[1] [1] Felzenszwalb P, McAllester D, Ramanan D. A discriminatively trained, multiscale, deformable part model[C]//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Rec-ognition, 2008: 24–26 .

[2] [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 Intelli-gence, 2010, 32(9): 1627–1645.

[3] [3] Manana M, Tu C L, Owolawi P A. A survey on vehicle detection based on convolution neural networks[C]//Proceedings of the 3rd IEEE International Conference on Computer and Commu-nications, 2017: 1751–1755.

[6] [6] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal net-works[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015: 91–99.

[7] [7] Lin T Y, Dollar P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 936–944.

[8] [8] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779–788.

[9] [9] Redmon J, Farhadi A. YOLO9000: better, faster, strong-er[C]//Proceedings of 2017 IEEE Conference on Computer Vi-sion and Pattern Recognition, 2017: 6517–6525.

[10] [10] Redmon J, Farhadi A. YOLOv3: an incremental improve-ment[EB/OL]. arXiv:1804.02767[cs.CV].

[11] [11] Cai Z W, Fan Q F, Feris R S, et al. A unified multi-scale deep convolutional neural network for fast object detec-tion[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 354–370.

[12] [12] Liu W, Anguelov D, Erhan D, et al. SSD: single shot MultiBox detector[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 21–37.

[13] [13] He K M, Zhang X Y, Ren S Q, et al. Deep Residual Learning for Image Recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770–778.

[14] [14] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012: 1097–1105.

[15] [15] Jia Y Q, Shelhamer E, Donahue J, et al. Caffe: convolutional architecture for fast feature embedding[C]//Proceedings of the 22nd ACM international conference on Multimedia, 2014: 675–678.

[16] [16] Dai J F, Li Y, He K M, et al. R-FCN: object detection via re-gion-based fully convolutional networks[EB/OL]. ar-Xiv:1605.06409.