[1] [1] SHAO Yanhua, ZHANG Duo, CHU Hongyu, et al. A review of YOLO object detection based on deep learning[J]. Journal of Electronics and Information Science, 2022, 44(10): 3697-3708.

[2] [2] LIU Hongjiang, WANG Mao, LIU Lihua, et al. A review of small object detection based on deep learning[J]. Computer Engineering and Science, 2021, 43(8): 1429-1442.

[3] [3] LIU Y, SUN P, Wergeles N, et al. A survey and performance evaluation of deep learning methods for small object detection[J]. Expert Systems with Applications, 2021, 172(4): 114602.1-114602.14.

[4] [4] ZUO Z, TONG X, WEI J, et al. AFFPN: attention fusion feature pyramid network for small infrared target detection[J]. Remote Sensing, 2022, 14(14): 3412.

[5] [5] ZHANG Chuancong, LI Fanming, RAO Junmin. Infrared small target detection based on feature saliency fusion[J]. Semiconductor Optoelectronics, 2022, 43(4): 828-834.

[6] [6] HAN Jinhui, WEI Yantao, PENG Zhenming, et al. Overview of infrared weak target detection methods[J]. Infrared and Laser Engineering, 2022, 51(4): 428-451.

[7] [7] Viola P, Jones M. Robust real-time face detection[J]. International Journal of Computer Vision, 2004, 57(2): 137-154.

[8] [8] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). IEEE, 2005, 1: 886-893.

[9] [9] Felzenszwalb P, Mcallester D, Ramanan D. A discriminatively trained, multiscale, deformable part model[C]//2008 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2008: 1-8.

[10] [10] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.

[11] [11] CAI Jie, ZHANG Huayi, WU Jingxuan, et al. Research on a lightweight infrared target detection network for power equipment based on YOLOv4 tin[J]. China Science and Technology Information, 2022, 11(14): 111-114.

[12] [12] LI Xiangrong, SUN Lihui. Multi scale infrared target detection using fusion attention mechanism[J]. Infrared Technology, 2022, 21(4): 4-13.

[13] [13] Banuls A, Mandow A, Vazquez R, et al. Object detection from thermal infrared and visible light cameras in search and rescue scenes[C]// 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). IEEE, 2020: 380-386.

[14] [14] JU Moran, LUO Haibo, LIU Guangqi, et al. Infrared weak target detection network using spatial attention mechanism[J]. Optical Precision Engineering, 2021, 29(4): 843-853.

[15] [15] LI C, LI L, JIANG H, et al. YOLOv6: A single-stage object detection framework for industrial applications[J]. arXiv preprint arXiv, 2022: 2209.02976.

[16] [16] 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.

[17] [17] LI Y, CHEN Y, WANG N, et al. Scale-aware trident networks for object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 6054-6063.

[18] [18] HWANG S, PARK J, KIM N, et al. Multispectral pedestrian detection: Benchmark dataset and baseline[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1037-1045.

[19] [19] LI C, LIANG X, LU Y, et al. RGB-T object tracking: Benchmark and baseline[J]. Pattern Recognition, 2019, 96: 106977.

[20] [20] LIN T Y, Maire M, Belongie S, et al. Microsoft coco: Common objects in context[C]// European Conference on Computer Vision, 2014: 740-755.