Research on traffic sign recognition algorithms for autonomous vehicles in smart city traffic systems

Chuanfei Wang

doi:10.1117/12.3037841

12 September 2024 Research on traffic sign recognition algorithms for autonomous vehicles in smart city traffic systems

Chuanfei Wang

Author Affiliations +

Proceedings Volume 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024); 1325619 (2024) https://doi.org/10.1117/12.3037841
Event: Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 2024, Anshan, China

Abstract

With the continuous advancement of smart city construction, autonomous driving technology is playing an increasingly important role in urban traffic systems. This study aims to explore the development and optimization of traffic sign recognition algorithms for autonomous vehicles in smart city traffic environments. By comprehensively analyzing current traffic sign recognition technologies, this paper proposes a traffic sign recognition system based on the YOLOv5 algorithm and utilizes the open-source COCO dataset for model training and testing. The images were preprocessed and annotated, employing CSPDarknet53 as the backbone network, which effectively extracts image features through multiple convolutional layers and residual blocks. A deep learning model was trained, capable of recognizing and classifying various traffic signs such as stop, speed limit, and turn signs. The research results indicate that the model demonstrates high average precision (AP) on the COCO dataset, effectively identifying traffic signs of different sizes and angles, even in complex backgrounds, with high robustness. Compared to traditional methods, the recognition accuracy has improved by 15%, and it has a significant advantage in real-time processing.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chuanfei Wang "Research on traffic sign recognition algorithms for autonomous vehicles in smart city traffic systems", Proc. SPIE 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 1325619 (12 September 2024); https://doi.org/10.1117/12.3037841

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KEYWORDS

Object detection

Detection and tracking algorithms

Unmanned vehicles

Performance modeling

Feature extraction

Image processing

Data modeling

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