A Framework for Detection of Face Mask using Deep Learning Approach with Internet of Things (IoT)

Authors

  • Pravat Kumar Rautaray Department of Computer Science & Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan, deemed to be University, Bhubaneswar, 751030 Odisha, India
  • Binod Kumar Pattanayak Department of Computer Science & Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan, deemed to be University, Bhubaneswar, 751030 Odisha, India
  • Mihir Narayan Mohanty Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan, deemed to be University, Bhubaneswar, 751030 Odisha, India
  • Bibhuprasad Mohanty Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan, deemed to be University, Bhubaneswar, 751030 Odisha, India

DOI:

https://doi.org/10.37934/araset.48.2.2844

Keywords:

IoMT, Transfer learning, Convolutional neural networks

Abstract

A wide range of diseases have become more common in modern human society. Without proper care, the mortality rate consequently rises, resulting in a loss of worth. Humans now place a high importance on genuinely caring for their health and wealth. A facemask covering nose and mouth is one of the most effective ways to protect against infection and to spread the corona virus. This safeguard rule is applied by almost all governments. For automation, we have developed an approach based on deep learning to detect the mask. The approach has been extended to an Internet of Things (IoT) based framework that can be an element of a smart city to keep people safe. Since health safety is a major challenge, this paper proposes an automatic detection process. A Convolutional neural network with transfer learning is used for the detection. This model is included in an IoT architecture for automation. The results of testing the approach are have been observed to be excellent in terms of accuracy. In addition, the module for IoT works well, as verified in the study, and also appears to be useful for the Internet of Medical Things.

Downloads

Download data is not yet available.

Author Biographies

Pravat Kumar Rautaray, Department of Computer Science & Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan, deemed to be University, Bhubaneswar, 751030 Odisha, India

pravat.routray@gmail.com

Binod Kumar Pattanayak, Department of Computer Science & Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan, deemed to be University, Bhubaneswar, 751030 Odisha, India

Mihir Narayan Mohanty, Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan, deemed to be University, Bhubaneswar, 751030 Odisha, India

Bibhuprasad Mohanty, Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan, deemed to be University, Bhubaneswar, 751030 Odisha, India

bibhumohanty@soa.ac.in

Published

2024-06-28

Issue

Section

Articles