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Traffic signs detection and recognition systems by light-weight multi-stage network

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Abstract

Traffic sign detection and recognition (TSDR) plays an important role in the fields for assistant driving, autonomous vehicle and so on. However, due to the complexity of real driving scene and variety of traffic signs, many challenging problems occurred, such as inaccurate color segmentation and time consumption of recognition algorithm based on deep learning. This paper describes an approach for efficiently detection and recognizing in real world scenarios. First of all, a traffic sign region of interest extraction algorithm based on multi-color space is proposed, the fusion future of HSV and RGB color space can obtain better color segmentation for the SVM classifier. Next a novel multi-scale two-stage lightweight network (MSTSN) is investigated, which adopts a coarse-to-fine strategy to improve recognition accuracy. Specially, the candidate Region of Interests (ROIs) are fed into a binary classification layer and only positive ones are further classified with multi-class classification network. The deeply separable convolution, residual structure and feature enhancement module is the bottleneck of MSTSN, which obtains more discriminative features and meets requirement for real-time performance. The experimental results successfully demonstrate effectiveness of our method.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. U1833115).

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, China

    Mingzheng Hou, Xin Zhang, Yang Chen, Penglin Dong & Ziliang Feng

  2. National Key Laboratory Of Fundamental Science On Synthetic Vision, Sichuan University, Chengdu, China

    Mingzheng Hou, Xin Zhang, Yang Chen, Penglin Dong & Ziliang Feng

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  1. Mingzheng Hou
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  2. Xin Zhang
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  3. Yang Chen
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  4. Penglin Dong
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Correspondence to Xin Zhang.

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Mingzheng Hou and Xin Zhang have contributed equally to this paper.

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Hou, M., Zhang, X., Chen, Y. et al. Traffic signs detection and recognition systems by light-weight multi-stage network. Multimed Tools Appl 81, 16155–16169 (2022). https://doi.org/10.1007/s11042-022-12201-x

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