YOLOv7 for Face Mask Identification Based on Deep Learning

The World Health Organization (WHO) has publicized a global public health emergency due to the COVID-19 coronavirus pandemic. Wearing a mask in public can provide protection against the spread of disease. Tremendous progress has been made in object detection in recent times, thanks in large part to deep learning models, which have shown encouraging results when it comes to recognizing objects in images. Recent technological developments have made this progress possible. Wearing a mask in public is one way to prevent the transmission of COVID-19 from others. Our study employs You Only Look Once (YOLO) v7 to determine whether a subject is wearing a mask, and then divides them into three groups depending on the degree to which they are wearing a mask correctly (none, bad, and good). In this study, we merged two datasets, the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD), to conduct our experiment. These models' evaluations and ratings include crucial criteria. According to our data, YOLOv7 achieves the highest mAP (98.5%) in the "Good"class. © 2023 IEEE.

Face Mask Identification Using Spatial and Frequency Features in Depth Image from Time-of-Flight Camera.

Face masks can effectively prevent the spread of viruses. It is necessary to determine the wearing condition of masks in various locations, such as traffic stations, hospitals, and other places with a risk of infection. Therefore, achieving fast and accurate identification in different application scenarios is an urgent problem to be solved. Contactless mask recognition can avoid the waste of human resources and the risk of exposure. We propose a novel method for face mask recognition, which is demonstrated using the spatial and frequency features from the 3D information. A ToF camera with a simple system and robust data are used to capture the depth images. The facial contour of the depth image is extracted accurately by the designed method, which can reduce the dimension of the depth data to improve the recognition speed. Additionally, the classification process is further divided into two parts. The wearing condition of the mask is first identified by features extracted from the facial contour. The types of masks are then classified by new features extracted from the spatial and frequency curves. With appropriate thresholds and a voting method, the total recall accuracy of the proposed algorithm can achieve 96.21%. Especially, the recall accuracy for images without mask can reach 99.21%.

Detection of Face Mask: A Systematic Approach

The COVID-19 coronavirus pandemic is causing health crises around the world. According to the World Health Organization (WHO), wearing a face mask is an effective means of protection in public places. In most public gatherings such as shopping centers, theaters, parks, it is increasingly necessary to make sure that people in the crowd are wearing masks. Developing an artificial intelligence solution that determines regardless of whether an individual is wearing a cover and letting it in will be great help for the society. In this case, a simple face mask detection system is built using deep learning techniques such as machine learning and persuasive neural network. The model is built with machine learning and OpenCV libraries often used for real-time applications. This model can also be used to develop complete software that scans each person before going to a public meeting © 2022 IEEE.

Face Masks Usage Monitoring for Public Health Security using Computer Vision on Hardware

Wearing face masks is one of the direct measures that can help tackling the spread of the new coronavirus. In this paper we presented an architecture for face mask wearing detection using pre-trained deep learning models for computer vision and implementation of them on embedded hardware platforms. Three object detection models were fine-tuned and optimized to run on 4 different hardware platforms. The fine tuning and optimization of the models resulted in significant reduction of the inference time, thus making the use of this technology in IoT based security systems for real-time automatic monitoring of face masks wearing realisable. © 2021 Crown.

Real-Time Mask Identification for COVID-19: An Edge-Computing-Based Deep Learning Framework.

During the outbreak of the Coronavirus disease 2019 (COVID-19), while bringing various serious threats to the world, it reminds us that we need to take precautions to control the transmission of the virus. The rise of the Internet of Medical Things (IoMT) has made related data collection and processing, including healthcare monitoring systems, more convenient on the one hand, and requirements of public health prevention are also changing and more challengeable on the other hand. One of the most effective nonpharmaceutical medical intervention measures is mask wearing. Therefore, there is an urgent need for an automatic real-time mask detection method to help prevent the public epidemic. In this article, we put forward an edge computing-based mask (ECMask) identification framework to help public health precautions, which can ensure real-time performance on the low-power camera devices of buses. Our ECMask consists of three main stages: 1) video restoration; 2) face detection; and 3) mask identification. The related models are trained and evaluated on our bus drive monitoring data set and public data set. We construct extensive experiments to validate the good performance based on real video data, in consideration of detection accuracy and execution time efficiency of the whole video analysis, which have valuable application in COVID-19 prevention.