[2] [2] BENENSON R, OMRAN M, HOSANG J, et al. Ten years of pedestrian detection, what have we learned? ［C］//European Conference on Computer Vision. Cham: Springer, 2015: 613-627.

[3] [3] LAN W B, DANG J W, WANG Y P, et al. Pedestrian detection based on YOLO network model［C］//2018 IEEE International Conference on Mechatronics and Automation (ICMA). Changchun, China: IEEE, 2018: 1547-1551.

[4] [4] KUANG P, MA T S, LI F, et al. Real-time pedestrian detection using convolutional neural networks［J］. International Journal of Pattern Recognition and Artificial Intelligence, 2018, 32(11): 1856014.

[6] [6] ARRAS K O, MOZOS O M, BURGARD W. Using boosted features for the detection of people in 2D range data［C］//Proceedings 2007 IEEE International Conference on Robotics and Automation. Rome, Italy: IEEE, 2007: 3402-3407.

[7] [7] CHEN J N, YE P, SUN Z P. Pedestrian detection and tracking based on 2D lidar［C］//2019 6th International Conference on Systems and Informatics (ICSAI). Shanghai, China: IEEE: 2020: 421-426.

[8] [8] CAI G R, JIANG Z N, WANG Z Y, et al. Spatial aggregation net: Point cloud semantic segmentation based on multi-directional convolution［J］. Sensors, 2019, 19(19): 4329.

[9] [9] BEYER L, HERMANS A, LEIBE B. DROW: Real-time deep learning-based wheelchair detection in 2-D range data［J］. IEEE Robotics and Automation Letters, 2017, 2(2): 585-592.

[10] [10] JIA D, HERMANS A, LEIBE B. DR-SPAAM: A spatial-attention and auto-regressive model for person detection in 2D range data［C］//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Las Vegas, NV, USA: IEEE: 2021, 10270-10277.

[12] [12] HOU Q B, ZHOU D Q, FENG J S. Coordinate attention for efficient mobile network design［C］//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA: IEEE, 2021: 13708-13717.

[13] [13] LIU S T, HUANG D, WANG Y H. Learning spatial fusion for single-shot object detection［EB/OL］. 2019: arXiv: 1911.09516. https://arxiv.org/abs/1911.09516.

[14] [14] SPARBERT J, DIETMAYER K, STRELLER D. Lane detection and street type classification using laser range images［C］//ITSC 2001.2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585). Oakland, CA, USA： IEEE, 2002: 454-459.

[15] [15] GE Z, LIU S T, WANG F, et al. YOLOX: Exceeding YOLO series in 2021［EB/OL］. 2021: arXiv: 2107.08430. https://arxiv.org/abs/2107.08430

[16] [16] REDMON J, FARHADI A. YOLOv3: An incremental improvement［EB/OL］. 2018: arXiv: 1804.02767. https://arxiv.org/abs/1804.02767

[17] [17] TIAN Z, SHEN C H, CHEN H, et al. FCOS: fully convolutional one-stage object detection［C］//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South)： IEEE, 2020: 9626-9635.

[18] [18] YAN Y C, LI J P, QIN J, et al. Anchor-free person search［C］//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA： IEEE, 2021: 7686-7695.

[19] [19] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: Optimal speed and accuracy of object detection［EB/OL］. 2020: arXiv: 2004.10934. https://arxiv.org/abs/2004.10934.

[20] [20] STOLLENGA M, MASCI J, GOMEZ F, et al. Deep networks with internal selective attention through feedback connections［EB/OL］. 2014: arXiv: 1407.3068. https://arxiv.org/abs/1407.3068.

[22] [22] JIA D, STEINWEG M, HERMANS A, et al. Self-supervised person detection in 2D range data using a calibrated camera［C］//2021 IEEE International Conference on Robotics and Automation (ICRA). Xi′an, China： IEEE, 2021: 13301-13307.