A System Design With Deep Learning and IoT to Ensure Education Continuity for Post-COVID
Product Description
Aim:
Ensure education continuity for post-covid it used to make a attendance for a student with face detection and ensure student were a mask.
Abstract:
Deep learning-based comprehensive study to reduce the effects of COVID-19 on the education system is presented. The proposed system consists of an edge device, IoT nodes, and a neural network that runs on a server. The purpose of the proposed system is to protect students and staff against infectious diseases and increase the students’ performance during classes by monitoring the environ- mental conditions via an IoT-based sensor network, during the current pandemic to ensure the use of masks in closed areas by training a customized deep learning model, and to monitor the student attendance data by deep learning and IoT-based solution. Furthermore, effective heating and cooling can be done to save energy by transmitting the environmental conditions of the indoor environment to the relevant destinations. The experiment
is conducted with five different networks to classify the faces in the images as masked or unmasked, and their performances were examined. The networks were trained on the Face Mask Detection Dataset which contains a total of 7553 masked and unmasked images
Proposed System:
If a mask is on the face, it is requested to be removed for a short time to proceed with the face recognition procedure. Face recognition and body temperature monitoring steps proceed if the student has previously enrolled in the system. If an unregistered face is detected, the student is asked to read his/her student card to the card reader, so the necessary information can be retrieved from the database, and then 50 images of the student are taken and stored in the database. Face mask detection using CNN model with high accuracy and then check allowed a classroom.
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The Delivery time for Hardware Mini projects is 7-8 working days.