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A New Traffic Sign Recognition Technique Taking Shuffled Frog-Leaping Algorithm into Account

Everyday humans use cars to move faster, and the world is a chaotic place, and a little distraction or a mistake could be the reason for an accident and bring people great pain. An assistance system that can distinguish and detect signs on the roads and brings the driver's attention to road sig...

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Detalles Bibliográficos
Autores principales: Demokri Dizji, Pouya, Joudaki, Saba, Kolivand, Hoshang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244287/
https://www.ncbi.nlm.nih.gov/pubmed/35789577
http://dx.doi.org/10.1007/s11277-022-09718-7
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author Demokri Dizji, Pouya
Joudaki, Saba
Kolivand, Hoshang
author_facet Demokri Dizji, Pouya
Joudaki, Saba
Kolivand, Hoshang
author_sort Demokri Dizji, Pouya
collection PubMed
description Everyday humans use cars to move faster, and the world is a chaotic place, and a little distraction or a mistake could be the reason for an accident and bring people great pain. An assistance system that can distinguish and detect signs on the roads and brings the driver's attention to road signs and make them aware of their meaning could be beneficial. The most important part of the Traffic Sign Recognition System is the algorithm. In this paper, a new way toward Traffic Sign Recognition algorithm taking the advantages of Color Segmentation, support vector machines, and histograms of oriented gradients on the GTSRB dataset is proposed. The unsupervised shuffled frog-leaping algorithm is employed for segmenting the images. The results show remarkable improvements by using meta-heuristic algorithms.
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spelling pubmed-92442872022-06-30 A New Traffic Sign Recognition Technique Taking Shuffled Frog-Leaping Algorithm into Account Demokri Dizji, Pouya Joudaki, Saba Kolivand, Hoshang Wirel Pers Commun Article Everyday humans use cars to move faster, and the world is a chaotic place, and a little distraction or a mistake could be the reason for an accident and bring people great pain. An assistance system that can distinguish and detect signs on the roads and brings the driver's attention to road signs and make them aware of their meaning could be beneficial. The most important part of the Traffic Sign Recognition System is the algorithm. In this paper, a new way toward Traffic Sign Recognition algorithm taking the advantages of Color Segmentation, support vector machines, and histograms of oriented gradients on the GTSRB dataset is proposed. The unsupervised shuffled frog-leaping algorithm is employed for segmenting the images. The results show remarkable improvements by using meta-heuristic algorithms. Springer US 2022-06-28 2022 /pmc/articles/PMC9244287/ /pubmed/35789577 http://dx.doi.org/10.1007/s11277-022-09718-7 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Demokri Dizji, Pouya
Joudaki, Saba
Kolivand, Hoshang
A New Traffic Sign Recognition Technique Taking Shuffled Frog-Leaping Algorithm into Account
title A New Traffic Sign Recognition Technique Taking Shuffled Frog-Leaping Algorithm into Account
title_full A New Traffic Sign Recognition Technique Taking Shuffled Frog-Leaping Algorithm into Account
title_fullStr A New Traffic Sign Recognition Technique Taking Shuffled Frog-Leaping Algorithm into Account
title_full_unstemmed A New Traffic Sign Recognition Technique Taking Shuffled Frog-Leaping Algorithm into Account
title_short A New Traffic Sign Recognition Technique Taking Shuffled Frog-Leaping Algorithm into Account
title_sort new traffic sign recognition technique taking shuffled frog-leaping algorithm into account
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244287/
https://www.ncbi.nlm.nih.gov/pubmed/35789577
http://dx.doi.org/10.1007/s11277-022-09718-7
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