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Vehicle recognition using multi-task cascaded network
Author(s): Hua Gong; Yong Zhang; Fang Liu; Ke Xu
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Paper 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.

Paper Details

Date Published: 12 March 2019
PDF: 7 pages
Proc. SPIE 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 1102351 (12 March 2019); doi: 10.1117/12.2520850
Show Author Affiliations
Hua Gong, Shenyang Ligong Univ. (China)
Yong Zhang, Technology on Electro-Optical Information Security Control Lab. (China)
Fang Liu, Shenyang Ligong Univ. (China)
Ke Xu, Shenyang Ligong Univ. (China)


Published in SPIE Proceedings Vol. 11023:
Fifth Symposium on Novel Optoelectronic Detection Technology and Application
Qifeng Yu; Wei Huang; You He, Editor(s)

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