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A Face Detection and Standardized Mask-Wearing Recognition Algorithm
In the era of coronavirus disease (COVID-19), wearing a mask could effectively protect people from the risk of infection and largely reduce transmission in public places. To prevent the spread of the virus, instruments are needed in public places to monitor whether people are wearing masks, which ha...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224451/ https://www.ncbi.nlm.nih.gov/pubmed/37430525 http://dx.doi.org/10.3390/s23104612 |
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author | Yu, Jimin Zhang, Xin Wu, Tao Pan, Huilan Zhang, Wei |
author_facet | Yu, Jimin Zhang, Xin Wu, Tao Pan, Huilan Zhang, Wei |
author_sort | Yu, Jimin |
collection | PubMed |
description | In the era of coronavirus disease (COVID-19), wearing a mask could effectively protect people from the risk of infection and largely reduce transmission in public places. To prevent the spread of the virus, instruments are needed in public places to monitor whether people are wearing masks, which has higher requirements for the accuracy and speed of detection algorithms. To meet the demand for high accuracy and real-time monitoring, we propose a single-stage approach based on YOLOv4 to identify the face and whether to regulate the wearing of masks. In this approach, we propose a new feature pyramidal network based on the attention mechanism to reduce the loss of object information that can be caused by sampling and pooling in convolutional neural networks. The network is able to deeply mine the feature map for spatial and communication factors, and the multi-scale feature fusion makes the feature map equipped with location and semantic information. Based on the complete intersection over union (CIoU), a penalty function based on the norm is proposed to improve positioning accuracy, which is more accurate at the detection of small objects; the new bounding box regression function is called Norm CIoU (NCIoU). This function is applicable to various object-detection bounding box regression tasks. A combination of the two functions to calculate the confidence loss is used to mitigate the problem of the algorithm bias towards determinating no objects in the image. Moreover, we provide a dataset for recognizing faces and masks (RFM) that includes 12,133 realistic images. The dataset contains three categories: face, standardized mask and non-standardized mask. Experiments conducted on the dataset demonstrate that the proposed approach achieves mAP@.5:.95 69.70% and AP75 73.80%, outperforming the compared methods. |
format | Online Article Text |
id | pubmed-10224451 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102244512023-05-28 A Face Detection and Standardized Mask-Wearing Recognition Algorithm Yu, Jimin Zhang, Xin Wu, Tao Pan, Huilan Zhang, Wei Sensors (Basel) Article In the era of coronavirus disease (COVID-19), wearing a mask could effectively protect people from the risk of infection and largely reduce transmission in public places. To prevent the spread of the virus, instruments are needed in public places to monitor whether people are wearing masks, which has higher requirements for the accuracy and speed of detection algorithms. To meet the demand for high accuracy and real-time monitoring, we propose a single-stage approach based on YOLOv4 to identify the face and whether to regulate the wearing of masks. In this approach, we propose a new feature pyramidal network based on the attention mechanism to reduce the loss of object information that can be caused by sampling and pooling in convolutional neural networks. The network is able to deeply mine the feature map for spatial and communication factors, and the multi-scale feature fusion makes the feature map equipped with location and semantic information. Based on the complete intersection over union (CIoU), a penalty function based on the norm is proposed to improve positioning accuracy, which is more accurate at the detection of small objects; the new bounding box regression function is called Norm CIoU (NCIoU). This function is applicable to various object-detection bounding box regression tasks. A combination of the two functions to calculate the confidence loss is used to mitigate the problem of the algorithm bias towards determinating no objects in the image. Moreover, we provide a dataset for recognizing faces and masks (RFM) that includes 12,133 realistic images. The dataset contains three categories: face, standardized mask and non-standardized mask. Experiments conducted on the dataset demonstrate that the proposed approach achieves mAP@.5:.95 69.70% and AP75 73.80%, outperforming the compared methods. MDPI 2023-05-10 /pmc/articles/PMC10224451/ /pubmed/37430525 http://dx.doi.org/10.3390/s23104612 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yu, Jimin Zhang, Xin Wu, Tao Pan, Huilan Zhang, Wei A Face Detection and Standardized Mask-Wearing Recognition Algorithm |
title | A Face Detection and Standardized Mask-Wearing Recognition Algorithm |
title_full | A Face Detection and Standardized Mask-Wearing Recognition Algorithm |
title_fullStr | A Face Detection and Standardized Mask-Wearing Recognition Algorithm |
title_full_unstemmed | A Face Detection and Standardized Mask-Wearing Recognition Algorithm |
title_short | A Face Detection and Standardized Mask-Wearing Recognition Algorithm |
title_sort | face detection and standardized mask-wearing recognition algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224451/ https://www.ncbi.nlm.nih.gov/pubmed/37430525 http://dx.doi.org/10.3390/s23104612 |
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