ABSTRACT
Currently, the world is witnessing a second wave of the Covid-19 pandemic, and the situation is getting worse day by day. Simple protocols like minimising human contact and wearing a mask outdoors are proving to be good measures to control the spread of the virus. We saw a huge rise in the demand for daily items and due to a lack of availability, large numbers of people gather without taking any precautions to stock essentials. This has led to the spread of the virus to a great extent. In self-checkout stores, the shopping experience is completely automated and there is no physical presence of the shop owner. The automation enables the customers to pick their goods, scan and make payments by themselves without the intervention of the owner or a cashier. In such stores there is a high chance of people not following Covid protocols. So, there is a need for a system that maintains an allowed threshold of people inside the store at any one time, thus minimizing the potential dangerous human contact at all possible cases. We propose an IoT-Based Self-Checkout Store Using Mask Detection. The primary goal of this project is to create a safe environment for the consumers who visit the shop, by keeping a check on the number of customers present at the store and ensuring that each and every customer is following the protocol of wearing a mask. The system consists of two parts, the face mask detection and the customer count. For the mask detection part, deep learning algorithms like CNN are used to generate a model that helps detect a mask, and for the customer count part, a threshold value is set, which gives us the maximum number of people allowed inside the store at a time. The PIR sensors detect the entry and exit of customers and help regulate the count below the threshold. So once the face mask detection of the customer is complete and the number of people present inside the store is checked, the system takes the decision of either allowing the customer inside or asking him or her to wait. This project is designed to provide a solution to the current real-world problem using minimally efficient technology with high accuracy. © 2023 Scrivener Publishing LLC. All rights reserved.