[1] Zhao Yuemeng, Liu Huigang. Detection and tracking of low-altitude unmanned aerial vehicles based on optimized YOLOv4 algorithm [J]. Laser & Optoelectronics Progress, 2022, 59(12): 397-406. (in Chinese) doi:  10.3788/LOP202259.1215017
[2] Bao Wenqi, Xie Liqiang, Xu Caihua, et al. Real-time detection method of micro UAV based on YOLOv5 [J]. Journal of Ordnance Equipment Engineering, 2022, 43(5): 232-237. (in Chinese) doi:  10.11809/bqzbgcxb2022.05.037
[3] Wang Jiannan, Lv Shengtao, Niu Jian. Drone detection method based on improved YOLOv5 [J]. Optics & Optoelectronic Technology, 2022, 20(5): 48-56. (in Chinese) doi:  10.19519/j.cnki.1672-3392.2022.05.015
[4] Liu Shanliang, Wu Renbiao, Qu Jingyi, et al. Bi PPYOLO tiny: A lightweight airport UAV detection method [J]. Journal of Safety and Environment, 2023, 23(2): 480-488. (in Chinese) doi:  10.13637/j.issn.1009-6094.2021.1818
[5] Zhang Lingling, Wang Peng, Li Xiaoyan, et al. Low-altitude UAV detection method based on optimized SSD [J]. Computer Engineering and Applications, 2022, 58(16): 204-212. (in Chinese) doi:  10.3778/j.issn.1002-8331.2201-0067
[6] Ma Qi, Zhu Bin, Zhang Hongwei, et al. Low-altitude UAV detection and recognition method based on optimized YOLOv3 [J]. Laser & Optoelectronics Progress, 2019, 56(20): 201006. (in Chinese) doi:  10.3788/LOP56.201006
[7] Zhou Qiang, Xia Mingyun. Research on Multi-UAV detection based on improved SSD algorithm [J]. Information Technology, 2020, 44(12): 71-76. (in Chinese) doi:  10.13274/j.cnki.hdzj.2020.12.014
[8] Zhang Ruixin, Li Ning, Zhang Xiaxia, et al. Low-altitude UAV detection method based on optimized CenterNet [J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(11): 2335-2344. (in Chinese) doi:  10.13700/j.bh.1001-5965.2021.0108
[9] Qi Jiangxin, Wu Ling, Lu Faxing, et al. UAV cluster detection based on improved YOLOv4 algorithm [J]. Journal of Ordnance Equipment Engineering, 2022, 43(6): 210-217. (in Chinese) doi:  10.11809/bqzbgcxb2022.06.033
[10] Wang C, Meng L, Gao Q, et al. A lightweight UAV swarm detection method integrated attention mechanism [J]. Drones, 2022, 7(1): 13. doi:  10.3390/drones7010013
[11] Sunkara R, Luo T. No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects[DB/OL]. (2022-08-07) https://arxiv.org/abs/2208.03641
[12] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
[13] Hu H, Gu J, Zhang Z, et al. Relation networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 3588-3597.
[14] Gevorgyan Z. SIoU loss: More powerful learning for bounding box regression[DB/OL].(2022-05-25) https://arxiv.org/abs/2205.12740
[15] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2980-2988.