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Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5
COVID-19 is highly contagious, and proper wearing of a mask can hinder the spread of the virus. However, complex factors in natural scenes, including occlusion, dense, and small-scale targets, frequently lead to target misdetection and missed detection. To address these issues, this paper proposes a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269836/ https://www.ncbi.nlm.nih.gov/pubmed/35808418 http://dx.doi.org/10.3390/s22134933 |
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author | Guo, Shuyi Li, Lulu Guo, Tianyou Cao, Yunyu Li, Yinlei |
author_facet | Guo, Shuyi Li, Lulu Guo, Tianyou Cao, Yunyu Li, Yinlei |
author_sort | Guo, Shuyi |
collection | PubMed |
description | COVID-19 is highly contagious, and proper wearing of a mask can hinder the spread of the virus. However, complex factors in natural scenes, including occlusion, dense, and small-scale targets, frequently lead to target misdetection and missed detection. To address these issues, this paper proposes a YOLOv5-based mask-wearing detection algorithm, YOLOv5-CBD. Firstly, the Coordinate Attention mechanism is introduced into the feature fusion process to stress critical features and decrease the impact of redundant features after feature fusion. Then, the original feature pyramid network module in the feature fusion module was replaced with a weighted bidirectional feature pyramid network to achieve efficient bidirectional cross-scale connectivity and weighted feature fusion. Finally, we combined Distance Intersection over Union with Non-Maximum Suppression to improve the missed detection of overlapping targets. Experiments show that the average detection accuracy of the YOLOv5-CBD model is 96.7%—an improvement of 2.1% compared to the baseline model (YOLOv5). |
format | Online Article Text |
id | pubmed-9269836 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92698362022-07-09 Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5 Guo, Shuyi Li, Lulu Guo, Tianyou Cao, Yunyu Li, Yinlei Sensors (Basel) Article COVID-19 is highly contagious, and proper wearing of a mask can hinder the spread of the virus. However, complex factors in natural scenes, including occlusion, dense, and small-scale targets, frequently lead to target misdetection and missed detection. To address these issues, this paper proposes a YOLOv5-based mask-wearing detection algorithm, YOLOv5-CBD. Firstly, the Coordinate Attention mechanism is introduced into the feature fusion process to stress critical features and decrease the impact of redundant features after feature fusion. Then, the original feature pyramid network module in the feature fusion module was replaced with a weighted bidirectional feature pyramid network to achieve efficient bidirectional cross-scale connectivity and weighted feature fusion. Finally, we combined Distance Intersection over Union with Non-Maximum Suppression to improve the missed detection of overlapping targets. Experiments show that the average detection accuracy of the YOLOv5-CBD model is 96.7%—an improvement of 2.1% compared to the baseline model (YOLOv5). MDPI 2022-06-29 /pmc/articles/PMC9269836/ /pubmed/35808418 http://dx.doi.org/10.3390/s22134933 Text en © 2022 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 Guo, Shuyi Li, Lulu Guo, Tianyou Cao, Yunyu Li, Yinlei Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5 |
title | Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5 |
title_full | Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5 |
title_fullStr | Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5 |
title_full_unstemmed | Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5 |
title_short | Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5 |
title_sort | research on mask-wearing detection algorithm based on improved yolov5 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269836/ https://www.ncbi.nlm.nih.gov/pubmed/35808418 http://dx.doi.org/10.3390/s22134933 |
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