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ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic
During the last two years, several deep learning-based methods for face mask detection have been proposed by researchers. However, most of the proposed methods struggle with the detection of face masks that are too small an object to detect and further achieve low detection accuracy. Considering the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier GmbH.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986544/ https://www.ncbi.nlm.nih.gov/pubmed/35411120 http://dx.doi.org/10.1016/j.ijleo.2022.169051 |
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author | Kumar, Akhil Kalia, Arvind Kalia, Aayushi |
author_facet | Kumar, Akhil Kalia, Arvind Kalia, Aayushi |
author_sort | Kumar, Akhil |
collection | PubMed |
description | During the last two years, several deep learning-based methods for face mask detection have been proposed by researchers. However, most of the proposed methods struggle with the detection of face masks that are too small an object to detect and further achieve low detection accuracy. Considering the issues of the existing methods, in this work, we have proposed ETL-YOLO v4 with a modified and improved feature extraction and prediction network for tiny YOLO v4 which surpasses all its predecessors and other related work in the literature. To develop ETL-YOLO v4, we have improved the backbone architecture of tiny YOLO v4 by adding a modified-dense SPP network, two additional detection layers with modified and optimized CNN layers that aid in accurate prediction, used Mish as the activation function, and utilized modified anchor boxes. Furthermore, to obtain detection results in images of varied viewpoints, we have added Mosaic and CutMix data augmentation at training time. The proposed ETL-YOLO v4 achieved 9.93% higher mAP, 5.75% higher average precision (AP) for faces with masks, and 16.6% higher average precision (AP) for the face mask region as compared to its original base-line variant. |
format | Online Article Text |
id | pubmed-8986544 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier GmbH. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89865442022-04-07 ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic Kumar, Akhil Kalia, Arvind Kalia, Aayushi Optik (Stuttg) Article During the last two years, several deep learning-based methods for face mask detection have been proposed by researchers. However, most of the proposed methods struggle with the detection of face masks that are too small an object to detect and further achieve low detection accuracy. Considering the issues of the existing methods, in this work, we have proposed ETL-YOLO v4 with a modified and improved feature extraction and prediction network for tiny YOLO v4 which surpasses all its predecessors and other related work in the literature. To develop ETL-YOLO v4, we have improved the backbone architecture of tiny YOLO v4 by adding a modified-dense SPP network, two additional detection layers with modified and optimized CNN layers that aid in accurate prediction, used Mish as the activation function, and utilized modified anchor boxes. Furthermore, to obtain detection results in images of varied viewpoints, we have added Mosaic and CutMix data augmentation at training time. The proposed ETL-YOLO v4 achieved 9.93% higher mAP, 5.75% higher average precision (AP) for faces with masks, and 16.6% higher average precision (AP) for the face mask region as compared to its original base-line variant. Elsevier GmbH. 2022-06 2022-04-07 /pmc/articles/PMC8986544/ /pubmed/35411120 http://dx.doi.org/10.1016/j.ijleo.2022.169051 Text en © 2022 Elsevier GmbH. 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 Kumar, Akhil Kalia, Arvind Kalia, Aayushi ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic |
title | ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic |
title_full | ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic |
title_fullStr | ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic |
title_full_unstemmed | ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic |
title_short | ETL-YOLO v4: A face mask detection algorithm in era of COVID-19 pandemic |
title_sort | etl-yolo v4: a face mask detection algorithm in era of covid-19 pandemic |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986544/ https://www.ncbi.nlm.nih.gov/pubmed/35411120 http://dx.doi.org/10.1016/j.ijleo.2022.169051 |
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