Real-Time Face Mask Detection to Prevent COVID-19 in Confined Spaces (original) (raw)
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Prototype for Integration of Face Mask Detection and Person Identification Model – COVID-19
2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2020
As people across the globe are combating the widespread COVID-19 pandemic and it becomes very essential to develop new technologies to analyse and fight against the disease spread. The most essential protection against corona virus is Face Mask and as the day surpasses scientist and Doctors have recommended everyone to wear the mask. Therefore, to distinguish the individuals wearing Face Mask, various identification procedures are available. Veils are prescribed as a straightforward obstruction to protect the respiratory beads from going into the air and onto others, when the individual is found to be wearing the cover hacks, wheezes, talks, or raises their voice. Moreover, this is called source control. This proposal depends on the present idea about the job respiratory beads that play a main role in the spread of the COVID-19 infection, matched with developing proof from clinical and research center examinations that show covers and decrease the splash of drops, when worn over the nose and mouth. Coronavirus spreads essentially among individuals who are in close contact with each other (inside around 6 feet), so the utilization of veils is especially significant in settings where individuals are near one another or where social removing is hard to keep up. CDC's suggestions for masks will be updated as new logical proof. Our project is more of a real-world application, the proposed face mask detection platform utilizes artificial network to identify the person with and without mask. If a person is not wearing a mask, then the proposed platform will send a notification to the person if he or she is in the database of the platform. MobileNet_V2 neural networks are used as our classification algorithm and the face recognition module is also used for the person identification model
Real Time Face-mask Detection with Arduino to Prevent COVID-19 Spreading
Qubahan Academic Journal, 2021
The rise of COVID-19 pandemic has had a lasting impact in many countries worldwide since 2019. Face-mask detection had been significant progress in the Image processing and deep learning fields studies. Many face detection models have been designed using different algorithms and techniques. The proposed approach in this paper developed to avoid mask-less people from entering to a desired places (i.e. Mall, University, Office, …etc.) by detecting face mask using deep learning, TensorFlow, Keras, and OpenCV and sending a signal to Arduino device that connected to the gate to be open. it detect a face in a real-time and identifies whether the person wear mask or not. The method attains accuracy up to 97.80%. The dataset provided in this paper, was collected from various sources.
Traitement du Signal
The COVID-19 pandemic continues to spread around the world at full speed, threatening public health. In response, the World Health Organization recommends various preventive measures to reduce the spread of the COVID-19 virus. Wearing a mask is one of the preventive measures to reduce the contagion of the disease, and many governments around the world advise people to wear masks. One of the prominent symptoms of coronavirus is high fever. A person with a fever above normal is likely to have contracted the corona virus. This requires the identification of people with a high fever in order to prevent the epidemic in the public arena. This situation has caused people who want to enter public places to need masks and officers who control their body temperature. The aim of this study is to detect people who do not wear masks or do not wear them properly, and also to detect people with high fever through a system. The proposed system is designed as a system that can be integrated into aut...
Face Mask Detection Using MobileNetV2 in The Era of COVID-19 Pandemic
2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 2020
Corona Virus Disease (COVID-19) pandemic is causing a health crisis. One of the effective methods against the virus is wearing a face mask. This paper introduces face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID-19. The face mask recognition in this study is developed with a machine learning algorithm through the image classification method: MobileNetV2. The steps for building the model are collecting the data, pre-processing, split the data, testing the model, and implement the model. The built model can detect people who are wearing a face mask and not wearing it at an accuracy of 96,85 percent. After the model implemented in 25 cities from various source of image, the percentage of people wearing face mask in the cities has a strong correlation to the vigilance index of COVID-19 which is 0,62.
