Skip to main content
SpringerLink
Log in

Traffic Sign Recognition from Digital Images by Using Deep Learning

  • Conference paper
  • First Online:
Image and Video Technology (PSIVT 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13763))

Included in the following conference series:

Abstract

Traffic signs are essentially needed to obey the traffic rules. Once a driver ignores the signs, especially those critical signs, due to the complexity of actual traffic scenes or the influence of inclement weather conditions, it will lead to violating traffic regulations or traffic accidents, causing casualties and property losses. Therefore, Traffic Sign Recognition (TSR) is an essential part of autonomous vehicles and has important academic significance. The main contributions of this paper are as follows: (1) We apply an algorithm to the dark channel prior, and we also provide a guided image filtering algorithm for image defogging. Our results show that the guided image filtering method is very effective in image defogging. (2) This paper presents a number of deep learning solutions towards the aforementioned problems. Based on the experiments conducted, we discover that YOLOv5 is very suitable for real-time TSR.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Litman, T., Burwell, D.: Issues in sustainable transportation. Int. J. Global Environ. Issues 6(4), 331–347 (2010)

    Article  Google Scholar 

  2. Berkaya, S.K., Gunduz, H., Ozsen, O., Akinlar, C., Gunal, S.: On circular traffic sign detection and recognition. Expert Syst. Appl. 48, 67–75 (2016)

    Article  Google Scholar 

  3. Shi, X., Fang, X., Zhang, D., Guo, Z.: Image classification based on mixed deep learning model transfer learning. J. Syst. Simul. 28, 167 (2016)

    Google Scholar 

  4. Ma, X., Fu, A., Wang, H., Yin, B.: Hyperspectral image classification based on deep deconvolution network with skip architecture. IEEE Trans. Geosci. Remote Sens. 56, 4781–4791 (2018)

    Article  Google Scholar 

  5. Pan, C., Sun, M., Yan, Z., Shao, J., Wu, D., Xu, X.: Vehicle logo recognition based on deep learning architecture in video surveillance for intelligent traffic system. In: International Conference on Smart and Sustainable City (2013)

    Google Scholar 

  6. Garg, K., Nayar, S.K.: Detection and removal of rain from videos. In: IEEE CVPR (2004)

    Google Scholar 

  7. Li, B., Wang, S., Zheng, J., Zheng, L.: Single image haze removal using content-adaptive dark channel and post enhancement. IET Comput. Vis. 8(2), 131–140 (2014)

    Article  Google Scholar 

  8. Peng, J., Liu, B., Dong, W., Wang, J., Wang, Y.: Method of image enhancement based on multi-scale retinex. Laser Infrared 38(11), 1160–1163 (2008)

    Google Scholar 

  9. Nayar, S.K., Narasimhan, S.G.: Vision in bad weather. In: IEEE International Conference on Computer Vision (1999)

    Google Scholar 

  10. Huang, D., Huang, W., Gu, P., Liu, P., Luo, Y.: Image super-resolution reconstruction based on regularization technique and guided filter. Infrared Phys. Technol. 83, 103–113 (2017)

    Article  Google Scholar 

  11. Feng, X., Li, J., Hua, Z.: Low-light image enhancement algorithm based on an atmospheric physical model. Multimed. Tools Appl. 79(43–44), 32973–32997 (2020). https://doi.org/10.1007/s11042-020-09562-6

    Article  Google Scholar 

  12. Hubel, D.H., Weisel, T.N.: Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195(1), 215–243 (1968)

    Article  Google Scholar 

  13. Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)

    Book  MATH  Google Scholar 

  14. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)

    Article  Google Scholar 

  15. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)

    Google Scholar 

  16. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)

    Google Scholar 

  17. Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: IEEE/CVF CVPR, pp. 658–666 (2019)

    Google Scholar 

  18. Luo, Z., Nguyen, M., Yan, W.Q.: Sailboat detection based on automated search attention mechanism and deep learning models. In: International Conference on Image and Vision Computing New Zealand (IVCNZ), pp. 1–6 (2021)

    Google Scholar 

  19. Liu, Z., Yan, W.Q., Yang, M.L.: Image denoising based on a CNN model. In: International Conference on Control, Automation and Robotics (ICCAR), pp. 389–393 (2018)

    Google Scholar 

  20. Wang, X., Zhang, J., Yan, W.Q.: Gait recognition using multichannel convolution neural networks. Neural Comput. Appl. 32(18), 14275–14285 (2019). https://doi.org/10.1007/s00521-019-04524-y

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Auckland University of Technology, Auckland, 1010, New Zealand

    Jiawei Xing, Ziyuan Luo, Minh Nguyen & Wei Qi Yan

Authors
  1. Jiawei Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ziyuan Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Minh Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Qi Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Qi Yan .

Editor information

Editors and Affiliations

  1. Xiamen University Malaysia, Sepang, Malaysia

    Han Wang

  2. Singapore Institute of Manufacturing Technology, Singapore, Singapore

    Wei Lin

  3. Charles Sturt University, Bathurst, NSW, Australia

    Paul Manoranjan

  4. Minjiang University, Fuzhou, China

    Guobao Xiao

  5. Yau Lee Holdings Ltd., Hong Kong, Hong Kong

    Kap Luk Chan

  6. Tsinghua University, Beijing, China

    Xiaonan Wang

  7. Nanyang Technological University, Singapore, Singapore

    Guiju Ping

  8. Nanyang Technological University, Singapore, Singapore

    Haoge Jiang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xing, J., Luo, Z., Nguyen, M., Yan, W.Q. (2023). Traffic Sign Recognition from Digital Images by Using Deep Learning. In: Wang, H., et al. Image and Video Technology. PSIVT 2022. Lecture Notes in Computer Science, vol 13763. Springer, Cham. https://doi.org/10.1007/978-3-031-26431-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-26431-3_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26430-6

  • Online ISBN: 978-3-031-26431-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation