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Neural Computing and Applications

, Volume 17, Issue 3, pp 265–289 | Cite as

Detection and classification of road signs in natural environments

  • Yok-Yen Nguwi
  • Abbas Z. Kouzani
Original Article

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.

Keywords

Recognition Multi-layer Perceptron Neural networks Road signs Images Smart vehicle 

References

  1. 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 1838–1843Google Scholar
  2. 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 1823–1826Google Scholar
  3. 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. 95–100Google Scholar
  4. 4.
    Fang CY, Chen SW, Fuh CS (2003) Road-sign detection and tracking. IEEE Trans Vehicular Technol 52:1329–1341CrossRefGoogle Scholar
  5. 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 computingGoogle Scholar
  6. 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 47–51Google Scholar
  7. 7.
    Gavrila DM, Philomin V (1999) Real-time object detection for smart vehicles. In: Proceedings of IEEE international conference on computer vision, Greece, pp 87–93Google Scholar
  8. 8.
    Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, January 15Google Scholar
  9. 9.
    Gunn SR (1997) Support vector machines for classification and regression. Technical report, Image Speech and Intelligent Systems Research Group, University of SouthamptonGoogle Scholar
  10. 10.
    Hagan MT, Demuth HB, Beale MH (2002) Neural network design, Martin HaganGoogle Scholar
  11. 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 4226–4231Google Scholar
  12. 12.
    Kumar S (2004) Neural networks—a class room approach, Tata McGraw-HillGoogle Scholar
  13. 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-95/09-35Google Scholar
  14. 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 70–75Google Scholar
  15. 15.
    Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533CrossRefGoogle Scholar
  16. 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 626–630Google Scholar
  17. 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, BelgiumGoogle Scholar
  18. 18.
    Paclik P, Novovicova J, Duin R (2006) Building road-sign classifiers using a trainable similarity measure. IEEE Trans Intell Transp Syst 7(3):309–321CrossRefGoogle Scholar
  19. 19.
    Paclik P, Novovicova J, Pudil P, Somol P (2000) Road sign classification using the Laplace kernel classifier. Pattern Recognit Lett 21(13–14):1165–1173zbMATHCrossRefGoogle Scholar
  20. 20.
    Paclik P (1999) Road sign recognition survey (Online). Available: http://euler.fd.cvut.cz/research/rs2/files/skoda-rs-survey.html
  21. 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 249–254Google Scholar
  22. 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 586–591Google Scholar
  23. 23.
    Road Sign Recognition Group (1999) The road sign recognition system (Online). Available: http://euler.fd.cvut.cz/research/rs2
  24. 24.
    Shapiro LG, Stockman GC (2001) Computer vision, Prentice Hall, January 23Google Scholar
  25. 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 213–217Google Scholar
  26. 26.
    Soetedjo A, Yamada K (2005) Traffic sign classification using ring partitioned method. IEICE Trans Fundam E88A(9):166–178Google Scholar
  27. 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 652–656Google Scholar
  28. 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 572–577Google Scholar
  29. 29.
    de la Escalera A, Armingol JM, Mata M (2003) Traffic sign recognition and analysis for intelligent vehicles. Image Vision Comput 21:247–258CrossRefGoogle Scholar
  30. 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, FinlandiaGoogle Scholar
  31. 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 69–76Google Scholar

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  1. 1.School of Computer EngineeringNanyang Technological UniversityNanyang AvenueSingaporeSingapore
  2. 2.School of Engineering and Information TechnologyDeakin UniversityGeelongAustralia

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