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Proceedings Paper

Co-occurrence relationship encoding via channel merging for vehicle part recognition
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Paper Abstract

Vehicle part recognition aims to determine the subcategories of each vehicle part. Existing algorithms consider to recognize each category as independent classification tasks, which ignore the potential co-occurrence relationship between vehicle parts. In addition, it remains challenges to obtain satisfactory results due to the small intra- class difference. In this paper, we propose a part-pair recognition method based on deep learning by utilizing the co-occurrence relationship. Specifically, we construct a deep neural network for vehicle part recognition, which can use the co-occurrence relationship and recognize two vehicle part simultaneously. We also propose a massive dataset of vehicle parts with fully annotated labels for training and testing. Extensive experimental results demonstrate that the proposed method performs favorably against the state-of-the-art vehicle recognition algorithms.

Paper Details

Date Published: 14 February 2020
PDF: 10 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301Z (14 February 2020); doi: 10.1117/12.2541920
Show Author Affiliations
Qinwei Chang, Huazhong Univ. of Science and Technology (China)
Nong Sang, Huazhong Univ. of Science and Technology (China)
Changxin Gao, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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