[2] [2] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2014: 580-587.

[3] [3] Ren S, He K, Girshick R, et al. Faster r-cnn: towards real-time object detection with region proposal networks[J]. IEEE Transaction Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.

[4] [4] He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9): 1904-1916.

[5] [5] 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.

[6] [6] Redmon J, Farhadi A. Yolov3: an incremental improvement[J]. arXiv preprint arXiv: 1804.02767, 2018.

[7] [7] 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: 21-37.

[8] [8] Zhou X, Zhuo J, Krahenbuhl P. Bottom-up object detection by grouping extreme and center points[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 850-859.

[9] [9] Law H, Deng J. Cornernet: detecting objects as paired keypoints[C]//Proceedings of the European conference on computer vision (ECCV), 2018: 734-750.

[10] [10] Zhou X, Wang D, Krhenbhl P. Objects as points[J]. arXiv preprint arXiv: 1904.07850, 2019.

[13] [13] Woo S, Park J, Lee J Y, et al. Cbam: convolutional block attention module[C]//Proceedings of the European conference on computer vision (ECCV), 2018: 3-19.

[14] [14] Li H, Li J, Wei H, et al. Slim-neck by GSConv: a better design paradigm of detector architectures for autonomous vehicles[J]. arXiv preprint arXiv: 2206.02424, 2022.

[15] [15] F Chollet. Xception: deep learning with depthwise separable convolutions[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017: 1800-1807.

[16] [16] Ma, N., Zhang, X., Zheng, HT., et al. ShuffleNet V2: practical guidelines for efficient CNN architecture design[C]//ECCV 2018. Lecture Notes in Computer Science, 2018.