Color, as a notable and stable attribute of vehicles, can serve as a useful and reliable cue in a variety of applications in intelligent transportation systems. Therefore, vehicle color recognition in natural scenes has become an important research topic in this area. In this paper, we propose a deep-learning-based algorithm for automatic vehicle color recognition. Different from conventional methods, which usually adopt manually designed features, the proposed algorithm is able to adaptively learn representation that is more effective for the task of vehicle color recognition, which leads to higher recognition accuracy and avoids preprocessing. Moreover, we combine the widely used spatial pyramid strategy with the original convolutional neural network architecture, which further boosts the recognition accuracy. To the best of our knowledge, this is the first work that employs deep learning in the context of vehicle color recognition. The experiments demonstrate that the proposed approach achieves superior performance over conventional methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle Color Recognition With Spatial Pyramid Deep Learning


    Contributors:
    Hu, Chuanping (author) / Bai, Xiang (author) / Qi, Li (author) / Chen, Pan (author) / Xue, Gengjian (author) / Mei, Lin (author)


    Publication date :

    2015-10-01


    Size :

    1679089 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    A Multilayer Pyramid Network Based on Learning for Vehicle Logo Recognition

    Yu, Ye / Li, Hua / Wang, Jun et al. | IEEE | 2021

    Free access

    Color Image Coding Using Wavelet Pyramid Coders

    Mitra, S. / Long, R. / Muyshondt, R. et al. | British Library Conference Proceedings | 1996


    RVDNet: Rotated Vehicle Detection Network with Mixed Spatial Pyramid Pooling for Accurate Localization

    Zhou, Jianhong / Liang, Zhangzhao / Tan, Zijun et al. | Springer Verlag | 2024


    Spatial Multi-object Recognition Based on Deep Learning

    Liu, Wang / Xiao, Hewen / Chengchao, Bai | IEEE | 2019