Abstract
The work presented in this paper suggests a Traffic Sign Recognition (TSR) system whose core is based on a Multilayer Perceptron (MLP). A pre-processing of the traffic sign image (blob) is applied before the core. This operation is made to reduce the redundancy contained in the blob, to reduce the computational cost of the core and to improve its performance. For comparison purposes, the performance of the a statistical method like the k-Nearest Neighbour (k-NN) is included. The number of hidden neurons of the MLP is studied to obtain the value that minimizes the total classification error rate. Once obtained the best network size, the results of the experiments with this parameter show that the MLP achieves a total error probability of 3.85%, which is almost the half of the best obtained with the k-NN.
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© 2005 Springer-Verlag Berlin Heidelberg
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Vicen-Bueno, R., Gil-Pita, R., Rosa-Zurera, M., Utrilla-Manso, M., López-Ferreras, F. (2005). Multilayer Perceptrons Applied to Traffic Sign Recognition Tasks. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_106
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DOI: https://doi.org/10.1007/11494669_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
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