A Survey on Face Mask Detection and Social Distancing Measurement Using Machine Learning (original) (raw)
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IRJET, 2022
The aftermath of the Covid-19 pandemic has ushered in a new normal, forcing numerous unavoidable changes in social life around the world. Wearing a face mask and maintaining social distance, especially in crowded settings, was made compulsory by the government, some disobeyed the orders, resulting in a rise in covid cases. Current work presents the study of machine learning algorithms for a face mask and social distance detection at crowded places during the pandemic. It suggests a system architecture that supports functions like detecting whether people are wearing masks (partially/completely) and identifying whether they are following social distancing. Study comprises of 3 modules-face detection, mask detection, and social distance detection. YOLO (You Only Look Once) algorithm is used for Object Detection as well as Object Tracking, because of its highest accuracy and precision achieved to date amongst prevalent techniques. It detects the people and their faces in the frame for counting the objects and keeps a record of those objects in the next frame using Object Tracking. The custom MAFA dataset is used to understand facemasks. The CNN (Convolutional neural network) Algorithm will be used to train the model on those datasets for classification and detection. Implementation of the application will be done in python and efficiency and accuracy are calculated. A notification is sent if the separation between the two entities is below the standard specifications. Faces, both masked and unmasked, are labelled appropriately.
IRJET- COVID-19 Face Mask and Social Distancing Detector using Machine Learning
IRJET, 2021
Coronavirus disease that hit us in 2020 is affecting humanity drastically. The only safety measure that we may take against this pandemic is to wear "Face Mask" in public areas and maintain "Social Distancing". Furthermore, many service providers require customers to use the service only if they wear Mask correctly and maintain social distance, the places include airports, hotels, hospitals, railway stations etc. It's not possible to examine manually at all at ones to look if the rule of wearing Mask and social distance is followed as it consumes high human resources. We proposed COVID-19 Face Mask and Social Distancing Detector System which is a onestage detector, which consists of an artificial neural network to fuse high-level semantic information with multiple feature maps, and a machine learning module to focus on detecting face Mask and social distances simultaneously. In addition, the system will use existing IP cameras and CCTV cameras combined with computer vision to detect people without Mask and violence of social distancing. This system provides tools for safety and security without any need for manual surveillance system. The system can be deployed on any infrastructure like Hospitals, Office Premises, Government Offices, Schools and Education Institutes, Construction sites, Airports etc. If deployed correctly, the face mask and social distance detector system we are building could potentially be used to help ensure people safety and the security of others.
COVID-19 Face Mask and Social Distancing Detector using Machine Learning
2021
Coronavirus disease that hit us in 2020 is affecting humanity drastically. The only safety measure that we may take against this pandemic is to wear “Face Mask” in public areas and maintain “Social Distancing”. Furthermore, many service providers require customers to use the service only if they wear Mask correctly and maintain social distance, the places include airports, hotels, hospitals, railway stations etc. It's not possible to examine manually at all at ones to look if the rule of wearing Mask and social distance is followed as it consumes high human resources. We proposed COVID-19 Face Mask and Social Distancing Detector System which is a onestage detector, which consists of an artificial neural network to fuse high-level semantic information with multiple feature maps, and a machine learning module to focus on detecting face Mask and social distances simultaneously. In addition, the system will use existing IP cameras and CCTV cameras combined with computer vision to de...
Face Mask Detection and Social Distancing Monitoring
International Journal for Research in Applied Science & Engineering Technology, 2021
During pandemic COVID-19, the World Health Organization (WHO) reports suggest that the two main routes of transmission of the COVID-19 virus are respiratory droplets and physical contact. Respiratory droplets are generated when an infected person coughs or sneezes. Any person in close contact (within 1 m) with someone who has respiratory symptoms (coughing, sneezing) is at risk of being exposed to potentially infective respiratory droplets. Droplets may also land on surfaces where the virus could remain viable; thus, the immediate environment of an infected individual can serve as a source of transmission (contact transmission). Wearing a medical mask and social distancing is one of the prevention measures that can limit the spread of certain respiratory viral diseases, including COVID-19. World Health Organization has made wearing masks and also social distancing compulsory to protect against this deadly virus. This paper is about we developed a generic Deep Neural Network-Based model for mask detection, tracking using cameras and also we developed second application for social distancing monitoring application with the help of Python and Computer Vision.
Social Distancing and Face Mask Detection Using Deep Learning
2021
The COVID-19 Pandemic caused by the new Coronavirus is the cause of this 21st-century global health crisis. It has forced the government to impose a lockdown to prevent the transmission of the virus. This led to the unprecedented shutdown of economic activities. The health care system is in crisis. Many different types of safety measures are being taken in order to reduce the risk of the spread of this disease at unprecedented times. Verified reports from renowned scientists and medial health practitioner indicated that wearing a face mask and maintaining social distance reduces the risk of transmitting the virus. Hence, we decided on an approach that is effective and economic by using deep learning techniques to create a safe environment in setups such as manufacturing plants, markets, malls, and other such places. To demonstrate our approach, the training dataset is composed of people, the images with and without the masks, which are collected from a variety of sources and use it ...
