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


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.


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


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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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