Universiti Teknologi Malaysia Institutional Repository

Convolutional neural network architecture for detecting facemask and social distancing: a preventive measure for COVID19

Malik, Najeeb Ur Rehman and Abu Bakar, Syed A. R. and Sheikh, Usman Ullah and Airij, Awais Gul (2022) Convolutional neural network architecture for detecting facemask and social distancing: a preventive measure for COVID19. In: 11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021, 5 April 2021 - 6 April 2021, Virtual, Online.

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Official URL: http://dx.doi.org/10.1007/978-981-16-8129-5_144

Abstract

COVID-19 is a life-threatening virus which affected people at a global level in just a matter of few months and is highly contagious. In order to reduce its spread, SOPs must be followed, such as washing hands, wearing face masks, and maintaining social distance. Hence, to aid the strict follow up of SOPs, this paper proposes a system to detect whether the people are wearing face masks and maintaining social distance or not in order to break the chain of COVID 19. The proposed system uses Deep Learning (DL) model based on Convolutional Neural Network (CNN) architecture for training the facemask detector and OpenPose 2D skeleton extraction technique for detecting social distance. A DL model based on a 7-layered CNN architecture was proposed in this research to detect masked and unmasked faces. Based on the proposed technique, 99.98% validation and 99.98% testing accuracies were achieved. In addition to that, the maintenance of social distance which is the new normal nowadays was also detected using the images obtained from the internet as currently, there is no such database available for detecting social distancing.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Convolutional Neural Network, COVID-19, social distancing
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:98812
Deposited By: Narimah Nawil
Deposited On:02 Feb 2023 09:14
Last Modified:02 Feb 2023 09:14

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