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Multiple traffic sign detection based on the artificial bee colony method

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Abstract

Traffic signs play an important role in warning drivers by providing information about traffic restrictions, directions, and road quality in order to make sure that every driver is kept safe. Over the past decade, the development of autonomous vehicles has been an active area of research. Therefore, automatic traffic sign detection is a crucial part of the intelligent transportation systems that can be used in autonomous vehicles to detect traffic signs on the road. The optimization methods based on a biologically inspired computation are very powerful in solving optimization problems. In this work, we consider the traffic sign detection task as an optimization problem and propose the artificial bee colony (ABC) method, one of the most popular biologically inspired methods, as an alternative approach for solving it. In other words, we aim to present an algorithm for the automatic detection of multiple traffic signs with a circular shape based on solutions generated by the ABC method without considering the conventional Hough transform principles. Experimental results obtained by our method demonstrate that the proposed approach works well for multiple traffic sign detection and outperforms other existing algorithms.

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Authors and Affiliations

  1. Computational Intelligence Research Laboratory (CIRLab), Computer Engineering Department, Faculty of Engineering at Sriracha, Kasetsart University Sriracha Campus, Chonburi, 20230, Thailand

    Anan Banharnsakun

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  1. Anan Banharnsakun
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Correspondence to Anan Banharnsakun.

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Banharnsakun, A. Multiple traffic sign detection based on the artificial bee colony method. Evolving Systems 9, 255–264 (2018). https://doi.org/10.1007/s12530-017-9215-7

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  • DOI: https://doi.org/10.1007/s12530-017-9215-7

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