Technological Evolution of Traffic Sign Recognition

Xiaorui Chen

doi:10.54254/2755-2721/2025.AST26571

Applied and Computational EngineeringOpen access

Technological Evolution of Traffic Sign Recognition

Research Article

Open Access

Technological Evolution of Traffic Sign Recognition

Xiaorui Chen ^1*

¹ Institute of Mechanical Engineering, Southwest Jiaotong University, Xi'an Road, Chengdu, China

^*Corresponding author: yrbcxcn@163.com

Published on 9 September 2025

ACE Vol.185

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-369-7

ISBN (Online): 978-1-80590-370-3

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Abstract

This paper aims to introduce deep learning approaches developed to overcome challenges in traffic sign recognition, which include YOLOv8-TS, STD-YOLOv5s, Semantic Scene Understanding & Structured Localization, and Cascade R-CNN with Multi-Scale Attention. The paper also delves into critical system architecture considerations, analyzing the roles of various sensors and computing platforms in enabling real-time and efficient TSR. Performance evaluation is discussed using essential metrics such as Precision, Recall, F1-score, and mean Average Precision (mAP), highlighting their importance in quantifying detection and classification accuracy. Optimization strategies are emphasized as crucial for practical deployment, relying heavily on continuous algorithmic advances (like novel network architectures and attention modules) and sophisticated hardware-software co-design to balance computational efficiency with recognition performance. Looking ahead, TSR research is poised to advance significantly in key directions, including multi-modal data fusion for enhanced environmental perception, extreme lightweighting and real-time optimization for edge deployment, achieving cross-regional and cross-language generality, developing robust adversarial defense and safety mechanisms, improving model interpretability, and fostering deeper integration with driving decision systems. These advancements are vital for enhancing the reliability and safety of autonomous vehicles operating in complex, real-world driving environments.

Keywords:

deep learning, traffic sign recognition, real-time detection

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References

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References

[4]. Wang, C., & Fu, Z. A. (2018).Traffic sign detection based on YOLO v2 model. Journal of Computer Applications(S2), 276-278. doi: CNKI: SUN: JSJY.0.2018-S2-059.

[12]. Xie, B. Q., & Weng, X. X. (2019).Real-Time Embedded Traffic Sign Recognition Using Efficient Convolutional Neural Network.. IEEE Access.

[13]. Muhe DING. (2024, November 18). Accuracy, precision, recall, F-measure, mAP. CSDN Blog. Retrieved July 19, 2025, from https: //blog.csdn.net/ding_programmer/article/details/89740668

[14]. Qingqian. (2025, January 5). Explanation of evaluation metrics for machine learning effectiveness. Zhihu. Retrieved July 19, 2025, from https: //zhuanlan.zhihu.com/p/16545421926

[15]. Fan, B. B., & Yang, H. (2021).Multi-scale traffic sign detection model with attention. Proceedings of the Institution of Mechanical Engineers(2-3). doi: 10.1177/0954407020950054.

[16]. Zeng, H. F. (2024).Research on Traffic Sign Detection and Recognition Algorithm Based on Improved YOLOv8. Computer Knowledge and Technology(30), 13-16. doi: 10.14004/j.cnki.ckt.2024.1589.

[20]. Shao, Y. X., Meng, W., Kong, D. Z., Han, L. X., & Liu, Y. (2020).Cross-modal Retrieval Method for Special Vehicles Based on Deep Learning. Computer Science(12), 205-209.

Cite this article

Chen,X. (2025). Technological Evolution of Traffic Sign Recognition. Applied and Computational Engineering,185,1-10.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-CDS 2025 Symposium: Application of Machine Learning in Engineering

ISBN: 978-1-80590-369-7(Print) / 978-1-80590-370-3(Online)

Editor: Marwan Omar, Mian Umer Shafiq

Conference website: https://www.confcds.org

Conference date: 19 August 2025

Series: Applied and Computational Engineering

Volume number: Vol.185

ISSN: 2755-2721(Print) / 2755-273X(Online)

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