[2] [2] Papageorgiou C P, Oren M, Poggio T. A general framework for object detection[C]//Sixth International Conference on Computer Vision. IEEE, 1998: 555-562.

[3] [3] 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.

[4] [4] Hearst M A, Dumais S T, Osuna E, et al. Support vector machines[J]. IEEE Intelligent Systems and their Applications, 1998, 13(4): 18-28.

[5] [5] Freund Y, Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting[J]. Journal of Computer and System Sciences, 1997, 55(1): 119-139.

[6] [6] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.

[7] [7] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 7263-7271.

[8] [8] REDMON J, FARHADI A. Yolov3: an incremental improvement[J]. arXiv Preprint, 2018, arXiv: 180402767.

[9] [9] Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. arXiv Preprint, 2020, arXiv: 2004.10934.

[10] [10] Liu W, Anguelov D, Erhan D, et al. Ssd: single shot multibox detector[C]//Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14,2016, Proceedings, Part I 14. Springer International Publishing, 2016: 21-37.

[11] [11] Li Z, Yang L, Zhou F. FSSD: feature fusion single shot multibox detector[J]. arXiv Preprint, 2017, arXiv: 171200960.

[14] [14] Luo X, Zhu H, Zhang Z. IR-YOLO: real-time infrared vehicle and pedestrian detection[J]. Computers, Materials & Continua, 2024, 78(2): 2667.

[15] [15] Sunkara R, Luo T. No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer Nature Switzerland, 2022: 443-459.

[16] [16] Tong Z, Chen Y, X Z, et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism[J]. arXiv Preprint, 2023, arXiv: 2301.10051.

[17] [17] Han K, Wang Y, Tian Q, et al. Ghostnet: more features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 1580-1589.

[18] [18] Zheng Z, Wang P, Ren D, et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J]. IEEE Transactions on Cybernetics, 2021, 52(8): 8574-8586.

[19] [19] Du S, Zhang B, Zhang P, et al. An improved bounding box regression loss function based on CIOU loss for multi-scale object detection[C]//2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML). IEEE, 2021: 92-98.

[20] [20] Zhang Y F, Ren W, Zhang Z, et al. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing, 2022, 506: 146-157.

[21] [21] 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]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 7464-7475.

[22] [22] Zhu X, Lyu S, Wang X, et al. TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 2778-2788.