Design and Development of Human Temperature Measuring System Using Drone Based Multispectral and Thermal Images

People's failure to maintain a social distance is causing the COVID19 virus to spread. We have used the drone thermal images for a maximum of 10 km of coverage to detect temperature and reduce virus spread areas. The part of the work is based on utilizing disinfectant spraying drones, disinfectant testing with the guidance of doctors, setting the path planning of drones for surveying the temperature of people, and monitoring the infected place using GPS. When the thermal camera of the drone detects the temperature values using remote sensing images, the drone covers crowded places like hospitals, cinemas, and temples using remote sensing images. One drone model is designed to provide present results using thermal images. The Proposed drone can cover an affected area of up to 16,000 square meters per hour for capturing remote sensing images. It predicts affected areas using faster CNN algorithms with 2100 thermal images. Thermal mapping is used to monitor the social distance between people, alert people that a virus is spreading, and reduce the risk factor of people's movement. In this paper, remote sensing images are analysed and detect higher temperature areas using thermal mapping (Messina and Modica in Remote Sensing 12:1491, 2020). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm.

The drones can be used to detect a group of people who are unmasked and do not maintain social distance. In this paper, a deep learning-enabled drone is designed for mask detection and social distance monitoring. A drone is one of the unmanned systems that can be automated. This system mainly focuses on Industrial Internet of Things (IIoT) monitoring using Raspberry Pi 4. This drone automation system sends alerts to the people via speaker for maintaining the social distance. This system captures images and detects unmasked persons using faster regions with convolutional neural network (faster R-CNN) model. When the system detects unmasked persons, it sends their details to respective authorities and the nearest police station. The built model covers the majority of face detection using different benchmark datasets. OpenCV camera utilizes 24/7 service reports on a daily basis using Raspberry Pi 4 and a faster R-CNN algorithm.

Real time data analysis of face mask detection and social distance measurement using Matlab.

This paper describes mask detection using Matlab when complex images in the dataset. Matlab specified the Faster R-CNN algorithm and Dataset allotment for mask detection. This paper manages complex pictures using facial recognition packages. The Faster R-CNN methodology used in the security system and the medical system. The proposed work balanced face restriction, color changes, brightness changes, and contrast changes. Segmentation and feature extraction used in face restriction of the person image. We chose RCNN, Fast RCNN, and Faster RCNN algorithm for detecting Mask detection and Social distance. Regions with Convolutional neural network Based on Mixing pictures, pixel prediction, and specific enhancements. The main objective was to solving multiple and multitask picture detection problems with speed rates. The Methodology used for face detection and detection of Unmask person in a dataset of face database.

Real time analysis of unmask face detection in human skin using tensor flow package and IoT algorithm

In covid19, security provided by Indian government, due to severe virus speed to sudden death. This proposed method solved face detection, mask detection and thermal value monitoring for security purpose. We need amount of testing and need tests 50000/day to measure count of infection. Government produces a report that maintain social distancing, wearing mask when in outside and proper testing. Most people do not serious to wearing mask. We focused the counting of Unmask-persons using opencv and MTCNN algorithm. Faces conditions are changing due to various atmospheric season and light vision. The MTCNN algorithm adjusted video streaming data with Mask detection accuracy at >90%. A database stored in Excel file or send Emergency message to email or buzzer kept on, when unmask visit at any places like that shopping complex, hotel, hospital, Traffic areas and temple. MTCNN- Multi-Task Convolutional Neural Network algorithm solved facial recognition problems. We have proposed the algorithm for Mask detection and Thermals value detection using Pycharm-python and implemented in Raspberry pi4. Support Vector Machine (SVM) supported tasks of MTCNN and stored in database. The outcome of the system is Alert through monitor and SMS to officials Government persons, when capturing unmasked persons visit in live streaming video using Opencv – Python.