Traffic sign recognition based on improved VGG16 algorithm

Jianxia Wang; Shan Jiang; Wanzhen Zhou

doi:10.1117/12.2684173

1 August 2023 Traffic sign recognition based on improved VGG16 algorithm

Jianxia Wang, Shan Jiang, Wanzhen Zhou

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127543O (2023) https://doi.org/10.1117/12.2684173
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

The detection and recognition of traffic signs acts an extremely crucial role in driverless technology and assisted driving systems. In view of the low recognition rate of traffic signs, this paper improves the neural network based on the existing VGG16 network architecture, including adding BN layer, adopting batch normalization algorithm and adding attention mechanism. The GTSRB data set is enhanced by scaling and random rotation. The data is preprocessed by image graying and image normalization. In this paper, the model recognition rate reached 99.34%, which is of great value to enhance the recognition accuracy of traffic signs and promote the development of intelligent transportation.

Citation Download Citation

Jianxia Wang, Shan Jiang, and Wanzhen Zhou "Traffic sign recognition based on improved VGG16 algorithm", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127543O (1 August 2023); https://doi.org/10.1117/12.2684173

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