DESIGN AND DEVELOPMENT OF REAL TIME THERMAL SCREENING AND MASK DETECTION SYSTEM FOR COVID PREVENTION
COVID 19 pandemic is causing a global health epidemic. The most powerful safety tool is wearing a face mask in public places and everywhere else. The COVID 19 outbreak forced governments around the world to implement lockdowns to deter virus transmission. According to survey reports, wearing
2025-06-28 16:26:18 - Adil Khan
DESIGN AND DEVELOPMENT OF REAL TIME THERMAL SCREENING AND MASK DETECTION SYSTEM FOR COVID PREVENTION
Project Area of Specialization Electrical/Electronic EngineeringProject SummaryCOVID 19 pandemic is causing a global health epidemic. The most powerful safety tool is wearing a face mask in
public places and everywhere else. The COVID 19 outbreak forced governments around the world to implement
lockdowns to deter virus transmission. According to survey reports, wearing a face mask at public places reduces
the risk of transmission significantly. In this paper, an IoT-enabled smart door that uses a machine learning model
for monitoring body temperature and face mask detection. The proposed model can be used for any shopping
mall, hotel, apartment entrance, etc. As an outcome a cost-effective and reliable method of using AI and sensors to
build a healthy environment. Evaluation of the proposed framework is done by the Face Mask Detection algorithm
using the TensorFlow software library. Besides, the body temperature of the individual is monitored using a non-
contact temperature sensor. This proposed system can detect the users from COVID 19 by enabling the Internet
of Things (IoT) technology.It is a multi purpose system that has a wide range of applications. The system makes use of a contactless temperature scanner and a mask monitor. The scanner is connected directly with a human barrier to bar entry if high temperature or no mask is detected.Any person will not be provided entry without temperature and mask scan. Only person having both conditions is instantly allowed inside. The system uses temperature sensor and camera connected with a raspberry pi system to control the entire operation. The camera is used to scan for mask and temperature sensor for forehead temperature. The raspberry processes the sensor inputs and decides weather the person is to be allowed. In this case the system operates a motor to open the barrier allowing the person to enter the premises. If a person is flagged by system for high temperature or no Mask the system glows the red light and bars the person from entry. Also the face and temperature of person is transmitted over IOT to server for authorities to take action and test the person for covid.
The face mask detection technology will install in this setup which detect the individual wearing a mask or not. A wide range camera will use for face detection and iot technology helps to detect weather a person weard mask or not. In case of not wearing the system display a notification to them that “put mask” from the audio and then it will check temperature if temperature is normal then it will speak normal temperature.When all three steps are done by individuals only then he will go in and barrier will remove.
THE MAIN OBJECTIVES OF PROJECT ARE:
- Face Mask Detector with OpenCV,
Keras/TensorFlow, and Deep Learning - Designing IoT-Based Independent Pulse
Oximetry Kit as an Early Detection Tool
for Covid-19 Symptoms Development of
portable pulse oximetry kit. - System for avoiding touching various
objects and surfaces in offices using IoT. - Face detection for Smart Door Unlocking
System using Raspberry pi. - A real time DNN-based face mask
detection system using single shot
multibox detector and MobileNetV2 - Durian Types Recognition Using Deep
Learning Techniques to increase the
accuracy and speed of recognition, an
efficient model based on durian
characteristics using CNN.
FOR TEMPERATURE MEASURMENT:
Multi-temperature sensors, such as the DS18B20, were attached and are used to capture a person’s body temperature signal, after whichthe SCM AT89C52 processed the signal. They use the nRF905 wireless transceiver chip to complete the signal wirelessly from the work station.known as slave station to a central station, a USB adapter PDIUSBD12 to link the upper PC. Since the temperature calculated errors are less than plusmn0.1degC, this system showed that the device with wireless communication is much better, and it meets the clinic’s medical require-ments well. It can be transplanted into another sector, namely green-house environment intelligent monitor, with the help of the system’s modularization design.
FOR FACE MASK DETECTION:
Two algorithms will use for face mask detection.
HAAR CASCADE ALGORITHMS:
The Haar Cascade algorithm to detect facets in the low-cost Internet of Things using the Raspberry Pi method was used . It is a cutting-edge access control scheme. It shows a machine learning approach for facial recognition and detection that makes use of the OpenCV library’s hair cascade to complete the task quickly and with a high detection rate.Face recognition is a way of recognizing and verifying an individual’s identity by looking at their face. The Python programming language is used to make modifications to the framework. A grey and a colored picture of the faces are differentiated by the pro-positive style. The frame-work’s effectiveness is calculated by measuring the face recognition rate for each individual in the database. The proposed system’s findings canbe used to accurately distinguish faces even from low-quality images.
