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Recognition Optimization of License Plate Targets Based on Improved Neural Network Model

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Abstract

With the continuous development of social economy, private cars have become more and more, and the traffic pressure is also increasing. To more accurately recognize vehicles violating traffic rules, the problem of recognition and optimization of license plate targets has become an urgent task with certain practical significance and guiding significance. In this paper, the license plate recognition (LPR) system is taken as the main research object, which is improved and optimized. Firstly, an improved neural network model is established by applying the improved convolutional neural network (CNN) algorithm to the license plate recognition system and based on the improved activation function, and the relevant experimental results are obtained; meanwhile compared with the traditional CNN model, the corresponding experimental results are obtained; finally, the experimental conclusions are obtained, i.e., the LPR system using improved CNN algorithm has better performance in recognizing license plate target and can more accurately recognize licence plates which are shielded. Therefore, the improved neural network model has great development potential in the application of the LPR system.

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Acknowledgements

This study was supported by National Natural Science Foundation of China: Research of the key technology of large strain sensing realized by two-dimensional coaxial cable Bragg grating for structural health monitoring (grant number: 61701116).

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Authors and Affiliations

  1. Dongguan University of Technology, Electronics Building, No. 1, Daxue Road, Songshan Lake District, Dongguan, 523000, Guangdong, China

    Xiaomin Jiang, Yingxin Lai, Yue Song, Ping Yang & Shanjin Wang

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  1. Xiaomin Jiang
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  2. Yingxin Lai
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  3. Yue Song
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  4. Ping Yang
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  5. Shanjin Wang
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Correspondence to Xiaomin Jiang.

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Jiang, X., Lai, Y., Song, Y. et al. Recognition Optimization of License Plate Targets Based on Improved Neural Network Model. Int. J. ITS Res. 19, 92–98 (2021). https://doi.org/10.1007/s13177-020-00225-2

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  • DOI: https://doi.org/10.1007/s13177-020-00225-2

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