scholarly journals Face Mask Detection with Raspberry Pi

2021 ◽  
Vol 9 (VI) ◽  
pp. 3451-3458
Author(s):  
G. Pavan Kumar
Keyword(s):  
Real Time ◽  
Close Contact ◽  
Face Mask ◽  
Raspberry Pi ◽  
Single Shot ◽  
Facial Image ◽  
Pixel Resolution ◽  
Infected Area ◽  
Spinal Network ◽  
Respiratory Droplets

In the wake of the COVID-19 epidemic, institutions such as the academy are suffering the most from global closure if the current situation haven’t rectified. COVID-19 also known as Serious Acute Respiratory Syndrome Corona virus-2 is an infectious disease that is transmitted to an infected person who talks, sneezes or coughs through respiratory droplets. This spreads quickly through close contact with anyone with the disease, or by touching objects or the infected area. By wearing a face mask under the jaws covering at crowded places or by frequently hygiene at your palms and by using at the minimum of 70% sanitizers which are based on alcohol is the best method for the against of the COVID-19. In this project we have used it ML, OpenCV and TensorFlow face recognition. This the model can be used for security purposes because of course an app that works well for use. In this way MobilenetV2 using a BN-based layout too lightweight and embedded this model with Raspberry pi to make real-time mask discovery, when, SSD (Single Shot Detector) format is used and the spinal network is light. As technology advances, Deep Learning has demonstrated its effectiveness in recognition and classification through image processing. The study uses in-depth reading techniques to distinguish facial recognition and to determine whether a person is wearing a facemask or not. The collected data contains 25,000 images using 224x224 pixel resolution and obtained 96% accuracy with the performance of a trained model. The system enhances the Raspberry Pi-based real-time recognition made by alarms and takes a facial image when the person found is not wearing a facemask. This study is beneficial in combating the spread of the virus and in avoiding contact with it.

scholarly journals SSDMNV2: A Real time DNN-Based Face Mask Detection System using Single Shot Multibox Detector and MobileNetV2

2020 ◽  
pp. 102692
Author(s):  
Preeti Nagrath ◽  
Rachna Jain ◽  
Agam Madan ◽  
Rohan Arora ◽  
Piyush Kataria ◽  
...  
Keyword(s):  
Real Time ◽  
Detection System ◽  
Face Mask ◽  
Single Shot

scholarly journals Update on COVID-19 Infection and Ivermectin Treatment

2021 ◽  
pp. 9-16
Author(s):  
Konne Felix Eedee ◽  
Emeji Roseline
Keyword(s):  
Mucous Membrane ◽  
Rna Virus ◽  
Close Contact ◽  
Face Mask ◽  
Ivermectin Treatment ◽  
Infected Person ◽  
Coronavirus Infection ◽  
Precautionary Measures ◽  
Respiratory Droplets

Coronaviruses are a group of related RNA viruses that cause disease in mammals and birds. COVID-19 infection is caused by a single stranded RNA virus called SARS-CoV-2 that is similar to the severe acute respiratory syndrome coronavirus (SARS-CoV). The aim of this review is to identify how COVID-19 infects man, the preventive approach and treatment possibility with ivermectin drug. The possible main source of transmission is thought to be a close contact with infected person or animal and respiratory droplets while the mucous membrane; conjunctiva, mouth, nasal cavity, and throat are the main routes of transmission. The virus enters the human through the ACE2 receptor which are found in the mucous membrane. This is an important step for coronavirus infection establishment. To stay safe from coronavirus, physical distancing, wearing of face mask, keeping rooms well ventilated, avoiding crowds, cleaning/washing your hands, the use of hand sanitizers and coughing into a bent elbow are precautionary measures to avoid contracting the infection. Ivemectin blocks the initiation of the binding of the viral protein to the cytoplasmic receptor (imp α/β). The inhibitory role of ivemectin prevents further increase in the viral load. Ivermectin drug could be a remarkable medical breakthrough for the lasting treatment of the infection; however, more clinical trials are suggested in this area.

scholarly journals Real-Time Face Mask Detection using Deep Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 25-31
Author(s):  
Pranad Munjal ◽  
Vikas Rattan ◽  
Rajat Dua ◽  
Varun Malik
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Face Mask ◽  
World Health ◽  
Infected Person ◽  
Accuracy Rate ◽  
Public Areas ◽  
Health Organization ◽  
Respiratory Droplets ◽  
Real Time Application

The outbreak of COVID-19 has taught everyone the importance of face masks in their lives. SARS-COV-2(Severe Acute Respiratory Syndrome) is a communicable virus that is transmitted from a person while speaking, sneezing in the form of respiratory droplets. It spreads by touching an infected surface or by being in contact with an infected person. Healthcare officials from the World Health Organization and local authorities are propelling people to wear face masks as it is one of the comprehensive strategies to overcome the transmission. Amid the advancement of technology, deep learning and computer vision have proved to be an effective way in recognition through image processing. This system is a real-time application to detect people if they are wearing a mask or are without a mask. It has been trained with the dataset that contains around 4000 images using 224x224 as width and height of the image and have achieved an accuracy rate of 98%. In this research, this model has been trained and compiled with 2 CNN for differentiating accuracy to choose the best for this type of model.It can be put into action in public areas such as airports, railways, schools, offices, etc. to check if COVID-19 guidelines are being adhered to or not.

