Citation

Wei W, Zhang L, Yang K, Li J, Cui N, Han Y, Zhang N, Yang X, Tan H, Wang K. Heliyon 2024; 10(4): e26182.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.heliyon.2024.e26182

PMID

38420439

PMCID

PMC10900943

Abstract

Traffic sign recognition is an important part of intelligent transportation system. It uses computer vision and traffic sign recognition technology to detect and recognize traffic signs on the road automatically. In this paper, we propose a lightweight model for traffic sign recognition based on convolutional neural networks called ConvNeSe. Firstly, the feature extraction module of the model is constructed using the Depthwise Separable Convolution and Inverted Residuals structures. The model extracts multi-scale features with strong representation ability by optimizing the structure of convolutional neural networks and fusing of features. Then, the model introduces Squeeze and Excitation Block (SE Block) to improve the attention to important features, which can capture key information of traffic sign images. Finally, the accuracy of the model in the German Traffic Sign Recognition Benchmark Database (GTSRB) is 99.85%. At the same time, the model has good robustness according to the results of ablation experiments.

Language: en

Keywords

Attention mechanism; ConvNeSe; Lightweight; Multi-scale feature; Traffic sign recognition