ArticleSensors (Basel, Switzerland)2020
Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.Current topics in medicinal chemistry · 2024Pooled it
- A multi-class framework for face mask compliance detection using lightweight deep learning models.Scientific reports · 2026Article
- Leveraging federated learning and edge computing for pandemic-resilient healthcare.Scientific reports · 2025Article
- 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
- Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network.Scientific reports · 2024Article
- Deep Learning-Based Biomimetic Identification Method for Mask Wearing Standardization.Biomimetics (Basel, Switzerland) · 2024Article
- An intriguing vision for transatlantic collaborative health data use and artificial intelligence development.NPJ digital medicine · 2024Review
- A hybrid human recognition framework using machine learning and deep neural networks.PloS one · 2024Article
- Multi-Scene Mask Detection Based on Multi-Scale Residual and Complementary Attention Mechanism.Sensors (Basel, Switzerland) · 2023Article
- An effective stacked autoencoder based depth separable convolutional neural network model for face mask detection.Array (New York, N.Y.) · 2023Article
- IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic.Sensors (Basel, Switzerland) · 2023Review
- IYOLO-NL: An improved you only look once and none left object detector for real-time face mask detection.Heliyon · 2023Article
- FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas.Sensors (Basel, Switzerland) · 2023Article
- Real-time face mask position recognition system based on MobileNet model.Smart health (Amsterdam, Netherlands) · 2023Article
- CoSumNet: A video summarization-based framework for COVID-19 monitoring in crowded scenes.Artificial intelligence in medicine · 2023Article
- Mask usage recognition using vision transformer with transfer learning and data augmentation.Intelligent systems with applications · 2023Article
- A survey on computer vision based human analysis in the COVID-19 era.Image and vision computing · 2023Article
- A hybrid tiny YOLO v4-SPP module based improved face mask detection vision system.Journal of ambient intelligence and humanized computing · 2023Article
- Face mask detection and social distance monitoring system for COVID-19 pandemic.Multimedia tools and applications · 2023Article
- COVID-19 risk reduce based YOLOv4-P6-FaceMask detector and DeepSORT tracker.Multimedia tools and applications · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The rapid worldwide spread of Coronavirus Disease 2019 (COVID-19) has resulted in a global pandemic. Correct facemask wearing is valuable for infectious disease control, but the effectiveness of facemasks has been diminished, mostly due to improper wearing. However, there have not been any published reports on the automatic identification of facemask-wearing conditions. In this study, we develop a new facemask-wearing condition identification method by combining image super-resolution and classification networks (SRCNet), which quantifies a three-category classification problem based on unconstrained 2D facial images. The proposed algorithm contains four main steps: Image pre-processing, facial detection and cropping, image super-resolution, and facemask-wearing condition identification. Our method was trained and evaluated on the public dataset Medical Masks Dataset containing 3835 images with 671 images of no facemask-wearing, 134 images of incorrect facemask-wearing, and 3030 images of correct facemask-wearing. Finally, the proposed SRCNet achieved 98.70% accuracy and outperformed traditional end-to-end image classification methods using deep learning without image super-resolution by over 1.5% in kappa. Our findings indicate that the proposed SRCNet can achieve high-accuracy identification of facemask-wearing conditions, thus having potential applications in epidemic prevention involving COVID-19.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.