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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...

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Autores principales: Guo, Shuyi, Li, Lulu, Guo, Tianyou, Cao, Yunyu, Li, Yinlei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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).
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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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