RT-LPDRnet: A Real-Time License Plate Detection and Recognition Network
Authors: 
Haijie Wang, Yanjie Ke, Ge YangAuthors Info & Claims
Pages 121 - 126
Abstract
Benefiting from the rapid development of deep learning, the accuracy of license plate (LP) detection and recognition has been greatly developed. However, the low computing power of embedded devices is difficult to complete this task in real-time. To tackle this problem, we propose a real-time license plate detection and recognition network in this paper. The detection head we designed can detect the bounding box and four corner points of the license plate, and then we apply the ROIAlign method to extract the features on the same backbone in order to perform license plate recognition. The resulting architecture, called RT-LPDRnet, outperforms all the SOTA methods on the large-scale license plate data set Chinese City Parking Dataset (CCPD), meanwhile with faster inference time than recent methods. Our code will be publicly available.
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- RT-LPDRnet: A Real-Time License Plate Detection and Recognition Network
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Published In
March 2021
246 pages
ISBN:9781450388634
DOI:10.1145/3461353
Copyright © 2021 ACM.
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Published: 04 September 2021
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ICIAI 2021
ICIAI 2021: 2021 the 5th International Conference on Innovation in Artificial Intelligence
March 5 - 8, 2021
Xia men, China
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