Real-Time Face Mask Detection Using Raspberry Pi and Camera

2021 ◽  
pp. 767-776
Author(s):  
Nisha Rani ◽  
Rashi Jain ◽  
Saurav Patel ◽  
Tushar Ruhela ◽  
Pankaj Kumari ◽  
...  
Keyword(s):  
Real Time ◽  
Face Mask ◽  
Raspberry Pi

scholarly journals Facing COVID-19 pandemic: development of custom-made face mask with rapid prototyping system

2021 ◽  
Vol 15 (01) ◽  
pp. 51-57
Author(s):  
Pasquale Piombino ◽  
Umberto Committeri ◽  
Giovanna Norino ◽  
Luigi Angelo Vaira ◽  
Stefania Troise ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
Close Contact ◽  
Face Mask ◽  
Virtual Design ◽  
Global Pandemic ◽  
Custom Made ◽  
3D Printed ◽  
Respiratory Droplets ◽  
Rapid Prototyping System

Background: COVID-19 is a global pandemic. The virus spreads through respiratory droplets and close contact. Therefore, the availability of personal protective equipment (PPE) for healthcare professionals is essential. 3D printing technology could represent a valid option to ameliorate PPE shortages. Methodology: Custom-made face mask were designed on the basis of facial scan and then 3D-printed. The whole protocol is executed with freeware software and only required a 3D printer. Six healthcare workers wore the device weekly thus expressing a judgment regarding quality of work, respiratory and skin comfort. Results: The estimated total cost of a single mask is approximately 5 USD. The virtual design of a complete mask lasted 68 minutes on average. Most healthcare workers rated comfort as very good. Conclusions: Based on the encouraging results obtained, we can confidently confirm that custom-made masks are novel and useful devices that may be used in the fight against COVID-19.

scholarly journals Real-Time Implementation of AI-Based Face Mask Detection and Social Distancing Measuring System for COVID-19 Prevention

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Safa Teboulbi ◽  
Seifeddine Messaoud ◽  
Mohamed Ali Hajjaji ◽  
Abdellatif Mtibaa
Keyword(s):  
Real Time ◽  
Vision System ◽  
Close Contact ◽  
Public Health Problem ◽  
Confidence Score ◽  
Face Mask ◽  
Physical Distance ◽  
Measuring System ◽  
Social Distancing ◽  
The World

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 deploying the models, the selected one achieved a confidence score of 100%. 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, recall, F1-score, support, sensitivity, specificity, and accuracy that demonstrate the practical applicability. The system performs with F1-score of 99%, sensitivity of 99%, specificity of 99%, and an accuracy of 100%. Hence, this solution tracks the people with or without masks in a real-time scenario and ensures social distancing by generating an alarm if there is a violation in the scene or in public places. This can be used with the existing embedded camera infrastructure to enable these analytics which can be applied to various verticals, as well as in an office building or at airport terminals/gates.

scholarly journals Low-Cost Camera-Based Smart Surveillance System for Detecting, Recognizing, and Tracking Masked Human Face

2021 ◽  
Vol 15 (23) ◽  
pp. 104-119
Author(s):  
Ervan Adiwijaya Haryadi ◽  
Grafika Jati ◽  
Ario Yudo Husodo ◽  
Wisnu Jatmiko
Keyword(s):  
Surveillance System ◽  
Low Cost ◽  
Face Mask ◽  
Raspberry Pi ◽  
Surveillance Systems ◽  
Single Shot ◽  
Surveillance Camera ◽  
Smart Surveillance ◽  
The Face ◽  
Smart Surveillance System

A surveillance system is still the most exciting and practical security system to prevent crime effectively. The primary purpose of this system is to recognize the identity of the face caught by the camera. With the advancement of the Internet of things, surveillance systems were implemented on edge devices such as the low-cost Raspberry mobile camera. It raises the challenge of unstructured image/video where the video contains low quality, blur, and variations of human poses. The challenge is increasing because people used to wear a mask during the Covid -19 pandemic.  Therefore, we proposed developing an all-in-one surveillance system with face detection, recognition, and face tracking capabilities. This system integrated three modules: MTCNN face detector, VGGFace2 face recognition, and Discriminative Single-Shot Segmentation (D3S) tracker to create a system capable of tracking the faces of people caught on surveillance camera. We also train new face mask data to recognize and track. This system obtains data from the Raspberry Pi camera and processes images on the cloud as a mobile sensor approach. The proposed system successfully implemented and obtained competitive results in detection, recognition, and tracking under an unconstrained surveillance camera.

PROTOTYPE ALAT PEMANTAU SALURAN AIR BAWAH TANAH DENGAN MENGGUNAKAN WEBCAM C170 BERBASIS RASPBERRY PI

2015 ◽  
Vol 1 (1) ◽  
pp. 37-45
Author(s):  
Irwansyah Irwansyah ◽  
Hendra Kusumah ◽  
Muhammad Syarif
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Public Awareness ◽  
Underground Water ◽  
Raspberry Pi ◽  
Laptop Computer ◽  
Web Browser ◽  
Disaster Monitoring ◽  
The Times ◽  
Human Desire

Along with the times, recently there have been found tool to facilitate human’s work. Electronics is one of technology to facilitate human’s work. One of human desire is being safe, so that people think to make a tool which can monitor the surrounding condition without being monitored with people’s own eyes. Public awareness of the underground water channels currently felt still very little so frequent floods. To avoid the flood disaster monitoring needs to be done to underground water channels.This tool is controlled via a web browser. for the components used in this monitoring system is the Raspberry Pi technology where the system can take pictures in real time with the help of Logitech C170 webcam camera. web browser and Raspberry Pi make everyone can control the devices around with using smartphone, laptop, computer and ipad. This research is expected to be able to help the users in knowing the blockage on water flow and monitored around in realtime.

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