Augmented Reality Maintenance Assistant Using YOLOv5
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/10316/100609 https://doi.org/10.3390/app11114758 |
Resumo: | Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Augmented Reality Maintenance Assistant Using YOLOv5Augmented realityCar engine datasetCar part detectionTask assistantYOLOv5Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/100609http://hdl.handle.net/10316/100609https://doi.org/10.3390/app11114758eng2076-3417Malta, AnaMendes, MateusFarinha, Torresinfo: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:RCAAP2022-07-07T20:31:08Zoai:estudogeral.uc.pt:10316/100609Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:17:57.633079Repositório 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 |
Augmented Reality Maintenance Assistant Using YOLOv5 |
title |
Augmented Reality Maintenance Assistant Using YOLOv5 |
spellingShingle |
Augmented Reality Maintenance Assistant Using YOLOv5 Malta, Ana Augmented reality Car engine dataset Car part detection Task assistant YOLOv5 |
title_short |
Augmented Reality Maintenance Assistant Using YOLOv5 |
title_full |
Augmented Reality Maintenance Assistant Using YOLOv5 |
title_fullStr |
Augmented Reality Maintenance Assistant Using YOLOv5 |
title_full_unstemmed |
Augmented Reality Maintenance Assistant Using YOLOv5 |
title_sort |
Augmented Reality Maintenance Assistant Using YOLOv5 |
author |
Malta, Ana |
author_facet |
Malta, Ana Mendes, Mateus Farinha, Torres |
author_role |
author |
author2 |
Mendes, Mateus Farinha, Torres |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Malta, Ana Mendes, Mateus Farinha, Torres |
dc.subject.por.fl_str_mv |
Augmented reality Car engine dataset Car part detection Task assistant YOLOv5 |
topic |
Augmented reality Car engine dataset Car part detection Task assistant YOLOv5 |
description |
Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 |
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/10316/100609 http://hdl.handle.net/10316/100609 https://doi.org/10.3390/app11114758 |
url |
http://hdl.handle.net/10316/100609 https://doi.org/10.3390/app11114758 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2076-3417 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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 |
|
_version_ |
1799134074966638592 |