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

[2] 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).

[3] DOLLÁR P, APPEL R, BELONGIE S et al. Fast feature pyramids for object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 1532-1545(2014).

[4] ZHANG S F, WEN L Y, BIAN X et al. Occlusion-aware R-CNN: detecting pedestrians in a crowd[C], 637-653(2018).

[5] LIU S T, HUANG D, WANG Y H. Adaptive NMS: refining pedestrian detection in a crowd[C], 6452-6461(2019).

[6] HOU Y Z, ZHENG L, GOULD S. Multiview detection with feature perspective transformation[C], 1-18(2020).

[7] HONG M B, LI S W, YANG Y C et al. SSPNet: scale selection pyramid network for tiny person detection from UAV images[J]. IEEE Geoscience and Remote Sensing Letters, 19, 8018505(2022).

[8] ZHANG X W, CAO S, CHEN C L Z. Scale-aware hierarchical detection network for pedestrian detection[J]. IEEE Access, 8, 94429-94439(2020).

[9] WANG T C, ANWER R M, CHOLAKKAL H et al. Learning rich features at high-speed for single-shot object detection[C], 1971-1980(2019).

[10] ZHANG Y, HE H Y, LI J G et al. Variational pedestrian detection[C], 11617-11626(2021).

[11] CHEN Y X, LIN W L et al. CA-YOLOv5 for crowded pedestrian detection[J]. Computer Engineering and Applications, 58, 238-245(2022).

[12] ZHANG Y, ZHANG S F, LIU W M. A small-scale pedestrian detection method based on fused residual networks and feature pyramids[J]. Journal of Transport Information and Safety, 41, 111-118, 156(2023).

[13] 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).

[14] FENG A Q, WU X J, XU T Y. Real-time traffic sign detection algorithm combining attention mechanism and contextual information[J]. Journal of Frontiers of Computer Science and Technology, 17, 2676-2688(2023).

[15] OUYANG D L, HE S, ZHANG G Z et al. Efficient multi-scale attention module with cross-spatial learning[C], 1-5(2023).

[16] YANG L X, ZHANG R Y, LI L D et al. SimAM: a simple, parameter-free attention module for convolutional neural networks[C], 11863-11874(2021).

[17] LIN T Y, GOYAL P, GIRSHICK R et al. Focal loss for dense object detection[C], 2999-3007(2017).

[18] GEVORGYAN Z. SIoU loss: more powerful learning for bounding box regression[J/OL](2022).

[19] ZHENG Z H, WANG P, REN D W et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J]. IEEE Transactions on Cybernetics, 52, 8574-8586(2022).

[20] ZHANG S F, XIE Y L, WAN J et al. WiderPerson: a diverse dataset for dense pedestrian detection in the wild[J]. IEEE Transactions on Multimedia, 22, 380-393(2020).

[21] SHAO S, ZHAO Z J, LI B X et al. CrowdHuman: a benchmark for detecting human in a crowd[J/OL](2018).

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

[23] REN S Q, HE K M, GIRSHICK R et al. Faster R-CNN: towards real-time object detection with region proposal networks[C], 91-99(2015).

[24] TAN M X, LE Q V. EfficientNet: rethinking model scaling for convolutional neural networks[C](2019).

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

[26] 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).

[27] VARGHESE R, SAMBATH M. YOLOv8: a novel object detection algorithm with enhanced performance and robustness[C], 1-6(2024).

[28] WANG C Y, YEH I H, LIAO H Y M. YOLOv9: learning what you want to learn using programmable gradient information[J/OL](2024).

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