[1] ZHAO B R, WU S K, JIA C B et al. Research on UAV inspection technology for transmission lines based on artificial intelligence[C], 153-159(2024).

[2] SUN Y Q, CHEN M Y, HUANG Q et al. Application of low-altitude unmanned aerial vehicle remote sensing in the phenotypic analysis of oil crops[J]. Journal of Zhejiang University (Agriculture & Life Sciences), 49, 472-483(2023).

[3] ZHAO X Y, LV X Z, XIAO Y H et al. Scheme design and application of intelligent inspection of geological hazards based on UAV hangar[J/OL]. Express Water Resources & Hydropower Information, 1-14(2024). http://kns.cnki.net/kcms/detail/42.1142.TV.20241028.1503.002.html.

[4] MARTINEZ-ALPISTE I, GOLCARENARENJI G, WANG Q et al. Search and rescue operation using UAVs: a case study[J]. Expert Systems with Applications, 178, 114937(2021).

[5] LI J H, TIAN J Z, LV Y Y et al. Covert tracking of small maritime target using unmanned aerial vehicle based on model predictive control[J]. Journal of Naval Aviation University, 39, 737-744(2024).

[6] 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, 37, 1904-1916(2015).

[7] 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, 39, 1137-1149(2017).

[8] LIN T Y, DOLLÁR P, GIRSHICK R et al. Feature pyramid networks for object detection[C], 936-944(2017).

[9] LIU W, ANGUELOV D, ERHAN D et al. SSD: single shot MultiBox detector[C], 21-37(2016).

[10] LIN T Y, GOYAL P, GIRSHICK R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327(2020).

[11] REDMON J, DIVVALA S, GIRSHICK R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

[12] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

[13] REDMON J, FARHADI A. YOLOv3: an incremental improvement[J/OL](2018).

[14] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J/OL](2020).

[15] 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], 7464-7475(2023).

[16] WANG A, CHEN H, LIU L H et al. YOLOv10: real-time end-to-end object detection[C](2024).

[17] CARION N, MASSA F, SYNNAEVE G et al. End-to-end object detection with transformers[C], 213-229(2020).

[18] BAI Y, ZHOU Y Y, AN S B. Research on UAV small object detection method improved by YOLOv5[J]. Computer Engineering and Applications, 60, 276-284(2024).

[19] JIN L W, XU W M, LI Y X. Object detection method for aerial images base on adaptive slicing aided inference[J]. Chinese Journal of Liquid Crystals and Displays, 40, 472-480(2025).

[20] 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], 2778-2788(2021).

[21] CHALAVADI V, JERIPOTHULA P, DATLA R et al. mSODANet: a network for multi-scale object detection in aerial images using hierarchical dilated convolutions[J]. Pattern Recognition, 126, 108548(2022).

[22] MA Y, CHAI L, JIN L Z. Scale decoupled pyramid for object detection in aerial images[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 4704314(2023).

[23] ZHANG Y Z, WU C Y, GUO W et al. CFANet: efficient detection of UAV image based on cross-layer feature aggregation[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 5608911(2023).

[24] HOWARD A G, ZHU M L, Chen B et al. MobileNets: efficient convolutional neural networks for mobile vision applications[J/OL](2017).

[25] HAN K, WANG Y H, TIAN Q et al. GhostNet: more features from cheap operations[C], 1577-1586(2020).

[26] SUNKARA R, LUO T. No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects[C], 443-459(2022).

[27] JOCHER G. Ultralytics YOLO11[EB/OL](2024). https://github.com/ultralytics/ultralytics

[28] LIU S, QI L, QIN H F et al. Path aggregation network for instance segmentation[C], 8759-8768(2018).

[29] CAO Y R, HE Z J, WANG L J et al. VisDrone-DET2021: the vision meets drone object detection challenge results[C], 2847-2854(2021).

[30] YU H Y, LI G R, ZHANG W G et al. The unmanned aerial vehicle benchmark: object detection, tracking and baseline[J]. International Journal of Computer Vision, 128, 1141-1159(2020).

[31] ZHAO Y, LV W Y, XU S L et al. DETRs beat YOLOs on real-time object detection[C], 16965-16974(2024).

[32] NING T, WU W T, ZHANG J. Small object detection based on YOLOv8 in UAV perspective[J]. Pattern Analysis and Applications, 27, 103(2024).

[33] TANG S Y, ZHANG S, FANG Y N. HIC-YOLOv5: improved YOLOv5 for small object detection[C], 6614-6619(2024).

[34] ZENG Y L, GUO D J, HE W K et al. ARF-YOLOv8: a novel real-time object detection model for UAV-captured images detection[J]. Journal of Real-Time Image Processing, 21, 107(2024).