IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

Abstract

The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19.

Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning

“…If we obtain the new confusion matrix and perform the calculations, the new precision obtained is 97.4%. This result is comparable to those obtained in [29][30][31][32] and could be improved with specific training for these three classes.…”

“…On the one hand, references [18][19][20][21][22][23][24][25][26][27] developed a mask detection system that can differentiate between the presence or absence of a mask. On the other hand, references [29][30][31][32] are also able to detect if the nose is outside the mask. This circumstance would reveal an incorrect use of the mask, but as has been discussed in this project, it is only one of the possible incorrect uses.…”

“…To our knowledge, no studies have been conducted on the identification of different misuses of masks through deep learning. The study carried out from [29][30][31][32] only detected the most obvious error, consisting of wearing the mask under the nose. Our proposal is capable of detecting, in addition to the previous mentioned issue, other types of problems, which occur very frequently.…”

“…Smart Screening and Disinfection Walkthrough Gate (SSDWG) was created by Hussain et al in 2021 [31]. It is a low-cost, fast and effective virus spread detection and control system based on IoT, for all places of entry.…”

A Survey on Covid Safety System

“…Keras and TensorFlow were used for implementing Machine Learning algorithm. In [12] this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. They proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance.…”