ArticleImage and vision computing2022
FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public.
Article in Image and vision computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed.
- Adaptive collaborative feature fusion and shape-aware optimization for multi-scale chest lesion detection.Scientific reports · 2026Article
- A Review on Face Mask Recognition.Sensors (Basel, Switzerland) · 2025Review
- IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective.Frontiers in public health · 2025Article
- Object Detection and Tracking with YOLO and the Sliding Innovation Filter.Sensors (Basel, Switzerland) · 2024Article
- Application of multimodal ultrasonography to predicting the acute kidney injury risk of patients with sepsis: artificial intelligence approach.PeerJ. Computer science · 2024Article
- A novel attention-based enhancement framework for face mask detection in complicated scenarios.Signal processing. Image communication · 2023Article
- Data-Augmented Deep Learning Models for Abnormal Road Manhole Cover Detection.Sensors (Basel, Switzerland) · 2023Article
- A Unified Framework for Monitoring Social Distancing and Face Mask Wearing Using Deep Learning: An Approach to Reduce COVID-19 Risk.Procedia computer science · 2023Article
- Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches.Neural computing & applications · 2023Review
- COVID-19 risk reduce based YOLOv4-P6-FaceMask detector and DeepSORT tracker.Multimedia tools and applications · 2023Article
- Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision.Expert systems with applications · 2022Article
- Hyperspectral Face Recognition with Adaptive and Parallel SVMs in Partially Hidden Face Scenarios.Sensors (Basel, Switzerland) · 2022Article
- Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5.Sensors (Basel, Switzerland) · 2022Article
- SMD-YOLO: An efficient and lightweight detection method for mask wearing status during the COVID-19 pandemic.Computer methods and programs in biomedicine · 2022Article
- Intelligent Real-Time Face-Mask Detection System with Hardware Acceleration for COVID-19 Mitigation.Healthcare (Basel, Switzerland) · 2022Article
- Theoretical and Experimental Studies of Micro-Surface Crack Detections Based on BOTDA.Sensors (Basel, Switzerland) · 2022Article
- A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram.Scientific reports · 2022Article
- A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images.Neurocomputing · 2022Article
- A Radiomics Nomogram for Classifying Hematoma Entities in Acute Spontaneous Intracerebral Hemorrhage on Non-contrast-Enhanced Computed Tomography.Frontiers in neuroscience · 2022Article
- A fused biometrics information graph convolutional neural network for effective classification of patellofemoral pain syndrome.Frontiers in neuroscience · 2022Article
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4 authors.
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Abstract
Coronavirus disease 2019 (COVID-19) is a world-wide epidemic and efficient prevention and control of this disease has become the focus of global scientific communities. In this paper, a novel face mask detection framework FMD-Yolo is proposed to monitor whether people wear masks in a right way in public, which is an effective way to block the virus transmission. In particular, the feature extractor employs Im-Res2Net-101 which combines Res2Net module and deep residual network, where utilization of hierarchical convolutional structure, deformable convolution and non-local mechanisms enables thorough information extraction from the input. Afterwards, an enhanced path aggregation network En-PAN is applied for feature fusion, where high-level semantic information and low-level details are sufficiently merged so that the model robustness and generalization ability can be enhanced. Moreover, localization loss is designed and adopted in model training phase, and Matrix NMS method is used in the inference stage to improve the detection efficiency and accuracy. Benchmark evaluation is performed on two public databases with the results compared with other eight state-of-the-art detection algorithms. At
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