Skip to main content
SpringerLink
Log in

Detection and classification of road signs in natural environments

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies the detected road signs. This paper presents an automatic neural-network-based road sign recognition system. First, a study of the existing road sign recognition research is presented. In this study, the issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign recognition problem are reviewed, and a comparison of the features of these methods is given. Second, the developed road sign recognition system is described. The system is capable of analysing live colour road scene images, detecting multiple road signs within each image, and classifying the type of road signs detected. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space, and then detects road signs using a Multi-layer Perceptron neural-network. The classification module determines the type of detected road signs using a series of one to one architectural Multi-layer Perceptron neural networks. Two sets of classifiers are trained using the Resillient-Backpropagation and Scaled-Conjugate-Gradient algorithms. The two modules of the system are evaluated individually first. Then the system is tested as a whole. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 95.96% using the scaled-conjugate-gradient trained classifiers.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

References

  1. Aoyagi Y, Asakura T (1996) A study on traffic sign recognition in scene image using genetic algorithms and neural networks. In: Proceedings of the 22nd international conference on industrial electronics, control, and instrumentation, Taipeis, pp 1838–1843

  2. Benallal M, Meunier J (2003) Real-time colour segmentation of road signs. In: Proceedings of the Canadian conference on electrical and computer engineering, vol 3, pp 1823–1826

  3. Estevez L and Kehtarnavaz N (1996) A real-time histographic approach to road sign recognition. In: Proceedings of the IEEE southwest symposium on image analysis and interpretation, pp. 95–100

  4. Fang CY, Chen SW, Fuh CS (2003) Road-sign detection and tracking. IEEE Trans Vehicular Technol 52:1329–1341

    Article  Google Scholar 

  5. Fleyeh H, Gilani S, Dougherty M (2006) Road sign detection and recognition using Fuzzy ARTMAP: a case study swedish speed-limit signs. In: Proceedings of artificial intelligence and soft computing

  6. Gao X, Shevtsova N, Hong K, Batty S, Podladchikova L, Golovan A, Shaposhnikov D, Gusakova V (2002) Vision models based identification of traffic signs. In: Proceedings of the 1st Europe conference on color in graphics, image and vision, France, pp 47–51

  7. Gavrila DM, Philomin V (1999) Real-time object detection for smart vehicles. In: Proceedings of IEEE international conference on computer vision, Greece, pp 87–93

  8. Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, January 15

  9. Gunn SR (1997) Support vector machines for classification and regression. Technical report, Image Speech and Intelligent Systems Research Group, University of Southampton

  10. Hagan MT, Demuth HB, Beale MH (2002) Neural network design, Martin Hagan

  11. Kellmeyer DL, Zwahlen HT (1994) Detection of highway warning signs in natural video images using color mage processing and neural networks. In: Proceedings of the IEEE International Conference on Neural Networks, vol 7, pp 4226–4231

  12. Kumar S (2004) Neural networks—a class room approach, Tata McGraw-Hill

  13. Lalonde M, Li Y (1995) Road sign recognition–survey of the state of art. Technical Report for Sub-Project 2.4, Centre de recherche informatique de Montreal, CRIM-IIT-95/09-35

  14. Loy G, Barnes N (2004) Fast shape-based road sign detection for a driver assistance system. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, vol 1, pp 70–75

  15. Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533

    Article  Google Scholar 

  16. Ohara H, Nishikawa I, Miki S, Yabuki N (2002) Detection and recognition of road signs using simple layered neural networks. In: Proceeding of the 9th international conference on neural information processing, vol 2, pp 626–630

  17. Paclik P, Novovicova J (2000) Road sign classification without colour information. In: Proceedings of 6th conference of advaced school of imaging and computing, Lommel, Belgium

  18. Paclik P, Novovicova J, Duin R (2006) Building road-sign classifiers using a trainable similarity measure. IEEE Trans Intell Transp Syst 7(3):309–321

    Article  Google Scholar 

  19. Paclik P, Novovicova J, Pudil P, Somol P (2000) Road sign classification using the Laplace kernel classifier. Pattern Recognit Lett 21(13–14):1165–1173

    Article  MATH  Google Scholar 

  20. Paclik P (1999) Road sign recognition survey (Online). Available: http://euler.fd.cvut.cz/research/rs2/files/skoda-rs-survey.html

  21. Priese L, Klieber J, Lakmann R, Rehrmann V, Schian R (1994) New results on traffic sign recognition. In: Proceedings of the intelligent vehicles symposium, pp 249–254

  22. Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of the IEEE international conference on neural networks, pp 586–591

  23. Road Sign Recognition Group (1999) The road sign recognition system (Online). Available: http://euler.fd.cvut.cz/research/rs2

  24. Shapiro LG, Stockman GC (2001) Computer vision, Prentice Hall, January 23

  25. Shaposhnikov D, Podladchikova LN, Golovan AV, Shevtsova N, Kunbin AH, Xiaohong G (2002) Road sign recognition by single positioning of space-variant sensor window. In: Proceedings of the 15th international conference on vision interface, Canada, Calgary, pp 213–217

  26. Soetedjo A, Yamada K (2005) Traffic sign classification using ring partitioned method. IEICE Trans Fundam E88A(9):166–178

    Google Scholar 

  27. Torresen J, Bakke J, Sekanina L (2004) Efficient recognition of speed limit signs. In: Proceedings of the 7th international IEEE conference on intelligent transportation systems, pp 652–656

  28. Vitabile S, Pollaccia G, Pilato G, Sorbello F (2001) Road signs recognition using a dynamic pixel aggregation technique in the HSV colour space. In: Proceedings of the international conference on image analysis and processing, Italy, pp 572–577

  29. de la Escalera A, Armingol JM, Mata M (2003) Traffic sign recognition and analysis for intelligent vehicles. Image Vision Comput 21:247–258

    Article  Google Scholar 

  30. de la Escalera A, Armingol JM, Salichs MA (2001) Traffic sign detection for driver support systems. In: Proceedings of the international conference on field and service robotics, Finlandia

  31. de la Escalera A, Radeva P (2004) Fast greyscale road sign model matching and recognition. In: Vitria J et al (eds) Recent advances in artificial intelligence research and development, IOS Press, pp 69–76

Download references

Author information

Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Block N4 #B1A-02, Nanyang Avenue, Singapore, 639798, Singapore

    Yok-Yen Nguwi

  2. School of Engineering and Information Technology, Deakin University, Geelong, VIC, 3217, Australia

    Abbas Z. Kouzani

Authors
  1. Yok-Yen Nguwi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abbas Z. Kouzani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yok-Yen Nguwi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nguwi, YY., Kouzani, A.Z. Detection and classification of road signs in natural environments. Neural Comput & Applic 17, 265–289 (2008). https://doi.org/10.1007/s00521-007-0120-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-007-0120-z

Keywords

Search

Navigation