Automatic License Plate Recognition: An Efficient and Layout-Independent Approach Based on the YOLO Detector

  • Rayson Laroca UFPR
  • David Menotti UFPR

Resumo


Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications, such as border control and traffic law enforcement. This work presents an efficient, robust and layout-independent ALPR system based on the YOLO object detector that contains a unified approach for license plate detection and layout classification and leverages post-processing rules in the recognition stage to eliminate a major shortcoming of existing ALPR systems (being layout dependent). We also introduce a publicly available dataset for ALPR that has become very popular, having been downloaded more than 550 times by researchers from 76 different countries in the last year alone. The proposed system, which performs in real time even when there are 4 vehicles in the scene, outperformed both previous works and commercial systems on four public datasets widely used in the literature.

Palavras-chave: Automatic License Plate Recognition, Convolutional Neural Networks, YOLO

Referências

Gonçalves, G. R., Diniz, M. A., Laroca, R., Menotti, D., and Schwartz, W. R. (2018). Real-time automatic license plate recognition through deep multi-task networks. In Conference on Graphics, Patterns and Images (SIBGRAPI), pages 110-117.

Gonçalves, G. R., Diniz, M. A., Laroca, R., Menotti, D., and Schwartz, W. R. (2019). Multi-task learning for low-resolution license plate recognition. In Iberoamerican Congress on Pattern Recognition (CIARP), pages 251-261.

Laroca, R., Barroso, V., Diniz, M. A., Gonçalves, G. R., Schwartz, W. R., and Menotti, D. (2019a). Convolutional neural networks for automatic meter reading. Journal of Electronic Imaging, 28(1):013023.

Laroca, R., Severo, E., Zanlorensi, L. A., Oliveira, L. S., Gonçalves, G. R., Schwartz, W. R., and Menotti, D. (2018). A robust real-time automatic license plate recognition based on the YOLO detector. In International Joint Conference on Neural Networks (IJCNN), pages 1-10.

Laroca, R., Zanlorensi, L. A., Gonçalves, G. R., Todt, E., Schwartz, W. R., and Menotti, D. (2019b). An efficient and layout-independent automatic license plate recognition system based on the YOLO detector. arXiv:1909.01754. submitted to IET Intelligent Transport Systems (provisionally accepted subject to major revisions).

Oliveira, I. O., Laroca, R., Menotti, D., Fonseca, K. V. O., and Minetto, R. (2019). Vehicle re-identification: exploring feature fusion using multi-stream convolutional networks. arXiv:1911.05541. submitted to IEEE Trans. on Intelligent Transportation Systems.

Panahi, R. and Gholampour, I. (2017). Accurate detection and recognition of dirty vehicle plate numbers for high-speed applications. IEEE Transactions on Intelligent Transportation Systems (ITS), 18(4):767-779.

Silva, S. M. and Jung, C. R. (2017). Real-time brazilian license plate detection and recognition using deep convolutional neural networks. In SIBGRAPI, pages 55-62.

Silva, S. M. and Jung, C. R. (2018). License plate detection and recognition in unconstrained scenarios. In European Conference on Computer Vision, pages 593-609.

Zhuang, J., Hou, S., Wang, Z., and Zha, Z. (2018). Towards human-level license plate recognition. In European Conference on Computer Vision (ECCV), pages 314-329.
Publicado
30/06/2020
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LAROCA, Rayson; MENOTTI, David. Automatic License Plate Recognition: An Efficient and Layout-Independent Approach Based on the YOLO Detector. In: CONCURSO DE TESES E DISSERTAÇÕES (CTD), 33. , 2020, Cuiabá. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 73-78. ISSN 2763-8820. DOI: https://doi.org/10.5753/ctd.2020.11372.