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Fast and robust road sign detection in driver assistance systems

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Abstract

Road sign detection plays a critical role in automatic driver assistance systems. Road signs possess a number of unique visual qualities in images due to their specific colors and symmetric shapes. In this paper, road signs are detected by a two-level hierarchical framework that considers both color and shape of the signs. To address the problem of low image contrast, we propose a new color visual saliency segmentation algorithm, which uses the ratios of enhanced and normalized color values to capture color information. To improve computation efficiency and reduce false alarm rate, we modify the fast radial symmetry transform (RST) algorithm, and propose to use an edge pairwise voting scheme to group feature points based on their underlying symmetry in the candidate regions. Experimental results on several benchmarking datasets demonstrate the superiority of our method over the state-of-the-arts on both efficiency and robustness.

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Acknowledgements

This research was partly supported by National Science Foundation, China (No. 61702226, 61672263, 21365008), the Natural Science Foundation of Jiangsu Province (Grant no. BK20170200, BK20161135), the Fundamental Research Funds for the Central Universities(JUSRP11854), the Natural Science Foundation of Hubei Province (No. 2018CFB538) and Hubei province high school outstanding young science and technology innovation team project (No.T201631).

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Authors and Affiliations

  1. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, 214122, China

    Tao Zhang

  2. School of Computer Science, Hubei University of Technology, Wuhan, 430068, China

    Jie Zou

  3. Department of Computer Science and Technology, Wuhan Technology and Business University, Wuhan, China

    Jie Zou

  4. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia

    Wenjing Jia

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  1. Tao Zhang
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  2. Jie Zou
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  3. Wenjing Jia
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Correspondence to Jie Zou.

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Zhang, T., Zou, J. & Jia, W. Fast and robust road sign detection in driver assistance systems. Appl Intell 48, 4113–4127 (2018). https://doi.org/10.1007/s10489-018-1199-x

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  • DOI: https://doi.org/10.1007/s10489-018-1199-x

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