Real Time Face Mask Detection and Monitoring System (RFMDM)

Face masks have played a critical role in limiting the spread of COVID-19, reducing the transmission of respiratory droplets containing the virus. As vaccination efforts ramp up and the pandemic subsides, wearing face masks will continue to be important in certain scenarios such as during flu season or in crowded indoor spaces. Although COVID-19 cases are on the decline, the risk of any new contagious diseases is simply around the corner. The enforcement of face mask policies, however, can be challenging. To address this issue, computer vision techniques have been applied to develop face mask detection algorithms. These algorithms utilize deep learning models that are capable of accurately detecting individuals who are not wearing face masks in real-time. Face mask detection algorithms have been integrated into surveillance systems to ensure compliance with face mask policies in various settings, including public transportation, schools, and workplaces. The development of such algorithms provides a novel approach to enforce public health policies and mitigate the spread of respiratory illnesses. The proposed Real Time Face Mask Detection and Monitoring System (RFMDM) is primarily concerned with using object identification algorithm for face mask recognition and merging the algorithm with a Raspberry PI to build RFMDM using standard laser tripwire technology.