Traffic sign classification by image preprocessing and neural networks

Authors:
R. Vicen-Bueno;A. García-González;E. Torijano-Gordo;R. Gil-Pita;M. Rosa-Zurera
Affiliations:
Signal Theory and Communications Department, Superior Polytechnic School, University of Alcalá, Alcalá de Henares, Madrid, Spain;Signal Theory and Communications Department, Superior Polytechnic School, University of Alcalá, Alcalá de Henares, Madrid, Spain;Signal Theory and Communications Department, Superior Polytechnic School, University of Alcalá, Alcalá de Henares, Madrid, Spain;Signal Theory and Communications Department, Superior Polytechnic School, University of Alcalá, Alcalá de Henares, Madrid, Spain;Signal Theory and Communications Department, Superior Polytechnic School, University of Alcalá, Alcalá de Henares, Madrid, Spain
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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Abstract

The aim of this work is to design a Traffic Sign Classification system that combines different image preprocessing techniques with Neural Networks. It must be robust against image problems like rotation, deterioration, vandalism, and so on. The preprocessings applied to the gray scale transformed image are: the median filter (MF), the histogram equalization (HE), and the vertical (VH) and horizontal (HH) histograms with fixed or variable (mean value or Otsu method) thresholding. The k-Nearest Neighbour (k-NN) classifier is used for comparison purposes. The best performance is obtained with the combination of preprocessings: MF, HE and VH and HH with a fixed threshold (T = 185), with a two hidden layer MultiLayer Perceptron (MLP), which achieves a probability of classification of 98, 72% for nine different classes of blue traffic signs and noise. The performance is better than the classifier based on one hidden layer MLP in at least 1, 28% and based on k-NN in at least 5, 13%. If computational cost must be reduced, other preprocessings with a one hidden layer MLP are proposed, which performance is lower.