Application of Deep-Learning Methods to Real Time Face Mask Detection

Autores
González Dondo, Diego; Redolfi, Javier Andrés; García, Daiana; Araguás, Roberto Gastón
Año de publicación
2021
Idioma
español castellano
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Due to the high rate of infection and the lack of a specific vaccine or medication for the new disease known as SARS-CoV2, the World Health Organization (WHO) has recommended the use of Personal Protective Equipment (PPE) as the main measure to avoid or reduce infections. One way to maximize compliance with this recommendation is through an automatic system that can recognize in real time whether a person is correctly using the corresponding PPE. This work presents the design, implementation and performance analysis of a system for recognizing the use of masks from image sequences, with the ability to operate in real time. Based on a generic object detection network, a training scheme is proposed for a detector of faces with masks and faces without masks, wherewith an average detection accuracy higher than 90% is obtained. This accuracy can be improved by using a network with a greater number of parameters, but with a longer computation time. The performance of the detector is validated with video sequences of people with and without facemasks, captured in different environments.
Fil: González Dondo, Diego. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina
Fil: Redolfi, Javier Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina. Universidad Tecnológica Nacional. Facultad Regional San Francisco; Argentina
Fil: García, Daiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Microbiología e Inmunología. Cátedra de Ecología Microbiana; Argentina
Fil: Araguás, Roberto Gastón. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina
Fuente
https://latamt.ieeer9.org/index.php/transactions/issue/view/37
Materia
Facemask detection
EPP detection
Neural Network
TinyYOLO
COVID-19
SARS-CoV2
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/136969
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Application of Deep-Learning Methods to Real Time Face Mask DetectionGonzález Dondo, DiegoRedolfi, Javier AndrésGarcía, DaianaAraguás, Roberto GastónFacemask detectionEPP detectionNeural NetworkTinyYOLOCOVID-19SARS-CoV2https://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Due to the high rate of infection and the lack of a specific vaccine or medication for the new disease known as SARS-CoV2, the World Health Organization (WHO) has recommended the use of Personal Protective Equipment (PPE) as the main measure to avoid or reduce infections. One way to maximize compliance with this recommendation is through an automatic system that can recognize in real time whether a person is correctly using the corresponding PPE. This work presents the design, implementation and performance analysis of a system for recognizing the use of masks from image sequences, with the ability to operate in real time. Based on a generic object detection network, a training scheme is proposed for a detector of faces with masks and faces without masks, wherewith an average detection accuracy higher than 90% is obtained. This accuracy can be improved by using a network with a greater number of parameters, but with a longer computation time. The performance of the detector is validated with video sequences of people with and without facemasks, captured in different environments.Fil: González Dondo, Diego. Universidad Tecnológica Nacional. Facultad Regional Córdoba; ArgentinaFil: Redolfi, Javier Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina. Universidad Tecnológica Nacional. Facultad Regional San Francisco; ArgentinaFil: García, Daiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Microbiología e Inmunología. Cátedra de Ecología Microbiana; ArgentinaFil: Araguás, Roberto Gastón. Universidad Tecnológica Nacional. Facultad Regional Córdoba; ArgentinaInstitute of Electrical and Electronics Engineers2021-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/136969González Dondo, Diego; Redolfi, Javier Andrés; García, Daiana; Araguás, Roberto Gastón; Application of Deep-Learning Methods to Real Time Face Mask Detection; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 19; 6; 6-2021; 994-10011548-0992CONICET DigitalCONICEThttps://latamt.ieeer9.org/index.php/transactions/issue/view/37reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicasspainfo:eu-repo/semantics/altIdentifier/url/https://latamt.ieeer9.org/index.php/transactions/article/view/4378/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/2025-09-29T09:37:49Zoai:ri.conicet.gov.ar:11336/136969instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:37:49.286CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Application of Deep-Learning Methods to Real Time Face Mask Detection
title Application of Deep-Learning Methods to Real Time Face Mask Detection
