[3] [3] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2016: 779-788.

[4] [4] Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector[C]//European Conference on Computer Vision, Amsterdam. Springer, 2016: 21-37.

[5] [5] Uijlings J, Sande K, Gevers T, et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104: 154-171.

[6] [6] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2014: 580-587.

[7] [7] Girshick R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision. IEEE, 2015, 1440－1448.

[8] [8] Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.

[11] [11] Luo J, Fang H, Shao F, et al. Multi-scale traffic vehicle detection based on faster R-CNN with NAS optimization and feature enrichment[J]. Defence Technology, 2021, 17(4): 1542-1554.

[12] [12] Alsanabani A A, Saeed S A, Al-Mkhlafi M, et al. A low cost and real time vehicle detection using enhanced YOLOv4-Tiny[C]//2021 IEEE International Conference on Artificial Intelligence and Computer Applications. IEEE, 2021: 372-377.

[14] [14] Woo S, Park J, Lee J, et al. CBAM: Convolutional block attention module[C]//15th European Conference. Springer, 2018: 3-19.

[15] [15] Lin T, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//2017 IEEE International Conference on Computer Vision. IEEE, 2017: 2999-3007.

[16] [16] Li X, Wang W, Wu L, et al. Generalized focal loss: learning qualified and distributed bounding boxes for dense object detection[C]//34th International Conference on Neural Information Processing Systems. ACM, 2020: 21002-21012.

[17] [17] Wang C, Liao H, Wu Y, et al. CSPNet: A new backbone that can enhance learning capability of CNN[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. IEEE, 2020: 1571-1580.

[18] [18] Howard A, Zhu M, Chen B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[EB]. arXiv: 1704.04861, 2017.

[19] [19] Lin M, Chen Q, Yan S. Network in network[EB]. arXiv: 1312.4400, 2013.

[20] [20] Chollet F. Xception: Deep learning with depthwise separable convolutions[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2017: 1800-1807.

[21] [21] Han K, Wang Y, Tian Q, et al. GhostNet: More features from cheap operations[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2020: 1577 -1586.

[22] [22] Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2021: 13708-13717.