IRJET- Social Distancing and Mask Detection by using Deep Learning
IRJET, 2021
Without a doubt, the COVID-19 pandemic has brought the world to a halt. A few months ago, we lived in a very different world than we do now. The virus is spreading quickly and poses a serious threat to humanity. Since there are no vaccines available, psychological distancing is the only effective method for fighting the pandemic. Because of the seriousness of the case, certain precautions must be taken at all times, the most important of which is social distancing and mask detection. Masks must be worn and social distance maintained during COVID-19 to ensure a slowdown in the rate of new cases. Our study aims to see if those in the vicinity maintain social distance by wearing masks. Detecting a person's frame and displaying labels using our self-developed model, Socialdistancing-20; they are identified as a mask or no mask if the distance is less than a certain value, and voice module warns if the distance is less than a certain value. To separate humans from the background, the proposed framework uses the YOLO v3 object detection model, as well as the Deepsort method to monitor recognized individuals using bounding boxes. Via CCTV video surveillance, this system can be used to keep an eye on people.
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The spread of the Coronavirus has prompted individuals to remain indoors and adhere to COVID-appropriate practices, which include social distance, the use of face masks, hand sanitizers, and other measures to protect themselves against infection. In heavily populated locations with limited resources, it is impossible to physically supervise compliance with these standards. As a result, an automated, lightweight, and powerful video monitoring system is required to make the process more efficient. This paper proposes an extensive and productive solution for performing person detection, social distance detection, face mask detection, and face mask classification using object detection, clustering, and Convolution Neural Networks (CNN) On video datasets, in addition to YOLOv3, density-based spatial clustering of applications with noise (DBSCAN), Dual Shot Face Detector (DSFD), and MobileNetV2 based binary classifier other techniques have also been used to achieve the predicted outcomes. This study also provides parallels for numerous face mask detection and classification models.
2021 International Conference on Emerging Smart Computing and Informatics (ESCI), 2021
In the current times, the fear and danger of COVID-19 virus still stands large. Manual monitoring of social distancing norms is impractical with a large population moving about and with insufficient task force and resources to administer them. There is a need for a lightweight, robust and 24X7 video-monitoring system that automates this process. This paper proposes a comprehensive and effective solution to perform person detection, social distancing violation detection, face detection and face mask classification using object detection, clustering and Convolution Neural Network (CNN) based binary classifier. For this, YOLOv3, Density-based spatial clustering of applications with noise (DBSCAN), Dual Shot Face Detector (DSFD) and MobileNetV2 based binary classifier have been employed on surveillance video datasets. This paper also provides a comparative study of different face detection and face mask classification models. Finally, a video dataset labelling method is proposed along w...
IJERT-Social Distancing and Face Mask Detection From CCTV Camera
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/social-distancing-and-face-mask-detection-from-cctv-camera https://www.ijert.org/research/social-distancing-and-face-mask-detection-from-cctv-camera-IJERTV10IS080142.pdf The novel coronavirus also called Covid-19 had a huge effect on different sectors in many territories and imposed governments across the world to urge lockdowns to avert novel coronavirus transmission. This little step of wearing a face mask, following social distancing would save plenty of lives as the spread of the novel coronavirus could be mitigated. This theme consists of social distancing noticing and face mask detection for the events of disease like novel coronavirus can be solved by maintaining social distancing as well as wearing/putting on its face mask. This used to develop a Mask Detection using OpenCV, Keras/TensorFlow and also Deep Learning. This System can easily integrated/implemented to various embedded devices with limited computational capacity that uses MobileNetV2 architecture. System will detect face masks in photos/images and in real-time videos.
Face Mask and Social Distance Detection
IRJET, 2022
Increasing numbers of cases of COVID-19 provide insight into the pandemic's spread.However, wearing the face mask in order to prevent the transmission of droplets in air and maintaining appropriate physical distance between the people can help to fight this pandemic. This project is helpful in detecting face masks and social distancing on a video feed using object detection and Deep learning. OpenCV, TensorFlow/Keras are the software requirements used to build a Convolutional Neural Network (CNN) model to detect face masks. Face masks will be detected in real-time video and in images. CNN algorithm is used for object detection, image classification and recognition and the specialty of this algorithm is its convolutional ability. YOLO algorithm is popular because of its speed and accuracy of detection of objects and YOLO algorithm abbreviated as "You Only Look Once". This YOLO algorithm detects the people in frame and check the social distancing.