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Proceedings Paper

Detection of vehicle parts based on Faster R-CNN and relative position information
Author(s): Mingwen Zhang; Nong Sang; Youbin Chen; Changxin Gao; Yongzhong Wang
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Paper Abstract

Detection and recognition of vehicles are two essential tasks in intelligent transportation system (ITS). Currently, a prevalent method is to detect vehicle body, logo or license plate at first, and then recognize them. So the detection task is the most basic, but also the most important work. Besides the logo and license plate, some other parts, such as vehicle face, lamp, windshield and rearview mirror, are also key parts which can reflect the characteristics of vehicle and be used to improve the accuracy of recognition task. In this paper, the detection of vehicle parts is studied, and the work is novel. We choose Faster R-CNN as the basic algorithm, and take the local area of an image where vehicle body locates as input, then can get multiple bounding boxes with their own scores. If the box with maximum score is chosen as final result directly, it is often not the best one, especially for small objects. This paper presents a method which corrects original score with relative position information between two parts. Then we choose the box with maximum comprehensive score as the final result. Compared with original output strategy, the proposed method performs better.

Paper Details

Date Published: 8 March 2018
PDF: 8 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091G (8 March 2018); doi: 10.1117/12.2287087
Show Author Affiliations
Mingwen Zhang, Huazhong Univ. of Science and Technology (China)
Nong Sang, Huazhong Univ. of Science and Technology (China)
Youbin Chen, Hubei Micropattern Science and Technology Development Co., Ltd. (China)
Changxin Gao, Huazhong Univ. of Science and Technology (China)
Yongzhong Wang, Hubei Micropattern Science and Technology Development Co., Ltd. (China)


Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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