Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

Abstract In the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range...

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Autores principales:	Majid Amirfakhrian, Mahboub Parhizkar
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Machine vision Image processing Image segmentation Dimensional reduction Crash damage detection Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/43ca8731bf2a4960bf40fa0bad38474e
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spelling	oai:doaj.org-article:43ca8731bf2a4960bf40fa0bad38474e2021-11-28T12:03:17ZIntegration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents10.1186/s40537-021-00539-22196-1115https://doaj.org/article/43ca8731bf2a4960bf40fa0bad38474e2021-11-01T00:00:00Zhttps://doi.org/10.1186/s40537-021-00539-2https://doaj.org/toc/2196-1115Abstract In the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.Majid AmirfakhrianMahboub ParhizkarSpringerOpenarticleMachine visionImage processingImage segmentationDimensional reductionCrash damage detectionComputer engineering. Computer hardwareTK7885-7895Information technologyT58.5-58.64Electronic computers. Computer scienceQA75.5-76.95ENJournal of Big Data, Vol 8, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine vision Image processing Image segmentation Dimensional reduction Crash damage detection Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Machine vision Image processing Image segmentation Dimensional reduction Crash damage detection Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95 Majid Amirfakhrian Mahboub Parhizkar Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents
description	Abstract In the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.
format	article
author	Majid Amirfakhrian Mahboub Parhizkar
author_facet	Majid Amirfakhrian Mahboub Parhizkar
author_sort	Majid Amirfakhrian
title	Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents
title_short	Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents
title_full	Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents
title_fullStr	Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents
title_full_unstemmed	Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents
title_sort	integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/43ca8731bf2a4960bf40fa0bad38474e
work_keys_str_mv	AT majidamirfakhrian integrationofimagesegmentationandfuzzytheorytoimprovetheaccuracyofdamagedetectionareasintrafficaccidents AT mahboubparhizkar integrationofimagesegmentationandfuzzytheorytoimprovetheaccuracyofdamagedetectionareasintrafficaccidents
_version_	1718408254760943616

Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

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