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
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- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/136969
Ver los metadatos del registro completo
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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/2025-10-22T11:06:00Zoai: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:34982025-10-22 11:06:00.382CONICET 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 |
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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. |
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2021 |
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2021-06 |
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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 |
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http://hdl.handle.net/11336/136969 |
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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 |
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