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FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public()

Coronavirus disease 2019 (COVID-19) is a world-wide epidemic and efficient prevention and control of this disease has become the focus of global scientific communities. In this paper, a novel face mask detection framework FMD-Yolo is proposed to monitor whether people wear masks in a right way in pu...

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Autores principales: Wu, Peishu, Li, Han, Zeng, Nianyin, Li, Fengping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8612756/
https://www.ncbi.nlm.nih.gov/pubmed/34848910
http://dx.doi.org/10.1016/j.imavis.2021.104341
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author Wu, Peishu
Li, Han
Zeng, Nianyin
Li, Fengping
author_facet Wu, Peishu
Li, Han
Zeng, Nianyin
Li, Fengping
author_sort Wu, Peishu
collection PubMed
description Coronavirus disease 2019 (COVID-19) is a world-wide epidemic and efficient prevention and control of this disease has become the focus of global scientific communities. In this paper, a novel face mask detection framework FMD-Yolo is proposed to monitor whether people wear masks in a right way in public, which is an effective way to block the virus transmission. In particular, the feature extractor employs Im-Res2Net-101 which combines Res2Net module and deep residual network, where utilization of hierarchical convolutional structure, deformable convolution and non-local mechanisms enables thorough information extraction from the input. Afterwards, an enhanced path aggregation network En-PAN is applied for feature fusion, where high-level semantic information and low-level details are sufficiently merged so that the model robustness and generalization ability can be enhanced. Moreover, localization loss is designed and adopted in model training phase, and Matrix NMS method is used in the inference stage to improve the detection efficiency and accuracy. Benchmark evaluation is performed on two public databases with the results compared with other eight state-of-the-art detection algorithms. At IoU = 0.5 level, proposed FMD-Yolo has achieved the best precision AP50 of 92.0% and 88.4% on the two datasets, and AP75 at IoU = 0.75 has improved 5.5% and 3.9% respectively compared with the second one, which demonstrates the superiority of FMD-Yolo in face mask detection with both theoretical values and practical significance.
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spelling pubmed-86127562021-11-26 FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public() Wu, Peishu Li, Han Zeng, Nianyin Li, Fengping Image Vis Comput Article Coronavirus disease 2019 (COVID-19) is a world-wide epidemic and efficient prevention and control of this disease has become the focus of global scientific communities. In this paper, a novel face mask detection framework FMD-Yolo is proposed to monitor whether people wear masks in a right way in public, which is an effective way to block the virus transmission. In particular, the feature extractor employs Im-Res2Net-101 which combines Res2Net module and deep residual network, where utilization of hierarchical convolutional structure, deformable convolution and non-local mechanisms enables thorough information extraction from the input. Afterwards, an enhanced path aggregation network En-PAN is applied for feature fusion, where high-level semantic information and low-level details are sufficiently merged so that the model robustness and generalization ability can be enhanced. Moreover, localization loss is designed and adopted in model training phase, and Matrix NMS method is used in the inference stage to improve the detection efficiency and accuracy. Benchmark evaluation is performed on two public databases with the results compared with other eight state-of-the-art detection algorithms. At IoU = 0.5 level, proposed FMD-Yolo has achieved the best precision AP50 of 92.0% and 88.4% on the two datasets, and AP75 at IoU = 0.75 has improved 5.5% and 3.9% respectively compared with the second one, which demonstrates the superiority of FMD-Yolo in face mask detection with both theoretical values and practical significance. Elsevier B.V. 2022-01 2021-11-25 /pmc/articles/PMC8612756/ /pubmed/34848910 http://dx.doi.org/10.1016/j.imavis.2021.104341 Text en © 2021 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Wu, Peishu
Li, Han
Zeng, Nianyin
Li, Fengping
FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public()
title FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public()
title_full FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public()
title_fullStr FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public()
title_full_unstemmed FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public()
title_short FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public()
title_sort fmd-yolo: an efficient face mask detection method for covid-19 prevention and control in public()
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8612756/
https://www.ncbi.nlm.nih.gov/pubmed/34848910
http://dx.doi.org/10.1016/j.imavis.2021.104341
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