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Journal of Sensors
Volume 2017, Article ID 5627281, 10 pages
Research Article

Vehicle Detection Based on Deep Dual-Vehicle Deformable Part Models

1Institute of Automotive Engineering, Jiangsu University, Zhenjiang 212013, China
2School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
3School of Automotive and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China

Correspondence should be addressed to Yong Zhang; moc.361@sj.yz

Received 8 June 2017; Revised 29 September 2017; Accepted 10 October 2017; Published 5 December 2017

Academic Editor: Mi Zhang

Copyright © 2017 Yingfeng Cai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Vehicle detection plays an important role in safe driving assistance technology. Due to the high accuracy and good efficiency, the deformable part model is widely used in the field of vehicle detection. At present, the problem related to reduction of false positivity rate of partially obscured vehicles is very challenging in vehicle detection technology based on machine vision. In order to address the abovementioned issues, this paper proposes a deep vehicle detection algorithm based on the dual-vehicle deformable part model. The deep learning framework can be used for vehicle detection to solve the problem related to incomplete design and other issues. In this paper, the deep model is used for vehicle detection that consists of feature extraction, deformation processing, occlusion processing, and classifier training using the back propagation (BP) algorithm to enhance the potential synergistic interaction between various parts and to get more comprehensive vehicle characteristics. The experimental results have shown that proposed algorithm is superior to the existing detection algorithms in detection of partially shielded vehicles, and it ensures high detection efficiency while satisfying the real-time requirements of safe driving assistance technology.