Simulation-Based Data Augmentation for the Quality Inspection of Structural Adhesive with Deep Learning

Detalhes bibliográficos
Autor(a) principal: Peres, Ricardo Silva
Data de Publicação: 2021
Outros Autores: Guedes, Magno, Miranda, Fabio, Barata, José
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/132977
Resumo: UIDB/00066/2020 POCI-01-0247-FEDER-034072
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spelling Simulation-Based Data Augmentation for the Quality Inspection of Structural Adhesive with Deep Learningdeep learningQuality inspectionsimulationstructural adhesivesynthetic dataComputer Science(all)Materials Science(all)Engineering(all)UIDB/00066/2020 POCI-01-0247-FEDER-034072The advent of Industry 4.0 has shown the tremendous transformative potential of combining artificial intelligence, cyber-physical systems and Internet of Things concepts in industrial settings. Despite this, data availability is still a major roadblock for the successful adoption of data-driven solutions, particularly concerning deep learning approaches in manufacturing. Specifically in the quality control domain, annotated defect data can often be costly, time-consuming and inefficient to obtain, potentially compromising the viability of deep learning approaches due to data scarcity. In this context, we propose a novel method for generating annotated synthetic training data for automated quality inspections of structural adhesive applications, validated in an industrial cell for automotive parts. Our approach greatly reduces the cost of training deep learning models for this task, while simultaneously improving their performance in a scarce manufacturing data context with imbalanced training sets by 3.1% (mAP@0.50). Additional results can be seen at https://ricardosperes.github.io/simulation-synth-adhesive/.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 SilvaGuedes, MagnoMiranda, FabioBarata, José2022-02-15T23:36:07Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10application/pdfhttp://hdl.handle.net/10362/132977engPURE: 36678917https://doi.org/10.1109/ACCESS.2021.3082690info: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:RCAAP2024-03-11T05:11:38Zoai:run.unl.pt:10362/132977Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:47:39.105393Repositório 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 Simulation-Based Data Augmentation for the Quality Inspection of Structural Adhesive with Deep Learning
title Simulation-Based Data Augmentation for the Quality Inspection of Structural Adhesive with Deep Learning
spellingShingle Simulation-Based Data Augmentation for the Quality Inspection of Structural Adhesive with Deep Learning
Peres, Ricardo Silva
deep learning
Quality inspection
simulation
structural adhesive
synthetic data
Computer Science(all)
Materials Science(all)
Engineering(all)
title_short Simulation-Based Data Augmentation for the Quality Inspection of Structural Adhesive with Deep Learning
title_full Simulation-Based Data Augmentation for the Quality Inspection of Structural Adhesive with Deep Learning
title_fullStr Simulation-Based Data Augmentation for the Quality Inspection of Structural Adhesive with Deep Learning
title_full_unstemmed Simulation-Based Data Augmentation for the Quality Inspection of Structural Adhesive with Deep Learning
title_sort Simulation-Based Data Augmentation for the Quality Inspection of Structural Adhesive with Deep Learning
author Peres, Ricardo Silva
author_facet Peres, Ricardo Silva
Guedes, Magno
Miranda, Fabio
Barata, José
author_role author
author2 Guedes, Magno
Miranda, Fabio
Barata, José
author2_role 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
Guedes, Magno
Miranda, Fabio
Barata, José
dc.subject.por.fl_str_mv deep learning
Quality inspection
simulation
structural adhesive
synthetic data
Computer Science(all)
Materials Science(all)
Engineering(all)
topic deep learning
Quality inspection
simulation
structural adhesive
synthetic data
Computer Science(all)
Materials Science(all)
Engineering(all)
description UIDB/00066/2020 POCI-01-0247-FEDER-034072
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01T00:00:00Z
2022-02-15T23:36:07Z
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/10362/132977
url http://hdl.handle.net/10362/132977
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 36678917
https://doi.org/10.1109/ACCESS.2021.3082690
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 10
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
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