Fast and robust road sign detection in driver assistance systems

Fast and robust road sign detection in driver assistance systems Road sign detection plays a critical role in automatic driver assistance systems. Road signs possess a number of unique visual qualities in images due to their specific colors and symmetric shapes. In this paper, road signs are detected by a two- level hierarchical framework that considers both color and shape of the signs. To address the problem of low image contrast, we propose a new color visual saliency segmentation algorithm, which uses the ratios of enhanced and normalized color values to capture color information. To improve computation efficiency and reduce false alarm rate, we modify the fast radial symmetry transform (RST) algorithm, and propose to use an edge pairwise voting scheme to group feature points based on their underlying symmetry in the candidate regions. Experimental results on several benchmarking datasets demonstrate the superiority of our method over the state-of-the-arts on both efficiency and robustness. Keywords Road sign detection · Visual saliency · Normalized RGB colors · Improved radial symmetry transform (IRST) · Real-time applications 1 Introduction information of the current road state, forbidden maneuvers, the right-of-way, etc, and has received more and more In recent years, traffic accidents have caused great eco- attentions [16, 17]. nomic loss, injuries and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Fast and robust road sign detection in driver assistance systems

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools
ISSN
0924-669X
eISSN
1573-7497
D.O.I.
10.1007/s10489-018-1199-x
Publisher site
See Article on Publisher Site

Abstract

Road sign detection plays a critical role in automatic driver assistance systems. Road signs possess a number of unique visual qualities in images due to their specific colors and symmetric shapes. In this paper, road signs are detected by a two- level hierarchical framework that considers both color and shape of the signs. To address the problem of low image contrast, we propose a new color visual saliency segmentation algorithm, which uses the ratios of enhanced and normalized color values to capture color information. To improve computation efficiency and reduce false alarm rate, we modify the fast radial symmetry transform (RST) algorithm, and propose to use an edge pairwise voting scheme to group feature points based on their underlying symmetry in the candidate regions. Experimental results on several benchmarking datasets demonstrate the superiority of our method over the state-of-the-arts on both efficiency and robustness. Keywords Road sign detection · Visual saliency · Normalized RGB colors · Improved radial symmetry transform (IRST) · Real-time applications 1 Introduction information of the current road state, forbidden maneuvers, the right-of-way, etc, and has received more and more In recent years, traffic accidents have caused great eco- attentions [16, 17]. nomic loss, injuries and

Journal

Applied IntelligenceSpringer Journals

Published: May 28, 2018

References

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