[2] [2] WU X，LI W，HONG D F，et al. Deep learning for unmanned aerial vehicle-based object detection and tracking: a survey［J］. IEEE Geoscience and Remote Sensing Magazine，2021，10(1): 91-124.

[4] [4] LIU Y，SUN P，WERGELES N M，et al. A survey and performance evaluation of deep learning methods for small object detection［J］. Expert Systems with Applications，2021，172: 114602.

[5] [5] GIRSHICK R，DONAHUE J，DARRELL T，et al. Rich feature hierarchies for accurate object detection and semantic segmentation［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Columbus: IEEE，2014: 580-587.

[6] [6] REDMON J，DIVVALA S，GIRSHICK R，et al. You only look once: unified，real-time object detection［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas: IEEE，2016: 779-788.

[9] [9] ZHU X K，LYU S C，WANG X，et al. TPH-YOLOv5: improved YOLOv5 based on Transformer prediction head for object detection on drone-captured scenarios［C］//2021 IEEE/CVF International Conference on Computer Vision Workshops(ICCVW). Montreal: IEEE，2021: 2778-2788.

[11] [11] LIU S H，ZHA J L，SUN J，et al. EdgeYOLO: an edge-real-time object detector［C］//2023 42nd Chinese Control Conference (CCC). Tianjin: IEEE，2023: 7507-7512.

[12] [12] WANG J W，XU C，YANG W，et al. A normalized Gaussian Wasserstein distance for tiny object detection［R］. Los Alamos: arXiv Preprint，2021: arXiv: 2110.13389.

[13] [13] ZHANG Z X. Drone-YOLO: an efficient neural network method for target detection in drone images［J］. Drones，2023，7(8): 526.

[14] [14] TANG S Y，FANG Y N，ZHANG S. HIC-YOLOv5: improved YOLOv5 for small object detection［R］. Los Alamos: arXiv Preprint，2023: arXiv: 2309.16393.

[15] [15] SUNKARA R，LUO T. No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects［C］//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer，2022: 443-459.