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Proceedings Paper

Evaluation of algorithms for traffic sign detection
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Paper Abstract

Traffic sign detection is a crucial task in autonomous driving systems. Due to its importance, several techniques have been used to solve this problem. In this work, the three more common approaches are evaluated. The first approach uses a model of the traffic sign which is based in color and shape. The second one enhances the image model of the first approach using K-means for color clustering. The last approach uses convolutional neural networks designed for image detection. The LISA Traffic Sign Dataset was used which it was divided into three superclasses: prohibition, mandatory, and warning signs. The evaluation was done using objective metrics used in the state-of-the-art.

Paper Details

Date Published: 6 September 2019
PDF: 17 pages
Proc. SPIE 11136, Optics and Photonics for Information Processing XIII, 111360M (6 September 2019); doi: 10.1117/12.2529709
Show Author Affiliations
Miguel Lopez-Montiel, Instituto Politécnico Nacional, CITEDI - IPN (Mexico)
Yoshio Rubio, Instituto Politécnico Nacional, CITEDI - IPN (Mexico)
Moisés Sánchez-Adame, Instituto Politécnico Nacional, CITEDI - IPN (Mexico)
CETYS Univ. Baja California (Mexico)
Ulises Orozco-Rosas, CETYS Univ. Baja California (Mexico)

Published in SPIE Proceedings Vol. 11136:
Optics and Photonics for Information Processing XIII
Khan M. Iftekharuddin; Abdul A. S. Awwal; Victor H. Diaz-Ramirez; Andrés Márquez, Editor(s)

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