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Learning to Detect License Plates Using Synthesized Data

  • Yanhui Pang
  • Wenzhong Wang
  • Aihua Zheng
  • Jin TangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11902)

Abstract

Due to the lack of large-scale license plate dataset, existing license plate detection methods are usually conducted on small and unrepresentative datasets. Therefore, the training of these models maybe insufficient and only sub-optimal results can be achieved. In this paper, we propose a simple but effective method to handle this issue by automatically synthesizing license plate images. Specifically, we utilize Blender as a modeling and rendering engine to simulate various environmental factors and create scenes with diverse vehicle models. With these created models, we can obtain massive training data by synthesizing unique license plate. The benefits of our proposed method are: (1) we cannot only automatically provide pixel-level bounding box annotation of license plate, but also avoid errors caused by manual labeling. (2) the introduced algorithm is more efficient than manual labelling and thus we can generate a large-scale dataset in a rather short term. Based on these synthesized data, we propose a dilated convolutional attention augmentation module in conventional deep license plate detection algorithm to further boost the final detection performance. Extensive experiments on two benchmarks validate the effectiveness of our proposed algorithm.

Keywords

Data rendering License plate detection Attention mechanism Dilated convolution 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61472002) and the Open Project Program of the National Laboratory of Pattern Recognition (NLPR)(No. 201900046).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yanhui Pang
    • 1
  • Wenzhong Wang
    • 1
  • Aihua Zheng
    • 1
  • Jin Tang
    • 1
    Email author
  1. 1.Anhui UniversityHefeiChina

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