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Traffic Sign Recognition Based on Improved VGG-16 Model

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Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14087))

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Abstract

Traffic sign recognition technology is very important in intelligent transportation systems. Aiming at the problem that the imbalance of existing traffic sign data sets affects the recognition accuracy. Firstly, this paper introduces the Weighted-Hybrid loss function in VGG-16 to enhance the feature extraction ability of the model. The model can reduce the contribution of easy-to-classify samples to the decline of the loss function. Then, we introduce the HDC-VGG lightweight model to ensure the accuracy of model recognition on the basis of reducing model parameters. Finally, the experiment results show that the recognition accuracy of the proposed model can reach 98.2%.

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China under Grant No. 2020YFB1808004, and in part by Jiangsu Key Research and Development Program under Grant No. BE2021013-2.

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Authors and Affiliations

  1. State Key Lab of Processors, Institute of Computing Technology, CAS, Beijing, 100190, China

    Tang Shuyuan, Li Jintao & Liu Chang

  2. Beijing Key laboratory of Mobile Computing and Pervasive Device, Beijing, 100190, China

    Tang Shuyuan, Li Jintao & Liu Chang

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Tang Shuyuan & Li Jintao

Authors
  1. Tang Shuyuan
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  2. Li Jintao
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  3. Liu Chang
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Corresponding author

Correspondence to Tang Shuyuan .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Shuyuan, T., Jintao, L., Chang, L. (2023). Traffic Sign Recognition Based on Improved VGG-16 Model. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_56

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  • DOI: https://doi.org/10.1007/978-981-99-4742-3_56

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4741-6

  • Online ISBN: 978-981-99-4742-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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