A YoloV3 based objected detection model for detecting Masked and Unmasked People.
This project aims to take advantage of the cutting-edge algorithms available from the domain of Computer Vision and Deep Learning and apply them to solve a very basic, yet tricky problem – To detect and classify the people that are Wearing masks and the ones that are not.
This problem, might be seemingly straightforward, but it can have a pretty good social outcome and impact; especially if the model itself is shrunk down to run on basic hardware. The end goal is to have a robust and reliable model that can be deployed in lightly/moderately crowded places like Schools, Universities, Gateways that have low-medium foot traffic, Public Parks, Amusement parks, Museums, etc.
