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19 July 2024 On the licence plate recognition algorithm based on image correction and improvement SE-LPRNet
Haiying Qi, Quanyan Gao, Yaqin Lin, Wei Shao
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Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131R (2024) https://doi.org/10.1117/12.3035217
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China
Abstract
Localisation detection and recognition of licence plate plays an important role in road traffic environment. In order to improve the intelligent traffic system and solve the problem of low recognition rate due to tilted licence plate numbers, a licence plate recognition method based on image rectification and improved LPRNet is proposed, and a Chinese parking dataset is used for training and testing. Firstly, the licence plate image is greyscaled, the edges are extracted by Canny operator, the tilt angle is obtained by Radon transform, and the tilted licence plate is cut after tilt correction; then it is recognized based on the model SE-LPRNet. The results show that the average recognition accuracy of the licence plate recognition model is more than 98%, and the proposed method shows strong robustness in the scene of tilted licence plate, and there is also an obvious advantage in recognition speed.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Haiying Qi, Quanyan Gao, Yaqin Lin, and Wei Shao "On the licence plate recognition algorithm based on image correction and improvement SE-LPRNet", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131R (19 July 2024); https://doi.org/10.1117/12.3035217
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KEYWORDS
Neural networks
Image segmentation
Convolution
Image processing
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