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Automatic recognition algorithm of traffic signs based on convolution neural network

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Abstract

Because of the hierarchical significance of traffic sign images, the traditional methods do not effectively control and extract the brightness and features of layered images. Therefore, an automatic recognition algorithm for traffic signs based on a convolution neural network is proposed in this paper. First, the histogram equalization method is used to pre-process the traffic sign images, with details of the images being enhanced and contrast of the images improved. Then, the traffic sign images are recognized by a convolution neural network and the large scale structure of information in the traffic sign images are obtained by using a hierarchical significance detection method based on graphical models. Next, the area of interest in the traffic sign images are extracted by using the hierarchical significance model. Finally, the Softmax classifier is selected to classify the input feature images to realize the automatic recognition of traffic signs. Experimental results show that the proposed algorithm can control the brightness of traffic sign images, which can accurately extract image regions of interest and complete the automatic recognition of traffic signs.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Xinyang Vocational and Technical College, Xinyang, 464000, China

    Hao Xu

  2. Department of Mathematics & Computer Science, Brandon University, Brandon, R7A6A9, Canada

    Gautam Srivastava

  3. Research Center for Interneural Computing, China Medical University, Taichung, Taiwan, 40402, Republic of China

    Gautam Srivastava

Authors
  1. Hao Xu
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  2. Gautam Srivastava
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Correspondence to Gautam Srivastava.

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Xu, H., Srivastava, G. Automatic recognition algorithm of traffic signs based on convolution neural network. Multimed Tools Appl 79, 11551–11565 (2020). https://doi.org/10.1007/s11042-019-08239-z

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  • DOI: https://doi.org/10.1007/s11042-019-08239-z

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