Mask and Temperature Detection and Automatic Sanitizer dispenser Using Raspberry Pi

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Mallikarjun P Y ◽  
Mallikarjun P Y
Keyword(s):  
Raspberry Pi ◽  
Temperature Detection

scholarly journals Face Mask Detection and Sanitizer Despenser with Temperature Detection

2021 ◽  
pp. 509-515
Author(s):  
Akash Thakre ◽  
Pravin Hande ◽  
Abhishek Pounikar ◽  
Jaydeo Dabre ◽  
Prof. Virendra Yadav
Keyword(s):  
Temperature Sensor ◽  
Face Mask ◽  
High Accuracy ◽  
Temperature Sensing ◽  
Automated System ◽  
Raspberry Pi ◽  
High Demand ◽  
Public Places ◽  
Temperature Detection ◽  
Simulation Results

In the present scenario due to Covid-19, the need for face mask detection applications, temperature detection and hand sanitizing are now in high demand for Railway Entrance, Airport Entrance, Office Entrance, Museums and Amusement Parks, Other Public Places and enterprises to ensure safety. These steps are now done in manual way by which the personnel may get in contact with the other personnel while sanitizing and checking temperature might not be accurate. To mitigate the problem, aiming to increase Covid-19 entrance safety, covering several relevant aspects: Contactless temperature sensing, Mask detection, Automatic hand sanitizing. Contactless temperature sensing subsystem relies on Raspberry Pi using temperature sensor,while mask detection performed by leveraging computer vision techniques on camera-equipped Raspberry Pi, then the automatic hand sanitizing is achieved by the DC motor connected with the sensor and Raspberry Pi. Any person without temperature check, hand sanitizing and mask scan will not be provided entry. Only person having the conditions satisfied by the system is instantly allowed inside, else the buzzer will alert the security about the situation, if any violation of the condition is found. From the simulation results, it is clearly observed that the proposed method has high accuracy compare to the existing methods. Thus the system provides a 100% automated system to prevent the spread of Covid-19.

scholarly journals Multiple Linear Regression and Deep Learning in Body Temperature Detection and Mask Detection

2021 ◽  
pp. 109-121
Author(s):  
Faisal Najib Abdullah ◽  
Mohamad Nurkamal Fauzan ◽  
Noviana Riza
Keyword(s):  
Deep Learning ◽  
Body Temperature ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Raspberry Pi ◽  
Thermal Camera ◽  
The Public ◽  
Temperature Detection ◽  
Distance Data ◽  
The Government

In this new normal era, many activities began to operate again, such as offices, malls, etc. This creates a potential mass crowd. The public must follow health protocols as recommended by the government, including wearing masks and checking the temperature to anticipate the spread of the coronavirus. This study tested a tool that included image processing and artificial intelligence to help implement health protocols as recommended by the government. This tool connects Raspberry PI, Thermal Camera (amg8833), Pi Camera, an ultrasonic sensor with Multiple Linear Regression and Deep Learning algorithms. The purpose of this tool is to detect body temperature and detect the use of masks. The system will check on the pi camera frame whether the person is wearing a mask or not. The system is trained using the Deep Learning method to detect the use of masks. The system will check the temperature of the human body and the distance between humans and the tool. Temperature and distance data are entered in multiple linear regression formulas to get more accurate results. The processed results of the system will be displayed on the monitor screen if detected using a mask and the normal temperature will be green and if it is not detected it will be red and give a warning sound. The data is sent to the server and displayed via the web. We found that this tool succeeded in detecting body temperature within a distance of 1 to 3 meters with an accuracy of 99.49%, detecting people using masks with an accuracy of 94.71%, and detecting people not wearing masks with an accuracy of 97.7%.

scholarly journals Automated Contactless Temperature and Facemask Detection Using Deep Learning

2021 ◽  
pp. 521-527
Author(s):  
Pratik A. Malave ◽  
Sachin M. Wagde ◽  
Iresh S. Vacche ◽  
Manali S. Gaikwad ◽  
Bapuraje Arkas
Keyword(s):  
Social Distance ◽  
Prevention And Control ◽  
Face Mask ◽  
Raspberry Pi ◽  
The Public ◽  
Corona Virus ◽  
Temperature Detection ◽  
The Common ◽  
And Control ◽  
Breathing Problem

In Our proposed project we are using raspberry pi to detect face mask and temperature of a person. COVID-19 is an infectious disease caused by the corona virus. Corona virus is nothing but a family of viruses which cause the illness in humans. The common symptoms of this virus are fever, dry cough, breathing problem, etc. It is necessary to maintain a social distance and wear a face mask to avoid the chances of getting the virus, as it is affecting the whole world. And for this we need a system which will keep an eye on everyone to ensure the safety of ourselves as well as of others. To ensure the safety of the public, we tried to build a system for contactless Face Mask and Temperature Detection. This project will be helpful in crowded places or workstations to provide support for the prevention and control of Covid-19.

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.

