International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 04 | Apr 2022
www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
Covid Mask Detection and Social Distancing Using Raspberry pi Vinod Raut1, Suyog Raskar2, Suraj Ravande3, Rahul Laudiya4, Prof. Jayshri Palkar5 1,2,3,4 Students,
Electronics and Telecommunication Engineering, Keystone School of Engineering, Pune Professor, Electronics and Telecommunication Engineering, Keystone School of Engineering,Pune -------------------------------------------------------------------***-------------------------------------------------------------------5Assistant
Abstract - COVID -19 spreads through the air from one
People should wear a mask and Social distance this will also help to break the chain. In this situation our system Comes handy. Because of machine learning everything is easy and possible to do Our system takes like pictures and video recordings. Converts it into different frames and compare it with trained data set to do that it takes the help of CNN and YOLO algorithms. CNN (Convolutional neural network)is algorithm used to analyzer different visual imagery c and YOLO (Youonly looks once) is used to detect objects in the images. in public place Some like malls, Schools, corporate offices and many other places they have to monitor it manually monitoring social distance and checking people mask is most likely to lead to scarcity of resources and is supposed to introduced errors due to human interaction.
person to another person which is close to each other. like this, it forms a chain of infection. In order to stop coronavirus from spreading, is to break the chain of a virus by implementing Small Changes our daily life like wearing putting face mask and social distance & Sanitizing hands Our system helps to check whether a person entering into public or Corporate places have they put a mask on their faces or not and it also checks whether they are maintaining a safe distance from each other. This system Can be easily implemented on any embedded system. To implement this system we have used some algorithms like CNN(Convolutional neural network)and YOLO they are used to train the data and to detect different objects from the live image. Monitoring Social distances regulations and checking people's masks is most likely to lead to a Scarcity of resources and is supposed to allow errors creeping in due to human intervention, So In this Kind of situation, our System Comes in handy This Paper Describes the approach to prevent the increase of the Coronavirus by monitoring in any time if any person is maintaining Social distance and face masks in a public place. using machine learning this system can identify the person with mast and without mask if no mask has occurred then the System will give an alert or it will make Sound using a buzzer.
1.1 Literature Survey [1] S. S. Paima, N. Hasanzadeh, A. Jodeiri, and &H.Soltanian Zadeh, “Detection of COVID-19 from chest radiographs"in Proceedings of the 2020 27thNationaland 5th International Iranian Conference on Biomedical Engineering(ICBME), IEEE, Tehran, Iran, November 2020. This paper also provides a comparative study of different face detection and face mask classification models.The system performance is evaluated in terms of precision, support, sensitivity and accuracy that demonstrate the practical applicability.
Key Words: Machine Learning, CNN, YOLO, Opencv
1. INTRODUCTION Before December 2019, We never thought that the one Small Virus can stop the whole world and can create a panic situation all the peoples have to stop their works Schools, malls and other places where Crowd can gather together are closed for long period of time. Coronavirus is very dangerous as it is affecting the Social and economic health of the countries
[2] M. Cos¸kun, A. Uçar, O. Yildirim, and Y.Demir, “Face recognition based on convolutional neural network,” in Proceedings of the 2017 International Conference on Modern Electrical and Energy Systems ,Kremenchuk, Ukraine,November 2017. This paper proposes a modified Convolutional Neural Network architecture by adding two normalization operations to two of the layers.The batch normalization provided acceleration of the network.
.Coronaviruses is kind of virus which gets spreads when an infected person breaths out droplets and very Small particles that contain the virus. And that droplets get inhaled by other peoples and they get affected by it Same pattern gets continuous, it frames a chain of infection. So to avoid or break the chain of virus all Countries have announced lockdowns in their countries. but in order to feed the families people Can not live forever in lock down’s So. WHO (world health organization) Suggests that along with vaccinations.
[3] Sultana, Sufian, and Dutta, “ In image classification using convolutional neural network(CNN) ,” in Proceedings of the 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks , IEEE, Kolkata, India, November 2018 . In this paper, We have explained different Convolutional Neural Network architectures for image verification. Through this paper, we have shown advancements in Convolutional neural network (CNN) from LeNet-5 to latest SENet model.
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