An Improved LeNet-5 Convolutional Neural Network for Intelligent Recognition of License Plate Images

被引：0

作者：

Li J. ^{[1
,2
]}

Cheng C. ^{[2
]}

机构：

[1] College of Information Engineering, Henan University of Science and Technology, Henan, Luoyang

[2] College of Information Engineering, Henan Mechanical and Electrical Vocational College, Henan, Zhengzhou

来源：

IEIE Transactions on Smart Processing and Computing | 2023年 / 12卷 / 05期

关键词：

Convolutional neural network; Image recognition; Inception-SE; License plate recognition;

D O I：

10.5573/IEIESPC.2023.12.5.428

中图分类号：

学科分类号：

摘要：

In intelligent transportation systems, accurate license plate recognition is an important component. This paper briefly introduces the LeNet-5 model for license plate image recognition. We improved the model by introducing an inception-SE convolution module. In simulation experiments, the optimized LeNet-5 model was compared with the original LeNet-5 model and a back-propagation neural network (BPNN). The results showed that the characters after preprocessing and character segmentation were clearer than those in the original images. During training, the optimized LeNet-5 converged the fastest, reached stability after 100 iterations, and had the smallest error after stability. The overall recognition accuracy of the BPNN model for the license images was 64.3%. For the original LeNet-5 model, it was 84.0%, and for the optimized LeNet-5 model, it was 98.6%. © 2023 Institute of Electronics and Information Engineers. All rights reserved.

引用

页码：428 / 433

页数：5

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