Implementasi Voip Server Berbasis IPV6 Dengan Raspberry PI

2019 ◽  
Vol 9 (01) ◽  
pp. 47-54
Author(s):  
Rabbai San Arif ◽  
Yuli Fitrisia ◽  
Agus Urip Ari Wibowo
Keyword(s):  
Internet Protocol ◽  
Wireless Transmission ◽  
Raspberry Pi ◽  
Test Results ◽  
Average Delay ◽  
Ipv6 Network ◽  
Ip Network ◽  
Selection Of ◽  
Better Than

Voice over Internet Protocol (VoIP) is a telecommunications technology that is able to pass the communication service in Internet Protocol networks so as to allow communicating between users in an IP network. However VoIP technology still has weakness in the Quality of Service (QoS). VOPI weaknesses is affected by the selection of the physical servers used. In this research, VoIP is configured on Linux operating system with Asterisk as VoIP application server and integrated on a Raspberry Pi by using wired and wireless network as the transmission medium. Because of depletion of IPv4 capacity that can be used on the network, it needs to be applied to VoIP system using the IPv6 network protocol with supports devices. The test results by using a wired transmission medium that has obtained are the average delay is 117.851 ms, jitter is 5.796 ms, packet loss is 0.38%, throughput is 962.861 kbps, 8.33% of CPU usage and 59.33% of memory usage. The analysis shows that the wired transmission media is better than the wireless transmission media and wireless-wired.

Conception and implementation of a course for professional training and education in the field of IoT and smart home security

2020 ◽  
Vol 2020 (3) ◽  
pp. 277-1-277-8
Author(s):  
Michael Pilgermann ◽  
Thomas Bocklisch ◽  
Reiner Creutzburg
Keyword(s):  
Smart Home ◽  
Professional Training ◽  
Raspberry Pi ◽  
Science Students ◽  
Training And Education ◽  
Home Security ◽  
Master Level ◽  
University Course ◽  
Home Technology ◽  
Smart Home Technology

The aim of this paper is to describe the new concept of a Master level university course for computer science students to address the issues of IoT and Smart Home Security. This concept is well suited for professional training for interested customers and allows the creation of practical exercises. The modular structure of the course contains lectures and exercises on the following topics: 1. Introduction - IoT and Smart Home Technology and Impact 2. Homematic Technology and Smart Home Applications 3. Loxone Technology and Smart Home Applications 4. Raspberry Pi and Smart Home Applications 5. Security of IoT and Smart Home Systems and contains laboratory exercises of diverse complexities.

An Adaptive Binarization Method for Cost-efficient Document Image System in Wavelet Domain

2020 ◽  
Vol 64 (3) ◽  
pp. 30401-1-30401-14 ◽  
Author(s):  
Chih-Hsien Hsia ◽  
Ting-Yu Lin ◽  
Jen-Shiun Chiang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Document Image ◽  
Background Information ◽  
Raspberry Pi ◽  
Discrete Wavelet ◽  
Image System ◽  
Low Frequencies ◽  
Cost Efficient ◽  
Binarization Method

Abstract In recent years, the preservation of handwritten historical documents and scripts archived by digitized images has been gradually emphasized. However, the selection of different thicknesses of the paper for printing or writing is likely to make the content of the back page seep into the front page. In order to solve this, a cost-efficient document image system is proposed. In this system, the authors use Adaptive Directional Lifting-Based Discrete Wavelet Transform to transform image data from spatial domain to frequency domain and perform on high and low frequencies, respectively. For low frequencies, the authors use local threshold to remove most background information. For high frequencies, they use modified Least Mean Square training algorithm to produce a unique weighted mask and perform convolution on original frequency, respectively. Afterward, Inverse Adaptive Directional Lifting-Based Discrete Wavelet Transform is performed to reconstruct the four subband images to a resulting image with original size. Finally, a global binarization method, Otsu’s method, is applied to transform a gray scale image to a binary image as the output result. The results show that the difference in operation time of this work between a personal computer (PC) and Raspberry Pi is little. Therefore, the proposed cost-efficient document image system which performed on Raspberry Pi embedded platform has the same performance and obtains the same results as those performed on a PC.

A driver drowsiness and distraction warning system based on raspberry Pi 3 Kit

2019 ◽  
Vol 70 (3) ◽  
pp. 184-192
Author(s):  
Toan Dao Thanh ◽  
Vo Thien Linh
Keyword(s):  
Processing System ◽  
Warning System ◽  
Raspberry Pi ◽  
Data Set ◽  
Lower Lip ◽  
Driver Drowsiness ◽  
Image Sensing ◽  
The Face ◽  
The Common ◽  
University Laboratory

In this article, a system to detect driver drowsiness and distraction based on image sensing technique is created. With a camera used to observe the face of driver, the image processing system embedded in the Raspberry Pi 3 Kit will generate a warning sound when the driver shows drowsiness based on the eye-closed state or a yawn. To detect the closed eye state, we use the ratio of the distance between the eyelids and the ratio of the distance between the upper lip and the lower lip when yawning. A trained data set to extract 68 facial features and “frontal face detectors” in Dlib are utilized to determine the eyes and mouth positions needed to carry out identification. Experimental data from the tests of the system on Vietnamese volunteers in our University laboratory show that the system can detect at realtime the common driver states of “Normal”, “Close eyes”, “Yawn” or “Distraction”

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