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Robust License Plate Detection Through Auxiliary Information and Context Fusion Model

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Pattern Recognition and Computer Vision (PRCV 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11859))

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Abstract

License plate detection has wide applications in the intelligent transportation system, while it still remains challenges to improve the robustness under various shooting distance and observation angles. To get better performance, a novel convolutional-neural-network-based method is proposed, which is achieved with auxiliary information and context fusion model. First, the auxiliary information is employed in our framework, which corresponds with resolutions, orientations and shapes of license plates. Specifically, the multiple resolutions are collected through integrating multi-level features of convolution hierarchy. Besides the various scales and ratios, the region proposal network (RPN) with multi-angle anchors and branching structure is applied to generate proper proposals. Second, an effective context fusion model is designed to fully exploit the hidden correlation between license plates and contextual properties. The local and contextual features are independently learned in the dual pathways, which are later joint to form a powerful representation in subsequent layers. Comprehensive experiments on the publicly available datasets confirm the effectiveness of the proposed method.

This is a student paper. This work was partially supported by the National Natural Science Foundation of China under Grant 61702278.

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Author information

Authors and Affiliations

  1. Jiangsu Province Key Lab on Image Processing and Image Communications, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Ning Wang, Feng Liu & Zongliang Gan

Authors
  1. Ning Wang
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  2. Feng Liu
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  3. Zongliang Gan
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Corresponding author

Correspondence to Feng Liu .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  4. Xidian University, Xi’an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi’an Jiaotong University, Xi’an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi’an, China

    Yanning Zhang

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Wang, N., Liu, F., Gan, Z. (2019). Robust License Plate Detection Through Auxiliary Information and Context Fusion Model. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_26

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  • DOI: https://doi.org/10.1007/978-3-030-31726-3_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31725-6

  • Online ISBN: 978-3-030-31726-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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