Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Series Vol. 52 , 27 March 2024


Open Access | Article

Unleashing the power of Convolutional Neural Networks in license plate recognition and beyond

Maolin Wang * 1
1 Shanghai Maritime University

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 52, 69-75
Published 27 March 2024. © 27 March 2024 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Maolin Wang. Unleashing the power of Convolutional Neural Networks in license plate recognition and beyond. ACE (2024) Vol. 52: 69-75. DOI: 10.54254/2755-2721/52/20241252.

Abstract

This article explores the application of Convolutional Neural Networks (CNN) in the field of license plate recognition. It begins by introducing the architecture of CNN, which consists of three key layers: Convolutional Layers, Pooling Layers, and Fully Connected Layers. The article then references three relevant papers that demonstrate how CNNs are applied in license plate recognition. The first paper utilizes TensorFlow to construct a CNN model and integrates it with an STM32MP157 embedded chip for license plate recognition. The second paper presents a real-time car license plate detection and recognition method called Multi-Task Light CNN, emphasizing robustness. The third paper employs the ResNet+FPN feature extraction network of the Mask R-CNN model and annotates a license plate dataset. The article highlights the promising future of CNNs in various fields beyond license plate recognition, emphasizing their potential for further development and industrial applications. CNNs have proven to be versatile and powerful tools in computer vision, offering solutions to a wide range of problems. Their adaptability and effectiveness make them a key player in the ongoing advancement of artificial intelligence and automation technologies.

Keywords

CNN, Deep Learning, License Plate Recognition

References

1. Zherzdev S, Gruzdev A. Lprnet: License plate recognition via deep neural networks. arXiv preprint arXiv:1806.10447, 2018.

2. Li H, Wang P, Shen C. Toward end-to-end car license plate detection and recognition with deep neural networks. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(3): 1126-1136.

3. Montazzolli S, Jung C. Real-time brazilian license plate detection and recognition using deep convolutional neural networks. 2017 30th SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE, 2017: 55-62.

4. Masood S Z, Shu G, Dehghan A, et al. License plate detection and recognition using deeply learned convolutional neural networks. arXiv preprint arXiv:1703.07330, 2017.

5. Silva S M, Jung C R. Real-time license plate detection and recognition using deep convolutional neural networks. Journal of Visual Communication and Image Representation, 2020, 71: 102773.

6. Lin C H, Lin Y S, Liu W C. An efficient license plate recognition system using convolution neural networks. 2018 IEEE International Conference on Applied System Invention (ICASI). IEEE, 2018: 224-227.

7. Kurpiel F D, Minetto R, Nassu B T. Convolutional neural networks for license plate detection in images. 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017: 3395-3399.

8. Peng P. Research on license plate recognition based on CNN Convolutional neural network. Shandong University, 2020.

9. Wang W, Yang J, Chen M, et al. A light CNN for end-to-end car license plates detection and recognition. IEEE Access, 2019, 7: 173875-173883.

10. Zeng Jianxing. Research on license plate recognition based on convolutional neural network. Shandong University of Science and Technology, 2020.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 4th International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-83558-349-4
ISBN (Online)
978-1-83558-350-0
Published Date
27 March 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/52/20241252
Copyright
27 March 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated