A Review of Computer Vision Techniques in the Detection of Metal Failures

This paper considers and contrasts several computer vision techniques used to detect defects in metallic components during manufacturing or in service. Methodologies include statistical analysis, weighted entropy modification, Fourier transformations, neural networks, and deep learning. Such systems...

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Autores principales:	Fitzgerald Deborah, Fragoudakis Roselita
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/cc29d016fa294ae6aa0f135dab1673b5
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id	oai:doaj.org-article:cc29d016fa294ae6aa0f135dab1673b5
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spelling	oai:doaj.org-article:cc29d016fa294ae6aa0f135dab1673b52021-12-02T17:13:46ZA Review of Computer Vision Techniques in the Detection of Metal Failures2261-236X10.1051/matecconf/202134902021https://doaj.org/article/cc29d016fa294ae6aa0f135dab1673b52021-01-01T00:00:00Zhttps://www.matec-conferences.org/articles/matecconf/pdf/2021/18/matecconf_iceaf2021_02021.pdfhttps://doaj.org/toc/2261-236XThis paper considers and contrasts several computer vision techniques used to detect defects in metallic components during manufacturing or in service. Methodologies include statistical analysis, weighted entropy modification, Fourier transformations, neural networks, and deep learning. Such systems are used by manufacturers to perform non-destructive testing and inspection of components at high speeds [1]; providing better error detection than traditional human visual inspection, and lower costs [2]. This is a review of the computer vision system comparing different mathematical analysis in order to illustrate the strengths and weaknesses relative to the nature of the defect. It includes exemplar that histograms and statistical analysis operate best with significant contrast between the defect and background, that co-occurrence matrix and Gabor filtering are computationally expensive, that structural analysis is useful when there are repeated patterns, that Fourier transforms, applied to spatial data, need windowing to capture localized issues, and that neural networks can be utilized after training.Fitzgerald DeborahFragoudakis RoselitaEDP SciencesarticleEngineering (General). Civil engineering (General)TA1-2040ENFRMATEC Web of Conferences, Vol 349, p 02021 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Fitzgerald Deborah Fragoudakis Roselita A Review of Computer Vision Techniques in the Detection of Metal Failures
description	This paper considers and contrasts several computer vision techniques used to detect defects in metallic components during manufacturing or in service. Methodologies include statistical analysis, weighted entropy modification, Fourier transformations, neural networks, and deep learning. Such systems are used by manufacturers to perform non-destructive testing and inspection of components at high speeds [1]; providing better error detection than traditional human visual inspection, and lower costs [2]. This is a review of the computer vision system comparing different mathematical analysis in order to illustrate the strengths and weaknesses relative to the nature of the defect. It includes exemplar that histograms and statistical analysis operate best with significant contrast between the defect and background, that co-occurrence matrix and Gabor filtering are computationally expensive, that structural analysis is useful when there are repeated patterns, that Fourier transforms, applied to spatial data, need windowing to capture localized issues, and that neural networks can be utilized after training.
format	article
author	Fitzgerald Deborah Fragoudakis Roselita
author_facet	Fitzgerald Deborah Fragoudakis Roselita
author_sort	Fitzgerald Deborah
title	A Review of Computer Vision Techniques in the Detection of Metal Failures
title_short	A Review of Computer Vision Techniques in the Detection of Metal Failures
title_full	A Review of Computer Vision Techniques in the Detection of Metal Failures
title_fullStr	A Review of Computer Vision Techniques in the Detection of Metal Failures
title_full_unstemmed	A Review of Computer Vision Techniques in the Detection of Metal Failures
title_sort	review of computer vision techniques in the detection of metal failures
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/cc29d016fa294ae6aa0f135dab1673b5
work_keys_str_mv	AT fitzgeralddeborah areviewofcomputervisiontechniquesinthedetectionofmetalfailures AT fragoudakisroselita areviewofcomputervisiontechniquesinthedetectionofmetalfailures AT fitzgeralddeborah reviewofcomputervisiontechniquesinthedetectionofmetalfailures AT fragoudakisroselita reviewofcomputervisiontechniquesinthedetectionofmetalfailures
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A Review of Computer Vision Techniques in the Detection of Metal Failures

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