融合多尺度上下文卷积特征的车辆目标检测
高琳, 陈念年, 范勇. 融合多尺度上下文卷积特征的车辆目标检测[J]. 光电工程, 2019, 46(4): 180331.
Gao Lin, Chen Niannian, Fan Yong. Vehicle detection based on fusing multi-scale context convolution features[J]. Opto-Electronic Engineering, 2019, 46(4): 180331.
[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] 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] 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.
[4] Cao S Y, Liu Y H, Li X Z. Vehicle detection method based on fast R-CNN[J]. Journal of Image and Graphics, 2017, 22(5): 671–677.曹诗雨, 刘跃虎, 李辛昭. 基于 Fast R-CNN的车辆目标检测[J].中国图象图形学报, 2017, 22(5): 671–677.
[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] 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] 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] 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] Redmon J, Farhadi A. YOLOv3: an incremental improve-ment[EB/OL]. arXiv:1804.02767[cs.CV].
[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] 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] 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] 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] 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] 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.
高琳, 陈念年, 范勇. 融合多尺度上下文卷积特征的车辆目标检测[J]. 光电工程, 2019, 46(4): 180331. Gao Lin, Chen Niannian, Fan Yong. Vehicle detection based on fusing multi-scale context convolution features[J]. Opto-Electronic Engineering, 2019, 46(4): 180331.