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

Qinwei Chang; Nong Sang; Changxin Gao

doi:10.1117/12.2541920

14 February 2020 Co-occurrence relationship encoding via channel merging for vehicle part recognition

Qinwei Chang, Nong Sang, Changxin Gao

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114301Z (2020) https://doi.org/10.1117/12.2541920
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

Citation Download Citation

Qinwei Chang, Nong Sang, and Changxin Gao "Co-occurrence relationship encoding via channel merging for vehicle part recognition", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301Z (14 February 2020); https://doi.org/10.1117/12.2541920

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