Facemask Detection to Prevent COVID-19 Disease Using Computer Vision and Deep Learning: Algorithms, Frameworks, Research and Implementation Challenges
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The ongoing global COVID-19 pandemic has impacted everyone’s life and brought economies to a standstill. World Health Organization (WHO) and governments all over the world have found that social distancing and donning a mask in public places has been instrumental in reducing the rate of COVID-19 transmission. Stepping out of her homes in a face mask is a social obligation and a law mandate that is often violated by people, and hence, a face mask detection model that is accessible and efficient will aid in curbing the spread of disease. Detecting and identifying a face mask on an individual in real time can be a daunting and challenging task, but using deep learning and computer vision, establish tech-based solutions that can help combat COVID-19 pandemic. In this paper, we have established various deep learning architecture, designs, models and parameters that are needed to establish a real-time working model to identify a person who is donning a face mask and a face mask violator. The main outcome of this survey is to bring light to the various deep transfer learning algorithms and parameters that help to build a face detection model in real time for various public environment or places.
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