Vehicle recognition using multi-task cascaded network

Hua Gong; Yong Zhang; Fang Liu; Ke Xu

doi:10.1117/12.2520850

12 March 2019 Vehicle recognition using multi-task cascaded network

Hua Gong, Yong Zhang, Fang Liu, Ke Xu

Proceedings Volume 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application; 1102351 (2019) https://doi.org/10.1117/12.2520850
Event: Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 2018, Xi'an, China

Abstract

Vehicle attribute recognition mainly contains two tasks: vehicle object location and vehicle category recognition. We propose a multi-task cascaded model MC-CNN, which integrates the improved Faster R-CNN and CNN. The first stage uses the improved Faster R-CNN network (IFR-CNN) to process the object location, and the second stage uses the improved CNN network (ICNN) to realize the object recognition. In IFR-CNN sub network, a max pooling and the deconvolution operation are added to the shallow layers of Faster R-CNN network. IFR-CNN can extract features from the different levels and increase the location information of shallow object. In ICNN sub network, we optimize the information extraction ability of high-level semantics in the middle layers and the deep layers of CNN network. The experimental results show that MC-CNN network proposed in this paper has better attribute recognition accuracy on BIT-Vehicle dataset and SYIT-Vehicle dataset than the single Faster R-CNN and CNN network models.

Citation Download Citation

Hua Gong, Yong Zhang, Fang Liu, and Ke Xu "Vehicle recognition using multi-task cascaded network", Proc. SPIE 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 1102351 (12 March 2019); https://doi.org/10.1117/12.2520850

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