Traffic Sign Recognition Algorithm Based on Improved Residual Network

Abstract

Abstract: For the problems of high-level information loss and insufficient feature extraction in sampling in network structure, the ResNet network structure is improved and a traffic sign recognition method based on multi-scale features and attention mechanism is put forward in this paper. Firstly, multi-scale features are used to fuse different levels of feature information to enrich feature semantic information and enhance the ability of feature extraction. Then, the features of different channels are strengthened through the attention mechanism to improve the overall presence of traffic signs for achieving more accurate traffic sign recognition. The experimental results on GTSRB and BelgiumTS traffic sign datasets show that the accuracies with the proposed methods reach 99.31% and 98.96% respectively, which achieves better results in traffic sign recognition.

Key words: deep learning, traffic sign recognition, convolutional neural network, multi-scale feature fusion, channel attention mechanism

LIANG Zheng-you, GENG Jing-bang, SUN Yu. Traffic Sign Recognition Algorithm Based on Improved Residual Network[J]. Computer and Modernization, 2022, 0(04): 52-57.

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