Convolution Neural Networks (CNN) Algorithm:
In this peoject, a deep learning algorithm is used to identify face mask recognition and, Convolution Neural Networks (CNN) classification. A CNN is a form of artificial neural network that is specifically built to in-terpret pixel input and is mainly used for image recognition and analysis,in which each layer applies to a different set of filters.
OVERALL SYSTEM:
Any person attempting to enter the building should
first pass through infrared sensors, which are used to track and man-age the individual count of people entering the room and later exiting.Body temperature is tested only when the people’s total count inside a room is less than the given limit. The MLX90614 body temperature sensor is used for this purpose. If the person’s body temperature is too high, the door will not open; if the person’s temperature is average, the door will open and proceed to the next level.The
Raspberry Pi single-board computer with Raspberry Pi Camera is used for this function. If an individual wearing a mask is detected, the door will be opened.If the individual is discovered without a mask, the door will not open.To ensure the guidelines and safety for indoor workers
during this COVID-19.so used this solution.
The first step to detect covid is by scanning for fever. Also we need to monitor every person for a mask. We will have temperature checking systems for every entrances for scanning but manual temperature scanning has a lot of disadvantages.The personnel are not well trained on using temperature scanner devices.There is human error in reading values.Many a times people are not barred from entry even after higher temperature readings or no masks.The scanning is skipped by the personnel if supervisors are not watching.Manual scanning system is not suitable for large crowds.To solve this problem we here propose a fully automated temperature scanner and entry provider system.
This project a simple and low price IoT node, the mobile
device, and fog-based machine learning (ML) instruments for statistical analyses and diagnostics.In this work, an IoT-enabled smart door is developed to monitor body temperature and detect face masks that can enhance public safety. This will help to reduce manpower while also providing an extra layer of protection against the spread of Covid-19 infection.The model uses a real-time deep learning system using Raspberry pi to detect face masks, and temperature detection as well as monitor the count of people present at any given time. The device will perform excel-lently when it will come to temperature measurement and mask detection.
Raspberry Pi:
The Raspberry Pi is a low-cost tiny computer that connects to a computer monitor or television and operates with a regular keyboard and mouse.
R pi’s Cam (Raspberry Pi Camera):
An 8-megapixel sensor Pi Camera of Raspberry is used in this project.This camera module consists of 1080p30, 720p60, and 640 × 480p90 video support and support resolution of 3270 × 2444 pixels resolution.
IR Sensor:
Infrared sensors are used to count and monitor the number of people who enter and leave the room.
Temperature Sensor:
The temperature sensor (MLX90614) acts as an infrared non-contact temperature reader that reads the temperature without contacting them.
Servo Motor:
A servo motor is used to demonstrate the opening and closing of the main door.
SOFTWARE REQUIREMENT:
- TEMSOR FLOW:TensorFlow is a machine learning software library that is open source
and free. - PUTTY:PuTTY is an open-source software that provides both serial console and software transfer for network files.
- VNC VIEWER:Virtual Network Computing (VNC) is a graphical desktop sharing ap-
plication that lets us monitor the desktop interface of one machine with another computer or mobile device remotely.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 70200 | |||
| Raspberry Pi | Equipment | 1 | 12000 | 12000 |
| DC Motor | Equipment | 1 | 14000 | 14000 |
| Raspberry Pi Camera | Equipment | 1 | 8000 | 8000 |
| Temperature Sensor | Equipment | 1 | 1900 | 1900 |
| ESP8266 Wifi Module | Equipment | 1 | 1000 | 1000 |
| Wires and Connectors ,Resistors ,Capacitors ,Transistors | Equipment | 10 | 100 | 1000 |
| Red & Green Indicators | Equipment | 4 | 200 | 800 |
| Buzzer Siren | Equipment | 1 | 1000 | 1000 |
| Nuts and bolts | Equipment | 1 | 2500 | 2500 |
| Frame prototype | Equipment | 1 | 5000 | 5000 |
| Flap barrier | Equipment | 1 | 20000 | 20000 |
| printing | Miscellaneous | 1 | 1000 | 1000 |
| Extra expenditure | Equipment | 1 | 2000 | 2000 |