|Table of Contents|

Research of License Plate Recognition Method Based on Parallel CNN and Optimization Strategies(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2023年03期
Page:
98-104
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Research of License Plate Recognition Method Based on Parallel CNN and Optimization Strategies
Author(s):
Wu Zhongping1; Liu Xinchang1; Hao Zongbo2
(1.School of Automobile and Transportation,Chengdu Institute of Technology,Chengdu 611730,China)
(2.School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China)
Keywords:
license plate detection;  optimization strategies;  character recognition;  convolutional neural network;  intelligent transportation system
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2023.03.013
Abstract:
To improve the generalization of the automatic license plate recognition system,based on the general convolutional neural network(CNN),a CNN with two shallow independent sub-networks is proposed,which has the function of parallel convolutional calculation,one is used to infer the probability of license plate,and the other uses linear activation to regress affine parameters. The proposed method supports the detection and recognition of different types of vehicle license plates such as cars(including buses and trucks)and motorcycles. In addition,the license plate character recognition module based on YOLO v3 is used and a series of optimization strategies are applied to accurately read the Chinese characters in the license plate. The experimental results show that the recognition accuracy of the proposed method is better than other advanced methods,and the license plate detection on AOLP data set achieved a test accuracy of 98.9%,and the character recognition accuracy on CLPD data set is 96.2%. The proposed method is helpful to promote the further development of intelligent transportation system.

References:

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Last Update: 2023-09-15