[3] [3] DAUD S M S M, YUSOF M Y P M, HEO C C, et al. Applications of drone in disaster management: a scoping review[J]. Science & Justice, 2021，62(1)：30-42.

[4] [4] REJEB A, ABDOLLAHI A, REJEB K, et al. Drones in agriculture: a review and bibliometric analysis[J]. Computers and Electronics in Agriculture, 2022, 198: 107017.

[6] [6] LI C Y, LI L, JIANG H L, et al. YOLOv6: a single-stage object detection framework for industrial applications[R]. Los Alamos：arXiv Preprint, 2022: arXiv: 2209.02976.

[7] [7] 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]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023: 7464-7475.

[8] [8] GE Z, LIU S T, WANG F, et al. YOLOx: exceeding YOLO series in 2021[R]. Los Alamos: arXiv Preprint, 2021: arXiv: 2107.08430.

[9] [9] GIRSHICK R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision. Washington DC: IEEE, 2015: 1440-1448.

[10] [10] SHEN S Y, ZHANG X, YAN W J, et al. An improved UAV target detection algorithm based on ASFF-YOLOv5s[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10773-10789.

[11] [11] HU Q, LI L, DUAN J, et al. Object detection algorithm of UAV aerial photography image based on anchor-free algorithms[J]. Electronics, 2023, 12(6): 1339.

[12] [12] TANG W Q, SUN J, WANG G. Horizontal feature pyramid network for object detection in UAV images[C]//2021 China Automation Congress (CAC). Beijing: IEEE, 2021: 7746-7750.

[13] [13] XI Y, JIA W J, MIAO Q G, et al. FiFoNet: fine-grained target focusing network for object detection in UAV images[J]. Remote Sensing, 2022, 14(16): 3919.

[15] [15] ZUO Z, TONG X Z, WEI J Y, et al. AFFPN: attention fusion feature pyramid network for small infrared target detection[J]. Remote Sensing, 2022, 14(14): 3412.

[17] [17] DAI Y M, GIESEKE F, OEHMCKE S, et al. Attentional feature fusion[C]//2021 IEEE Winter Conference on Applications of Computer Vision (WACV). Waikoloa: IEEE, 2021: 3560-3569.

[18] [18] ZHANG Y F, REN W Q, ZHANG Z, et al. Focal and efficient IoU loss for accurate bounding boxregression[J]. Neurocomputing, 2022, 506: 146-157.

[19] [19] HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015: 37(9): 1904-1916.

[20] [20] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018: 40(4): 834-848.

[21] [21] LIU S T, HUANG D. Receptive field block net for accurate and fast object detection[C]//Proceedings of the European Conference on Computer Vision (ECCV). Cham: Springer, 2018: 385-400.

[22] [22] GEVORGYAN Z. SIoU loss: more powerful learning for bounding box regression[R]. Los Alamos: arXiv Preprint, 2022: arXiv: 2205.12740.

[23] [23] TONG Z J, CHEN Y H, XU Z W, et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism[R]. Los Alamos: arXiv Preprint, 2023: arXiv: 2301.10051.

[24] [24] WANG X, HE N, HONG C, et al. YOLO-ERF: lightweight object detector for UAV aerial images[J]. Multimedia Systems, 2023, 29: 3329-3339.

[25] [25] YANG Y H, GAO X Z, WANG Y, et al. VAMYOLOX: an accurate and efficient object detection algorithm based on visual attention mechanism for UAV optical sensors[J]. IEEE Sensors Journal, 2023, 23(11): 11139-11155.

[26] [26] YANG R J, LI W F, SHANG X N, et al. KPE-YOLOv5: an improved small target detection algorithm based on YOLOv5[J]. Electronics, 2023, 12(4): 817.