ArticleSensors (Basel, Switzerland)2022
Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Public announcements and mask use during COVID-19 bridging regression and machine learning.Scientific reports · 2026Article
- Empowering precision public health control: a robust intelligent mask-wearing monitoring framework for complex crowd scenarios.BMC public health · 2026Article
- Leveraging artificial intelligence to promote COVID-19 appropriate behaviour in a healthcare institution from north India: A feasibility study.The Indian journal of medical research · 2025Article
- A method of identification and localization of tea buds based on lightweight improved YOLOV5.Frontiers in plant science · 2024Article
- Real-Time Detection of an Undercarriage Based on Receptive Field Blocks and Coordinate Attention.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
Funding
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Abstract
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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Registered trials
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