MASK DETECTION USING DEEP LEARNING METHODS

Abstract

If nothing changes, the COVID-19 pandemic will devastate institutions like the academy around the world, forcing them to lock their doors virtually. SARS-CoV-2 is a coronavirus that causes the severe acute respiratory syndrome. Droplets of contaminated respiratory secretions spread corona virus-2 when an infected person talks, sneezes, or coughs. Close contact with an infected person or exposure to infected surfaces and items speeds up the spread. The only surefire way to keep ourselves safe at this point is to avoid getting infected in the first place. One strategy to prevent exposure to the virus is to wear a facemask that covers the nose and mouth whenever one goes into a public place and to wash hands often or use sanitisers with at least 70% alcohol. As our ability to analyse images has improved, Deep Learning has proven to be an invaluable tool for recognition and classification. The study uses deep learning to determine if a person is correctly wearing a facemask if they are wearing a facemask at all, or if they are not wearing a facemask at all. The gathered dataset consists of 8982 photos with a resolution of 224x224 pixels, and the trained model attained an accuracy rate of between 99.55% and 98.94%. In real time, the system learns to distinguish between three distinct states—not wearing a mask, wearing the wrong mask, and wearing a mask. This research helps prevent infection and stop the spread of the virus.