Blind people: clothing category classification and stain detection using transfer learning
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/85137 |
Resumo: | The ways in which people dress, as well as the styles that they prefer for different contexts and occasions, are part of their identity. Every day, blind people face limitations in identifying and inspecting their garments, and dressing can be a difficult and stressful task. Taking advantage of the great technological advancements, it becomes of the utmost importance to minimize, as much as possible, the limitations of a blind person when choosing garments. Hence, this work aimed at categorizing and detecting the presence of stains on garments, using artificial intelligence algorithms. In our approach, transfer learning was used for category classification, where a benchmark was performed between convolutional neural networks (CNNs), with the best model achieving an F1 score of 91%. Stain detection was performed through the fine tuning of a deep learning object detector, i.e., the mask R (region-based)-CNN. This approach is also analyzed and discussed, as it allowed us to achieve better results than those available in the literature. |
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Blind people: clothing category classification and stain detection using transfer learningblind peopleclothing recognitionstain detectiontransfer learningdeep learningScience & TechnologyThe ways in which people dress, as well as the styles that they prefer for different contexts and occasions, are part of their identity. Every day, blind people face limitations in identifying and inspecting their garments, and dressing can be a difficult and stressful task. Taking advantage of the great technological advancements, it becomes of the utmost importance to minimize, as much as possible, the limitations of a blind person when choosing garments. Hence, this work aimed at categorizing and detecting the presence of stains on garments, using artificial intelligence algorithms. In our approach, transfer learning was used for category classification, where a benchmark was performed between convolutional neural networks (CNNs), with the best model achieving an F1 score of 91%. Stain detection was performed through the fine tuning of a deep learning object detector, i.e., the mask R (region-based)-CNN. This approach is also analyzed and discussed, as it allowed us to achieve better results than those available in the literature.his work had the support of the Association of the Blind and Amblyopes of Portugal (ACAPO) and the Association of Support for the Visually Impaired of Braga (AADVDB). Their considerations were essential in obtaining key insights into a viable solution for the blind community.Multidisciplinary Digital Publishing InstituteUniversidade do MinhoRocha, DanielSoares, FilomenaOliveira, EvaCarvalho, Vítor2023-02-022023-02-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/85137engRocha, D.; Soares, F.; Oliveira, E.; Carvalho, V. Blind People: Clothing Category Classification and Stain Detection Using Transfer Learning. Appl. Sci. 2023, 13, 1925. https://doi.org/10.3390/app130319252076-341710.3390/app13031925https://www.mdpi.com/2076-3417/13/3/1925info: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:RCAAP2023-07-21T12:36:22Zoai:repositorium.sdum.uminho.pt:1822/85137Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:32:28.360732Repositório 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 |
Blind people: clothing category classification and stain detection using transfer learning |
title |
Blind people: clothing category classification and stain detection using transfer learning |
spellingShingle |
Blind people: clothing category classification and stain detection using transfer learning Rocha, Daniel blind people clothing recognition stain detection transfer learning deep learning Science & Technology |
title_short |
Blind people: clothing category classification and stain detection using transfer learning |
title_full |
Blind people: clothing category classification and stain detection using transfer learning |
title_fullStr |
Blind people: clothing category classification and stain detection using transfer learning |
title_full_unstemmed |
Blind people: clothing category classification and stain detection using transfer learning |
title_sort |
Blind people: clothing category classification and stain detection using transfer learning |
author |
Rocha, Daniel |
author_facet |
Rocha, Daniel Soares, Filomena Oliveira, Eva Carvalho, Vítor |
author_role |
author |
author2 |
Soares, Filomena Oliveira, Eva Carvalho, Vítor |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Rocha, Daniel Soares, Filomena Oliveira, Eva Carvalho, Vítor |
dc.subject.por.fl_str_mv |
blind people clothing recognition stain detection transfer learning deep learning Science & Technology |
topic |
blind people clothing recognition stain detection transfer learning deep learning Science & Technology |
description |
The ways in which people dress, as well as the styles that they prefer for different contexts and occasions, are part of their identity. Every day, blind people face limitations in identifying and inspecting their garments, and dressing can be a difficult and stressful task. Taking advantage of the great technological advancements, it becomes of the utmost importance to minimize, as much as possible, the limitations of a blind person when choosing garments. Hence, this work aimed at categorizing and detecting the presence of stains on garments, using artificial intelligence algorithms. In our approach, transfer learning was used for category classification, where a benchmark was performed between convolutional neural networks (CNNs), with the best model achieving an F1 score of 91%. Stain detection was performed through the fine tuning of a deep learning object detector, i.e., the mask R (region-based)-CNN. This approach is also analyzed and discussed, as it allowed us to achieve better results than those available in the literature. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-02-02 2023-02-02T00:00:00Z |
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 |
https://hdl.handle.net/1822/85137 |
url |
https://hdl.handle.net/1822/85137 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Rocha, D.; Soares, F.; Oliveira, E.; Carvalho, V. Blind People: Clothing Category Classification and Stain Detection Using Transfer Learning. Appl. Sci. 2023, 13, 1925. https://doi.org/10.3390/app13031925 2076-3417 10.3390/app13031925 https://www.mdpi.com/2076-3417/13/3/1925 |
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 |
Multidisciplinary Digital Publishing Institute |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799132837112184832 |