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License plate detection and recognition using hierarchical feature layers from CNN

  • Qiang Lu
  • Yu Liu
  • Jing Huang
  • Xiaohui Yuan
  • Qingxin Hu
Article
  • 18 Downloads

Abstract

In recent years, a variety of systems using deep convolutional neural network (CNN) approaches have achieved good performance on license plate detection and character recognition. However, most of these systems are not stable when the scenes changed, specification of each hierarchical layer to get the final detection result, which can detect multi-scale license plates from an input image. Meanwhile, at the stage of character recognition, data annotation is heavy and time-consuming, giving rise to a large burden on training a better model. We devise an algorithm to generate annotated training data automatically and approximate the data from the real scenes. Our system used for detecting license plate achieves 99.99% mean average precision (mAP) on OpenITS datasets. Character recognition also sees high accuracy, thus verifying the superiority of our method.

Keywords

License plate detection Character recognition Hierarchical feature layers Generated training data 

Notes

Acknowledgements

This work is supported by the National Nature Science Foundation of China (No. 61672201), the Natural Science Foundation of Anhui Province of China (No. 1708085MF158) and the China Scholarship Council (CSC No.201706695044).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Qiang Lu
    • 1
  • Yu Liu
    • 2
  • Jing Huang
    • 2
  • Xiaohui Yuan
    • 3
  • Qingxin Hu
    • 2
  1. 1.School of Computer and Information, Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei Engineering Research Center of Electric Power Data ApplicationHefei University of TechnologyHefeiChina
  2. 2.School of Computer and InformationHefei University of TechnologyHefeiChina
  3. 3.Department of Science and EngineeringUniversity of North TexasDentonUSA

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