection[R]. Los Alamos: arXiv Preprint, 2023: arXiv: 2306.15988.

[17] [17] XIA G S, BAI X, DING J, et al. DOTA: a large-scale dataset for object detection in aerial images[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 3974-3983.