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On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures
Defects in textured materials present a great variability, usually requiring ad-hoc solutions for each specific case. This research work proposes a solution that combines two machine learning-based approaches, convolutional autoencoders, CA; one class support vector machines, SVM. Both methods are t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150843/ https://www.ncbi.nlm.nih.gov/pubmed/34064975 http://dx.doi.org/10.3390/s21103339 |
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author | Tellaeche Iglesias, Alberto Campos Anaya, Miguel Ángel Pajares Martinsanz, Gonzalo Pastor-López, Iker |
author_facet | Tellaeche Iglesias, Alberto Campos Anaya, Miguel Ángel Pajares Martinsanz, Gonzalo Pastor-López, Iker |
author_sort | Tellaeche Iglesias, Alberto |
collection | PubMed |
description | Defects in textured materials present a great variability, usually requiring ad-hoc solutions for each specific case. This research work proposes a solution that combines two machine learning-based approaches, convolutional autoencoders, CA; one class support vector machines, SVM. Both methods are trained using only defect free textured images for each type of analyzed texture, labeling the samples for the SVMs in an automatic way. This work is based on two image processing streams using image sensors: (1) the CA first processes the incoming image from the input to the output, producing a reconstructed image, from which a measurement of correct or defective image is obtained; (2) the second process uses the latent layer information as input to the SVM to produce a measurement of classification. Both measurements are effectively combined, making an additional research contribution. The results obtained achieve a percentage of success of 92% on average, outperforming results of previous works. |
format | Online Article Text |
id | pubmed-8150843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81508432021-05-27 On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures Tellaeche Iglesias, Alberto Campos Anaya, Miguel Ángel Pajares Martinsanz, Gonzalo Pastor-López, Iker Sensors (Basel) Article Defects in textured materials present a great variability, usually requiring ad-hoc solutions for each specific case. This research work proposes a solution that combines two machine learning-based approaches, convolutional autoencoders, CA; one class support vector machines, SVM. Both methods are trained using only defect free textured images for each type of analyzed texture, labeling the samples for the SVMs in an automatic way. This work is based on two image processing streams using image sensors: (1) the CA first processes the incoming image from the input to the output, producing a reconstructed image, from which a measurement of correct or defective image is obtained; (2) the second process uses the latent layer information as input to the SVM to produce a measurement of classification. Both measurements are effectively combined, making an additional research contribution. The results obtained achieve a percentage of success of 92% on average, outperforming results of previous works. MDPI 2021-05-11 /pmc/articles/PMC8150843/ /pubmed/34064975 http://dx.doi.org/10.3390/s21103339 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tellaeche Iglesias, Alberto Campos Anaya, Miguel Ángel Pajares Martinsanz, Gonzalo Pastor-López, Iker On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures |
title | On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures |
title_full | On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures |
title_fullStr | On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures |
title_full_unstemmed | On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures |
title_short | On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures |
title_sort | on combining convolutional autoencoders and support vector machines for fault detection in industrial textures |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150843/ https://www.ncbi.nlm.nih.gov/pubmed/34064975 http://dx.doi.org/10.3390/s21103339 |
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