Traffic sign detection based on deep learning

Zhaoyang Xie; Peilin Liu; Taijun Li

doi:10.1117/12.2684598

1 August 2023 Traffic sign detection based on deep learning

Zhaoyang Xie, Peilin Liu, Taijun Li

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127542Z (2023) https://doi.org/10.1117/12.2684598
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

With the popularity of vehicles, traffic safety issues are becoming more and more important. In the field of assisted driving and intelligent driving, traffic sign detection is particularly important, which can provide adequate response time for drivers and indirectly protect the safety of people's lives and property. At present, although the development of traffic sign detection algorithms has been relatively mature, there is still some room for improvement. For example, the detection accuracy of traffic signs is not high enough, which is easy to cause false detection and missing detection of signs. Therefore, this paper proposes a traffic sign detection algorithm based on deep learning, which is based on yolov5 model and embedded with SE attention mechanism. transformer module is introduced into the backbone network framework to improve the feature extraction capability of the network. Finally, the weighted BIFPN module is used in the feature fusion network. Experiments show that the improved algorithm proposed in this paper has a good performance in the Chinese traffic sign data set, with the average accuracy of multiple categories increased by 3.6%. The detection accuracy has been greatly improved.

Citation Download Citation

Zhaoyang Xie, Peilin Liu, and Taijun Li "Traffic sign detection based on deep learning", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127542Z (1 August 2023); https://doi.org/10.1117/12.2684598

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KEYWORDS

Detection and tracking algorithms

Object detection

Transformers

Deep learning

Feature fusion

Feature extraction

Safety

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