Machine learning for quality control system
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/20860 |
Resumo: | In this work, we propose and develop a classification model to be used in a quality control system for clothing manufacturing using machine learning algorithms. The system consists of using pictures taken through mobile devices to detect defects on production objects. In this work, a defect can be a missing component or a wrong component in a production object. Therefore, the function of the system is to classify the components that compose a production object through the use of a classification model. As a manufacturing business progresses, new objects are created, thus, the classification model must be able to learn the new classes without losing previous knowledge. However, most classification algorithms do not support an increase of classes, these need to be trained from scratch with all . Thus. In this work, we make use of an incremental learning algorithm to tackle this problem. This algorithm classifies features extracted from pictures of the production objects using a convolutional neural network (CNN), which have proven to be very successful in image classification problems. We apply the current developed approach to a process in clothing manufacturing. Therefore, the production objects correspond to clothing items |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Machine learning for quality control systemQuality controlIncremental learningImage classificationDefect detection systemIn this work, we propose and develop a classification model to be used in a quality control system for clothing manufacturing using machine learning algorithms. The system consists of using pictures taken through mobile devices to detect defects on production objects. In this work, a defect can be a missing component or a wrong component in a production object. Therefore, the function of the system is to classify the components that compose a production object through the use of a classification model. As a manufacturing business progresses, new objects are created, thus, the classification model must be able to learn the new classes without losing previous knowledge. However, most classification algorithms do not support an increase of classes, these need to be trained from scratch with all . Thus. In this work, we make use of an incremental learning algorithm to tackle this problem. This algorithm classifies features extracted from pictures of the production objects using a convolutional neural network (CNN), which have proven to be very successful in image classification problems. We apply the current developed approach to a process in clothing manufacturing. Therefore, the production objects correspond to clothing itemsSpringer2020-12-15T00:00:00Z2020-01-01T00:00:00Z20202020-11-25T16:12:20Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/20860eng1868-513710.1007/s12652-019-01640-4San-Payo, G.Ferreira, J.Santos, P.Martins, A.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:59:07Zoai:repositorio.iscte-iul.pt:10071/20860Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:30:57.423662Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Machine learning for quality control system |
title |
Machine learning for quality control system |
spellingShingle |
Machine learning for quality control system San-Payo, G. Quality control Incremental learning Image classification Defect detection system |
title_short |
Machine learning for quality control system |
title_full |
Machine learning for quality control system |
title_fullStr |
Machine learning for quality control system |
title_full_unstemmed |
Machine learning for quality control system |
title_sort |
Machine learning for quality control system |
author |
San-Payo, G. |
author_facet |
San-Payo, G. Ferreira, J. Santos, P. Martins, A. |
author_role |
author |
author2 |
Ferreira, J. Santos, P. Martins, A. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
San-Payo, G. Ferreira, J. Santos, P. Martins, A. |
dc.subject.por.fl_str_mv |
Quality control Incremental learning Image classification Defect detection system |
topic |
Quality control Incremental learning Image classification Defect detection system |
description |
In this work, we propose and develop a classification model to be used in a quality control system for clothing manufacturing using machine learning algorithms. The system consists of using pictures taken through mobile devices to detect defects on production objects. In this work, a defect can be a missing component or a wrong component in a production object. Therefore, the function of the system is to classify the components that compose a production object through the use of a classification model. As a manufacturing business progresses, new objects are created, thus, the classification model must be able to learn the new classes without losing previous knowledge. However, most classification algorithms do not support an increase of classes, these need to be trained from scratch with all . Thus. In this work, we make use of an incremental learning algorithm to tackle this problem. This algorithm classifies features extracted from pictures of the production objects using a convolutional neural network (CNN), which have proven to be very successful in image classification problems. We apply the current developed approach to a process in clothing manufacturing. Therefore, the production objects correspond to clothing items |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-15T00:00:00Z 2020-01-01T00:00:00Z 2020 2020-11-25T16:12:20Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/20860 |
url |
http://hdl.handle.net/10071/20860 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1868-5137 10.1007/s12652-019-01640-4 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134871344381952 |