Home > Archive > 2013 > Volume 3 Number 3 (Jun. 2013) >
IJMLC 2013 Vol.3(3): 313-317 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.329

Road Traffic Signs Recognition Using Genetic Algorithms and Neural Networks

Stephen Karungaru, Hitoshi Nakano, and Minoru Fukumi

Abstract—This paper proposes an efficient real time driving sign “stop” detection method using template matching, genetic algorithms and neural network. Stop signs are usually installed on junctions without traffic lights to warn drivers. However, many accidents still occur at these locations because either the driver does not notice the signs or just ignore them. Therefore, to reduce such accidents, we propose an automatic stop sign detection method to aid the drivers and also contribute to future automatic driving system such as the Intelligent Traffic System (ITS). Our method efficiently extracts the sign region´s candidate regions, performs template matching using genetic algorithms and verifies the presence of the road sign using neural networks. Although, we face various problems including camera shake and complicated scenes, our method produces an encouraging accuracy of about 96%.

Index Terms—Genetic algorithms, road signs, template matching, neural networks.

The authors are with the Graduate School of Advanced Technology and Science, The University of Tokushima (e-mail: karunga@is.tokushima-u.ac.jp).


Cite:Stephen Karungaru, Hitoshi Nakano, and Minoru Fukumi, "Road Traffic Signs Recognition Using Genetic Algorithms and Neural Networks," International Journal of Machine Learning and Computing vol.3, no. 3, pp. 313-317, 2013.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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