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Anchor-Free Location Refinement Network for Small License Plate Detection

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13537))

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Abstract

In the road scenarios, small-sized license plates are challenging to detect due to their small area. The direct detection methods based on anchors are not experts in detecting small-sized license plates due to foreground-background class imbalance. In this work, we propose an anchor-free location refinement network to alleviate the imbalance problem. First, we adopt the anchor-free mechanism to increase the proportion of foreground samples. Second, we use a coarse-to-fine strategy to reduce the background noises. Specifically, the whole network is combined with a coarse detection module (CDM) and a location refinement module (LRM), where the anchor-free CDM can approximately locate the local region around the license plate, and LRM can get a more accurate location of the license plate in the local region. The whole network can be trained in an end-to-end manner. Moreover, the proposed network can detect multi-directional license plates by regressing the four corners of the license plate in LRM. Extensive experiments verify that our method outperforms the baseline model by 6\(\%\) AP on the road license plate detection dataset and improves the recall of small-sized license plates by 7.5\(\%\), thus improving license plate recognition by 3.5\(\%\). Moreover, we improve the AP by 4.2\(\%\) on the traffic sign dataset TT100K, verifying its generalization ability.

Z.-J. Li and S.-L. Chen—Equal contribution.

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Author information

Authors and Affiliations

  1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

    Zhen-Jia Li, Song-Lu Chen, Qi Liu & Xu-Cheng Yin

  2. USTB-EEasyTech Joint Lab of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China

    Zhen-Jia Li, Song-Lu Chen, Qi Liu, Feng Chen & Xu-Cheng Yin

  3. EEasy Technology Company Ltd., Zhuhai, China

    Feng Chen

Authors
  1. Zhen-Jia Li
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  2. Song-Lu Chen
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  3. Qi Liu
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  4. Feng Chen
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  5. Xu-Cheng Yin
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Corresponding author

Correspondence to Xu-Cheng Yin .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

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

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

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

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Li, ZJ., Chen, SL., Liu, Q., Chen, F., Yin, XC. (2022). Anchor-Free Location Refinement Network for Small License Plate Detection. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_41

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  • DOI: https://doi.org/10.1007/978-3-031-18916-6_41

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  • Print ISBN: 978-3-031-18915-9

  • Online ISBN: 978-3-031-18916-6

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