[1] HONG T, LIANG H M, YANG Q Y et al. A real-time tracking algorithm for multi-target UAV based on deep learning[J]. Remote Sens, 15, 2(2022).

[2] VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features[C](2001).

[3] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C], 886-893(2005).

[4] FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1627-1645(2010).

[5] XIE S N, GIRSHICK R, DOLLÁR P et al. Aggregated residual transformations for deep neural networks[C], 5987-5995(2017).

[6] GIRSHICK R, DONAHUE J, DARRELL T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C], 580-587(2014).

[7] REN S, HE K, GIRSHICK R et al. Faster R-CNN： Towards real-time object detection with region proposal networks，” in Proc[J]. Adv.Neural Inf. Process. Syst., 28, 1-9(2015).

[8] HE K et al. Mask R-CNN[C], 2961-2969(2017).

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

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

[11] REDMON J, FARHADI A. YOLOv3： An Incremental Improvement[webpage]. arXiv, 1804-02767(2018). http：//arxiv.org/abs/1804.02767

[12] LIU W, ANGUELOV D, ERHAN D et al. SSD： Single Shot Multibox Detector[webpage]. arXiv, 1512-02325(2015). http：//arxiv.org/abs/1512.02325

[13] TAN M X, PANG R M, LE Q V. EfficientDet： scalable and efficient object detection[C], 10778-10787(2020).

[14] ZHANG S F, WEN L Y, BIAN X et al. Single-shot refinement neural network for object detection[C], 4203-4212(2018).

[15] WANG E S, ZHANG H X, XU S et al. Research on UAV detection algorithm based on Attention feature fusion[J]. Journal of Beijing University of Aeronautics and Astronautics, 1-11(2024).

     王尔申, 张宏轩, 徐嵩. 基于注意力特征融合的无人机检测算法研究[J]. 北京航空航天大学学报, 1-11(2024).

[16] ZHANG H J, LI K P, JI M X et al. UAV detection algorithm based on spatial correlation enhancement[J]. Journal of Zhejiang University （Engineering Science）, 58, 468-479(2024).

     张会娟, 李坤鹏, 姬淼鑫. 基于空间相关性增强的无人机检测算法[J]. 浙江大学学报（工学版）, 58, 468-479(2024).

[17] 高武奇, 杨婷, 李亮亮. 基于多特征交叉融合及跨层级联的航拍目标检测算法[J]. 西北工业大学学报, 41, 1179-1189(2023).

     GAO W Q, YANG T, LI L L. Aerial military target detection algorithm based on multi-feature cross fusion and cross-layer concatenation[J]. Journal of Northwestern Polytechnical University, 41, 1179-1189(2023).

[18] 李校林, 刘大东, 刘鑫满. 改进YOLOv5的无人机航拍图像目标检测算法[J]. 计算机工程与应用, 60, 204-214(2024).

     LI X L, LIU D D, LIU X M et al. Improved YOLOv5 UAV Aerial image target Detection Algorithm[J]. Computer Engineering and Applications, 60, 204-214(2024).

[19] WU Z H, LIU C L, WEN J et al. Selecting high-quality proposals for weakly supervised object detection with bottom-up aggregated attention and phase-aware loss[J]. IEEE Transactions on Image Processing, 32, 682-693(2023).

[20] XIE Y M, ZHANG L W, YU X Y et al. YOLO-MS： multispectral object detection via feature interaction and self-attention guided fusion[J]. IEEE Transactions on Cognitive and Developmental Systems, 15, 2132-2143(2023).

[21] 陈健松, 蔡艺军. 面向垃圾分类场景的轻量化目标检测方案[J]. 浙江大学学报（工学版）, 58, 71-77(2024).

     CHEN J S, CAI Y J. Lightweight object detection scheme for garbage classification scenario[J]. Journal of Zhejiang University （Engineering Science）, 58, 71-77(2024).

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

[23] HE K M, ZHANG X Y, REN S Q et al. Deep residual learning for image recognition[C], 770-778(2016).