Rider Face Mask Detection and Alerting Using Machine Learning Techniques

The major goal of this study is to identify the individual in question, whether they are wearing a helmet or not and whether or not they are wearing a mask. In our daily lives, the COVID-19 epidemic has brought about a lot of changes. We must take specific steps in order to safeguard ourselves from contracting COVID-19. To protect oneself, we must take certain measures. Put on a face mask, keep your distance from others, use hand sanitizer, etc. Wearing a face mask is one such crucial and fundamental safety measure that we can do to protect ourselves. Respiratory droplets are the primary method of virus transmission. Therefore, when a virus in person speaks, shouts, sneezes, or coughs, the droplets enter the nearby person's mouth or nose. Therefore, using a face mask over the mouth and nose creates a barrier between people to stop respiratory droplets from being shared. However, so many people disregarded donning a mask in public. In addition to that problem, the number of motorbike collisions is rising quickly. The risk is greater for those who choose not to wear helmets than for those who do. A programme or system that can recognise people in public settings who are not wearing masks is a fantastic idea. The suggested method uses the input photos and image processing to determine whether the rider is wearing a mask or helmet. The registered vehicle number is identified and processed into an API (plate recognizer) where the number plate characters are retrieved if the rider is not wearing a mask or helmet. An email and SMS notification of a rule violation are delivered to the affected party.