Harnessing Deep Learning in Computer Vision for Effective Face Mask Detection in the Global COVID-19 Crisis

Abstract

Objective: This study aims to explore the use of deep learning algorithms in computer vision to address the effective detection of face mask use in the global COVID-19 crisis.
 Results: Deep learning techniques enable the execution of tasks by computers and smart devices without human intervention, including image identification and predictions. Its applications have demonstrated significant advances in several areas, particularly in engineering and health research. The study highlights that computer vision scientists can contribute to the prevention, control and management of the fight against COVID-19 and other airborne viruses. Computer vision employs algorithmic tools to process images, perform associations and transmit relevant information.
 Implications for Research, Practice and Social: Construction of a deep learning model using a dataset of people with and without face masks. The developed model, implemented in Python with the help of OpenCV, Keras and TensorFlow libraries, presented highly promising results, reaching an accuracy of 99% in predictions in test images.
 Originality/Value: This study highlights the originality and value of deep learning techniques in computer vision as an effective means of tackling virus-borne pandemics such as COVID-19, and contributing to a preventive, efficient and cost-effective approach to use of face masks.
 Keywords: Computer vision; Convolutional Neural Network; Face mask.