Skip to main content
Springer Nature Link
Log in

Traffic-Sign Recognition Using Deep Learning

  • Conference paper
  • First Online:
Geometry and Vision (ISGV 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1386))

Included in the following conference series:

Abstract

Traffic-sign recognition (TSR) has been an essential part of driver-assistance systems, which is able to assist drivers in avoiding a vast number of potential hazards and improve the experience of driving. However, the TSR is a realistic task that is full of constraints, such as visual environment, physical damages, and partial occasions, etc. In order to deal with the constrains, convolutional neural networks (CNN) are accommodated to extract visual features of traffic signs and classify them into corresponding classes. In this project, we initially created a benchmark (NZ-Traffic-Sign 3K) for the traffic-sign recognition in New Zealand. In order to determine which deep learning models are the most suitable one for the TSR, we choose two kinds of models to conduct deep learning computations: Faster R-CNN and YOLOv5. According to the scores of various metrics, we summarized the pros and cons of the picked models for the TSR task.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Mogelmose, A., Trivedi, M., Moeslund, T.B.: Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans. Intell. Transp. Syst. 13(4), 1484–1497 (2012)

    Article  Google Scholar 

  2. Zhu, Y., Zhang, C., Zhou, D., Wang, X., Bai, X., Liu, W.: Traffic sign detection and recognition using fully convolutional network guided proposals. Neurocomputing 214, 758–766 (2016)

    Article  Google Scholar 

  3. Yang, Y., Luo, H., Xu, H., Wu, F.: Towards real-time traffic sign detection and classification. IEEE Trans. Intell. Transp. Syst. 17(7), 2022–2031 (2015)

    Article  Google Scholar 

  4. Zhang, J., Huang, M., Jin, X., Li, X.: A real-time Chinese traffic sign detection algorithm based on modified YOLOv2. Algorithms 10(4), 127 (2017)

    Article  MathSciNet  Google Scholar 

  5. Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw. 32, 323–332 (2012). https://doi.org/10.1016/j.neunet.2012.02.016

    Article  Google Scholar 

  6. Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German traffic sign recognition benchmark: a multi-class classification competition. In: International Joint Conference on Neural Networks (2011)

    Google Scholar 

  7. Larsson, F., Felsberg, M.: Using Fourier descriptors and spatial models for traffic sign recognition. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 238–249. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21227-7_23

    Chapter  Google Scholar 

  8. Wang, G., Ren, G., Quan, T.: A traffic sign detection method with high accuracy and efficiency. In: International Conference on Computer Science and Electronics Engineering (2013)

    Google Scholar 

  9. Sermanet, P., LeCun, Y.: Traffic sign recognition with multi-scale convolutional networks. In: International Joint Conference on Neural Networks (2011)

    Google Scholar 

  10. Mao, X., Hijazi, S., Casas, R., Kaul, P., Kumar, R., Rowen, C.: Hierarchical CNN for traffic sign recognition. In: IEEE Intelligent Vehicles Symposium (IV) (2016)

    Google Scholar 

  11. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE CVPR, pp. 779–788 (2016)

    Google Scholar 

  12. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: IEEE CVPR, pp. 7263–7271 (2017)

    Google Scholar 

  13. Girshick, R.: Fast R-CNN. In: IEEE ICCV, pp. 1440–1448 (2015)

    Google Scholar 

  14. Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: ICML (2013)

    Google Scholar 

  15. 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 Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  16. Yan, W.Q.: Computational Methods for Deep Learning - Theoretic. Practice and Applications. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-030-61081-4

    Book  MATH  Google Scholar 

  17. Yan, W.Q.: Introduction to Intelligent Surveillance - Surveillance Data Capture, Transmission, and Analytics, 3rd edn. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-319-60228-8

    Book  Google Scholar 

  18. Pan, C., Yan, W.Q.: Object detection based on saturation of visual perception. Multimed. Tools Appl. 79(27–28), 19925–19944 (2020). https://doi.org/10.1007/s11042-020-08866-x

    Article  Google Scholar 

  19. Pan, C., Li, X., Yan, W.: A learning-based positive feedback approach in salient object detection. In: IEEE IVCNZ (2018)

    Google Scholar 

  20. Liu, X., Yan, W., Kasabov, N.: Vehicle-related scene segmentation using CapsNets. In: IEEE IVCNZ (2020)

    Google Scholar 

  21. Liu, X., Neuyen, M., Yan, W.: Vehicle-related scene understanding using deep learning. In: Cree, M., Huang, F., Yuan, J., Yan, W.Q. (eds.) ACPR 2019. CCIS, vol. 1180, pp. 61–73. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3651-9_7

    Chapter  Google Scholar 

  22. Wang, J., Bacic, B., Yan, W.Q.: An effective method for plate number recognition. Multimed. Tools Appl. 77(2), 1679–1692 (2017). https://doi.org/10.1007/s11042-017-4356-z

    Article  Google Scholar 

  23. Zheng, K., Yan, W., Nand, P.: Video dynamics detection using deep neural networks. IEEE Trans. Emerg. Top. Comput. Intell. 2(3), 224–234 (2018)

    Article  Google Scholar 

  24. Shen, Y., Yan, W.: Blind spot monitoring using deep learning. In: IEEE IVCNZ (2018)

    Google Scholar 

  25. Qin, G., Yang, J., Yan, W., Li, Y., Klette, R.: Local fast R-CNN flow for object-centric event recognition in complex traffic scenes. In: Satoh, S. (ed.) PSIVT 2017. LNCS, vol. 10799, pp. 439–452. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92753-4_34

    Chapter  Google Scholar 

  26. Wang, J., Yan, W.: BP-neural network for plate number recognition. Int. J. Digit. Crime Forensics 8(3), 34–45 (2016)

    Article  Google Scholar 

  27. An, N., Yan, W.: Multitarget tracking using Siamese neural networks. ACM TOMM (2021)

    Google Scholar 

  28. Liu, X., Yan, W.: Traffic-light sign recognition using Capsule network. MTAP (2021)

    Google Scholar 

  29. Xing, J., Yan, W.: Traffic sign recognition using guided image filtering. In: ISGV (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Zhongbing Qin & Wei Qi Yan

Authors
  1. Zhongbing Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Qi Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhongbing Qin or Wei Qi Yan .

Editor information

Editors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Minh Nguyen

  2. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

  3. Auckland Bioengineering House, Auckland, New Zealand

    Harvey Ho

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Qin, Z., Yan, W.Q. (2021). Traffic-Sign Recognition Using Deep Learning. In: Nguyen, M., Yan, W.Q., Ho, H. (eds) Geometry and Vision. ISGV 2021. Communications in Computer and Information Science, vol 1386. Springer, Cham. https://doi.org/10.1007/978-3-030-72073-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72073-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72072-8

  • Online ISBN: 978-3-030-72073-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics