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

Maolin Wang

doi:10.54254/2755-2721/52/20241252

Research Article

Open access

Published on 27 March 2024

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Wang,M. (2024). Unleashing the power of Convolutional Neural Networks in license plate recognition and beyond. Applied and Computational Engineering,52,69-75.

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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.

https://doi.org/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

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References

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Cite this article

Wang,M. (2024). Unleashing the power of Convolutional Neural Networks in license plate recognition and beyond. Applied and Computational Engineering,52,69-75.

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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About volume

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

Conference website: https://www.confspml.org/

ISBN：978-1-83558-349-4(Print) / 978-1-83558-350-0(Online)

Conference date: 15 January 2024

Editor：Marwan Omar

Series: Applied and Computational Engineering

Volume number: Vol.52

ISSN：2755-2721(Print) / 2755-273X(Online)

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