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LPSRGAN : Generative Adversarial Networks for Super-Resolution of License Plate Image
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Pan, Yuecheng, Tang, Jin and Tjahjadi, Tardi (2024) LPSRGAN : Generative Adversarial Networks for Super-Resolution of License Plate Image. Neurocomputing . 127426. doi:10.1016/j.neucom.2024.127426 ISSN 0925-2312. (In Press)
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Official URL: https://doi.org/10.1016/j.neucom.2024.127426
Abstract
This paper proposes a super-resolution algorithm for reconstructing license plate images based on generative adversarial networks (GAN) for improving the recognition rate of low-resolution license plate images. To this end, this paper first designs a new image degradation model, called the n-stage random combination degradation model (n-RCD), which extends the traditional degradation model horizontally and vertically to better simulate the features of low-resolution license plate images in natural scenes. Subsequently, to address the problem that existing GAN models cannot adapt to the complex degradation space in natural scenes, this paper proposes a new network structure called LPSRGAN, which optimizes the SRGAN model from the generator and discriminator to improve its super-resolution capability. In addition, this paper also proposes a perceptual optical character recognitioin (OCR) loss for license plate images, which defines the connectionist temporal classification (CTC) loss of the output of the OCR network with character label values to better preserve the character details and features of the license plate images. Finally, the experiments reported in this paper show that the proposed algorithm improves the recognition rate of the original low-resolution license plate image by 12.48%, and the recognition rate of the reconstructed image reaches 93.90%. Based on this, this paper also tests the algorithm on actual captured data, and the results show that it performs well in natural scenes, demonstrating the algorithm has broad application prospects.
Item Type: | Journal Article | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
Journal or Publication Title: | Neurocomputing | ||||||
Publisher: | Elsevier | ||||||
ISSN: | 0925-2312 | ||||||
Official Date: | 21 February 2024 | ||||||
Dates: |
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Article Number: | 127426 | ||||||
DOI: | 10.1016/j.neucom.2024.127426 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | In Press | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 21 February 2024 | ||||||
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