Face mask detection and social distance monitoring system for COVID-19 pandemic
Multimedia Tools and Applications, 2022
Coronavirus triggers several respirational infections such as sneezing, coughing, and pneumonia, which transmit humans to humans through airborne droplets. According to the guidelines of the World Health Organization, the spread of COVID-19 can be mitigated by avoiding public interactions in proximity and following standard operating procedures (SOPs) including wearing a face mask and maintaining social distancing in schools, shopping malls, and crowded areas. However, enforcing the adaptation of these SOPs on a larger scale is still a challenging task. With the emergence of deep learning-based visual object detection networks, numerous methods have been proposed to perform face mask detection on public spots. However, these methods require a huge amount of data to ensure robustness in real-time applications. Also, to the best of our knowledge, there is no standard outdoor surveillance-based dataset available to ensure the efficacy of face mask detection and social distancing methods in public spots. To this end, we present a large-scale dataset comprising of 10,000 outdoor images categorized into a binary class labeling i.e., face mask, and non-face masked people to accelerate the development of automated face mask detection and social distance measurement on public spots. Alongside, we also present an end-to-end pipeline to perform real-time face mask detection and social distance measurement in an outdoor environment. Initially, existing state-of-the-art single and multi-stage object detection networks are fine-tuned on the proposed dataset to evaluate their performance in terms of accuracy and inference time. Based on better performance, YOLO-v3 architecture is further optimized by tuning its feature extraction and region proposal generation layers to improve the performance in real-time applications. Our results indicate that the presented pipeline performed better than the baseline version, showing an improvement of 5.3% in terms of accuracy.
Enhanced Real-Time Detection of Face Mask with Alarm System Using MOBILENETV2
EPH - International Journal of Science And Engineering
To prevent the coronavirus from spreading, the government adopted measures such as wearing a face mask in public locations. The researchers aimed to create a face detection system using the MobilenetV2 architecture that would identify a person’s faces and determine whether they were wearing a face mask. The built model will help to reduce the danger of viral transmission. In this study, face mask detection is achieved using a machine learning algorithm and the classification method using MobileNetV2. The steps for building the model are data gathering, data pre-processing, splitting the data, testing the model, and implementing the model. The built model can distinguish between those who are wearing a face mask (with no design patterns) and those who are not wearing it with a 96% accuracy. In terms of classification accuracy, the proposed model using MobileNetV2 outperformed the other models LeNet-5, AlexNet, and ResNet-50. If the detected person is labeled with “no mask”, the syst...
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.
Mask Detection System for COVID-19 Scenario Using Computer Vision
2021
The new pandemic of (Coronavirus Disease-2019) COVID-19 continues to spread worldwide. Every potential sector is experiencing a decline in growth. (World Health Organization) WHO suggests that Wearing Face Mask can reduce the impact of COVID-19. So, This Paper Proposed a system that controls the growth of COVID-19 by finding individuals who don't wear masks in populated areas like malls, markets where all public places are under surveillance with closed-circuit television cameras (CCTV). When a person without a mask is found, the corresponding authority is informed by the CCTV network. And it can calculate the number of people that do not wear the mask and emit an audible signal to inform the authority. A deep learning module is trained on a dataset composed of images of people wearing different types of masks and people without masks collected from various sources. It also contains some confusing images that help the model to achieve greater precision than other models. This mo...
Scientific Programming
Since the infectious coronavirus disease (COVID-19) was first reported in Wuhan, it has become a public health problem in China and even around the world. This pandemic is having devastating effects on societies and economies around the world. The increase in the number of COVID-19 tests gives more information about the epidemic spread, which may lead to the possibility of surrounding it to prevent further infections. However, wearing a face mask that prevents the transmission of droplets in the air and maintaining an appropriate physical distance between people, and reducing close contact with each other can still be beneficial in combating this pandemic. Therefore, this research paper focuses on implementing a Face Mask and Social Distancing Detection model as an embedded vision system. The pretrained models such as the MobileNet, ResNet Classifier, and VGG are used in our context. People violating social distancing or not wearing masks were detected. After implementing and deploy...
An Automated System to Limit COVID-19 Using Facial Mask Detection in Smart City Network
2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)
COVID-19 pandemic caused by novel coronavirus is continuously spreading until now all over the world. The impact of COVID-19 has been fallen on almost all sectors of development. The healthcare system is going through a crisis. Many precautionary measures have been taken to reduce the spread of this disease where wearing a mask is one of them. In this paper, we propose a system that restrict the growth of COVID-19 by finding out people who are not wearing any facial mask in a smart city network where all the public places are monitored with Closed-Circuit Television (CCTV) cameras. While a person without a mask is detected, the corresponding authority is informed through the city network. A deep learning architecture is trained on a dataset that consists of images of people with and without masks collected from various sources. The trained architecture achieved 98.7% accuracy on distinguishing people with and without a facial mask for previously unseen test data. It is hoped that our study would be a useful tool to reduce the spread of this communicable disease for many countries in the world.