spellingShingle Application of Deep-Learning Methods to Real Time Face Mask Detection
González Dondo, Diego
Facemask detection
EPP detection
Neural Network
TinyYOLO
COVID-19
SARS-CoV2
title_short Application of Deep-Learning Methods to Real Time Face Mask Detection
title_full Application of Deep-Learning Methods to Real Time Face Mask Detection
title_fullStr Application of Deep-Learning Methods to Real Time Face Mask Detection
title_full_unstemmed Application of Deep-Learning Methods to Real Time Face Mask Detection
title_sort Application of Deep-Learning Methods to Real Time Face Mask Detection
dc.creator.none.fl_str_mv González Dondo, Diego
Redolfi, Javier Andrés
García, Daiana
Araguás, Roberto Gastón
author González Dondo, Diego
author_facet González Dondo, Diego
Redolfi, Javier Andrés
García, Daiana
Araguás, Roberto Gastón
author_role author
author2 Redolfi, Javier Andrés
García, Daiana
Araguás, Roberto Gastón
author2_role author
author
author
dc.subject.none.fl_str_mv Facemask detection
EPP detection
Neural Network
TinyYOLO
COVID-19
SARS-CoV2
topic Facemask detection
EPP detection
Neural Network
TinyYOLO
COVID-19
SARS-CoV2
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Due to the high rate of infection and the lack of a specific vaccine or medication for the new disease known as SARS-CoV2, the World Health Organization (WHO) has recommended the use of Personal Protective Equipment (PPE) as the main measure to avoid or reduce infections. One way to maximize compliance with this recommendation is through an automatic system that can recognize in real time whether a person is correctly using the corresponding PPE. This work presents the design, implementation and performance analysis of a system for recognizing the use of masks from image sequences, with the ability to operate in real time. Based on a generic object detection network, a training scheme is proposed for a detector of faces with masks and faces without masks, wherewith an average detection accuracy higher than 90% is obtained. This accuracy can be improved by using a network with a greater number of parameters, but with a longer computation time. The performance of the detector is validated with video sequences of people with and without facemasks, captured in different environments.
Fil: González Dondo, Diego. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina
Fil: Redolfi, Javier Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina. Universidad Tecnológica Nacional. Facultad Regional San Francisco; Argentina
Fil: García, Daiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Departamento de Microbiología e Inmunología. Cátedra de Ecología Microbiana; Argentina
Fil: Araguás, Roberto Gastón. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina
description Due to the high rate of infection and the lack of a specific vaccine or medication for the new disease known as SARS-CoV2, the World Health Organization (WHO) has recommended the use of Personal Protective Equipment (PPE) as the main measure to avoid or reduce infections. One way to maximize compliance with this recommendation is through an automatic system that can recognize in real time whether a person is correctly using the corresponding PPE. This work presents the design, implementation and performance analysis of a system for recognizing the use of masks from image sequences, with the ability to operate in real time. Based on a generic object detection network, a training scheme is proposed for a detector of faces with masks and faces without masks, wherewith an average detection accuracy higher than 90% is obtained. This accuracy can be improved by using a network with a greater number of parameters, but with a longer computation time. The performance of the detector is validated with video sequences of people with and without facemasks, captured in different environments.
publishDate 2021
dc.date.none.fl_str_mv 2021-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/136969
González Dondo, Diego; Redolfi, Javier Andrés; García, Daiana; Araguás, Roberto Gastón; Application of Deep-Learning Methods to Real Time Face Mask Detection; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 19; 6; 6-2021; 994-1001
1548-0992
CONICET Digital
CONICET
url http://hdl.handle.net/11336/136969
identifier_str_mv González Dondo, Diego; Redolfi, Javier Andrés; García, Daiana; Araguás, Roberto Gastón; Application of Deep-Learning Methods to Real Time Face Mask Detection; Institute of Electrical and Electronics Engineers; IEEE Latin America Transactions; 19; 6; 6-2021; 994-1001
1548-0992
CONICET Digital
CONICET
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://latamt.ieeer9.org/index.php/transactions/article/view/4378/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv https://latamt.ieeer9.org/index.php/transactions/issue/view/37
reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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