Vehicle Recognition Method Based on Improved YOLOv3 Algorithm

Yongshun Wang; Wenjie Jia; Chenfei Wang; Hui Song

doi:10.3788/LOP202158.1610010

[1] Zhang F K, Yang F, Li C. Fast vehicle detection method based on improved YOLOv3[J]. Computer Engineering and Applications, 55, 12-20(2019).

[2] Li H, Fu K, Yan M L et al. Vehicle detection in remote sensing images using denoizing-based convolutional neural networks[J]. Remote Sensing Letters, 8, 262-270(2017).

[3] Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite[C]. //2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA., 3354-3361(2012).

[4] Yu F, Chen H F, Wang X et al. BDD100K: a diverse driving dataset for heterogeneous multitask learning[C]. //2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, Seattle, WA, USA, 2633-2642(2020).

[5] Brostow G J, Fauqueur J, Cipolla R. Semantic object classes in video: a high-definition ground truth database[J]. Pattern Recognition Letters, 30, 88-97(2009).

[6] Zhuo D, Jing J F, Zhang H H et al. Classification of chopped strand mat defects based on convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 101009(2019).

[7] Yuan L S, Lou M Y, Liu Y Q et al. Palm vein classification based on deep neural network and random forest[J]. Laser & Optoelectronics Progress, 56, 101010(2019).

[8] Zhao H, An W S. Image salient object detection combined with deep learning[J]. Laser & Optoelectronics Progress, 55, 121003(2018).

[9] 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, June 23-28, 2014, Columbus, OH, USA., 580-587(2014).

[10] He K M, Zhang X Y, Ren S Q et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916(2015). http://www.sciencedirect.com/science/article/pii/S0031320315004252

[11] Girshick R. Fast R-CNN[C]. //2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile., 1440-1448(2015).

[12] Ren S Q, He K M, 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, 39, 1137-1149(2017).

[13] Dai J F, Li Y, He K M et al. R-FCN: object detection via region-based fully convolutional networks[C]. //2016 Conference on Neural Information Processing Systems, December 5, 2016, Red Hook, NY, United States, 379-387(2016).

[14] 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 (CVPR), June 27-30, 2016, Las Vegas, NV, USA, 779-788(2016).

[15] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. //Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).

[16] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 6517-6525(2017).

[17] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08)[2019-11-01]. https://arxiv.org/abs/1804.02767

[18] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 936-944(2017).

[19] Zheng Z H, Wang P, Liu W et al. Distance-IoU loss: faster and better learning for bounding box regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12993-13000(2020).