Detection and classification of road signs in natural environments

Nguwi, Yok-Yen and Kouzani, Abbas Z. 2008, Detection and classification of road signs in natural environments, Neural computing and applications, vol. 17, no. 3, pp. 265-289, doi: 10.1007/s00521-007-0120-z.

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Title Detection and classification of road signs in natural environments
Author(s) Nguwi, Yok-Yen
Kouzani, Abbas Z.ORCID iD for Kouzani, Abbas Z.
Journal name Neural computing and applications
Volume number 17
Issue number 3
Start page 265
End page 289
Total pages 25
Publisher Springer
Place of publication London, England
Publication date 2008-06
ISSN 0941-0643
Keyword(s) recognition
multi-layer perceptron
neural networks
road signs
smart vehicle
Summary 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.
Language eng
DOI 10.1007/s00521-007-0120-z
Field of Research 090609 Signal Processing
Socio Economic Objective 880109 Road Safety
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, Springer-Verlag
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