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Real-time traffic sign detection and classification towards real traffic scene

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Abstract

In this paper we propose a real-time traffic sign recognition algorithm which is robust to the small-sized objects and can identify all traffic sign categories. Specifically, we present a two-level detection framework which consists of the region proposal module(RPM) which is responsible for locating the objects and the classification module(CM) which aims to classify the located objects. In addition, to solve the problem of insufficient samples, we present an effective data augmentation method based on traffic sign logo to generate enough training data. The experiments are conducted in TT100k, and the results show the superiority of our method.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No.61672215), National Key R&D Program of China (No. 2018YFB1308604) and Hunan Science and Technology Innovation Project (No. 2017XK2102).

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering Hunan University, Changsha, China

    Yiqiang Wu, Zhiyong Li, Ying Chen, Ke Nai & Jin Yuan

  2. Key Laboratory for Embedded and Network Computing of Hunan Province, Changsha, China

    Yiqiang Wu, Zhiyong Li, Ying Chen, Ke Nai & Jin Yuan

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  1. Yiqiang Wu
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  2. Zhiyong Li
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  3. Ying Chen
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  4. Ke Nai
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  5. Jin Yuan
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Correspondence to Zhiyong Li.

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Wu, Y., Li, Z., Chen, Y. et al. Real-time traffic sign detection and classification towards real traffic scene. Multimed Tools Appl 79, 18201–18219 (2020). https://doi.org/10.1007/s11042-020-08722-y

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