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6 September 2019 Evaluation of algorithms for traffic sign detection
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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.
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© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Miguel Lopez-Montiel, Yoshio Rubio, Moisés Sánchez-Adame, and Ulises Orozco-Rosas "Evaluation of algorithms for traffic sign detection", Proc. SPIE 11136, Optics and Photonics for Information Processing XIII, 111360M (6 September 2019); https://doi.org/10.1117/12.2529709
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