Generative adversarial networks for data augmentation in structural adhesive inspection
Autor(a) principal: | |
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Data de Publicação: | 2021 |
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/10362/133127 |
Resumo: | UIDB/- 00066/2020 POCI-01-0247-FEDER-034072 |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Generative adversarial networks for data augmentation in structural adhesive inspectionData augmentationDeep learningIndustry 4.0Quality controlStructural adhesiveMaterials Science(all)InstrumentationEngineering(all)Process Chemistry and TechnologyComputer Science ApplicationsFluid Flow and Transfer ProcessesUIDB/- 00066/2020 POCI-01-0247-FEDER-034072The technological advances brought forth by the Industry 4.0 paradigm have renewed the disruptive potential of artificial intelligence in the manufacturing sector, building the data-driven era on top of concepts such as Cyber-Physical Systems and the Internet of Things. However, data availability remains a major challenge for the success of these solutions, particularly concerning those based on deep learning approaches. Specifically in the quality inspection of structural adhesive applications, found commonly in the automotive domain, defect data with sufficient variety, volume and quality is generally costly, time-consuming and inefficient to obtain, jeopardizing the viability of such approaches due to data scarcity. To mitigate this, we propose a novel approach to generate synthetic training data for this application, leveraging recent breakthroughs in training generative adversarial networks with limited data to improve the performance of automated inspection methods based on deep learning, especially for imbalanced datasets. Preliminary results in a real automotive pilot cell show promise in this direction, with the approach being able to generate realistic adhesive bead images and consequently object detection models showing improved mean average precision at different thresholds when trained on the augmented dataset. For reproducibility purposes, the model weights, configurations and data encompassed in this study are made publicly available.DEE - Departamento de Engenharia Electrotécnica e de ComputadoresUNINOVA-Instituto de Desenvolvimento de Novas TecnologiasCTS - Centro de Tecnologia e SistemasDEE2010-C2 Robótica e Manufactura Integrada por ComputadorRUNPeres, Ricardo SilvaAzevedo, MiguelAraújo, Sara OleiroGuedes, MagnoMiranda, FábioBarata, José2022-02-17T23:19:32Z2021-04-012021-04-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/133127eng2076-3417PURE: 36679070https://doi.org/10.3390/app11073086info: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:RCAAP2024-05-22T17:59:33Zoai:run.unl.pt:10362/133127Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:59:33Repositório 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 |
Generative adversarial networks for data augmentation in structural adhesive inspection |
title |
Generative adversarial networks for data augmentation in structural adhesive inspection |
spellingShingle |
Generative adversarial networks for data augmentation in structural adhesive inspection Peres, Ricardo Silva Data augmentation Deep learning Industry 4.0 Quality control Structural adhesive Materials Science(all) Instrumentation Engineering(all) Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
title_short |
Generative adversarial networks for data augmentation in structural adhesive inspection |
title_full |
Generative adversarial networks for data augmentation in structural adhesive inspection |
title_fullStr |
Generative adversarial networks for data augmentation in structural adhesive inspection |
title_full_unstemmed |
Generative adversarial networks for data augmentation in structural adhesive inspection |
title_sort |
Generative adversarial networks for data augmentation in structural adhesive inspection |
author |
Peres, Ricardo Silva |
author_facet |
Peres, Ricardo Silva Azevedo, Miguel Araújo, Sara Oleiro Guedes, Magno Miranda, Fábio Barata, José |
author_role |
author |
author2 |
Azevedo, Miguel Araújo, Sara Oleiro Guedes, Magno Miranda, Fábio Barata, José |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
DEE - Departamento de Engenharia Electrotécnica e de Computadores UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias CTS - Centro de Tecnologia e Sistemas DEE2010-C2 Robótica e Manufactura Integrada por Computador RUN |
dc.contributor.author.fl_str_mv |
Peres, Ricardo Silva Azevedo, Miguel Araújo, Sara Oleiro Guedes, Magno Miranda, Fábio Barata, José |
dc.subject.por.fl_str_mv |
Data augmentation Deep learning Industry 4.0 Quality control Structural adhesive Materials Science(all) Instrumentation Engineering(all) Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
topic |
Data augmentation Deep learning Industry 4.0 Quality control Structural adhesive Materials Science(all) Instrumentation Engineering(all) Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
description |
UIDB/- 00066/2020 POCI-01-0247-FEDER-034072 |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-04-01 2021-04-01T00:00:00Z 2022-02-17T23:19:32Z |
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/10362/133127 |
url |
http://hdl.handle.net/10362/133127 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2076-3417 PURE: 36679070 https://doi.org/10.3390/app11073086 |
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.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 |
mluisa.alvim@gmail.com |
_version_ |
1817545847415504896 |