Warning System with Face Mask Detection Using CNN

The main motive of writing this paper is to create a facial mask detector that can find whether a person has worn a facemask properly or not. This paper is used to solve real-life problems that nowadays we must avoid. So it helps any person. After implementing this detector, we can easily classify people who are wearing a face mask and the people who are not wearing the mask and people who are incorrectly wearing facemasks. The proposed method of this paper uses the YOLO algorithm. This detector can be used for precautionary purposes during the spread of COVID-19. The aim of this research paper is to propose an alert system that detects individuals not wearing masks in public places using Convolutional Neural Networks (CNN). The proposed system uses a dataset of images to train the CNN model, which accurately identifies the presence or absence of a mask on a person's face. Live video feeds from cameras installed in public areas are monitored by the system, which sends alerts to authorities when it detects individuals not wearing masks. The effectiveness of the system is evaluated through experiments and comparison with other state-of-the-art face mask detection methods. This paper proposes a YOLO algorithm-based facial mask detector that can classify people who are wearing masks, those who are not, and those who are wearing masks incorrectly. The proposed method can be used for precautionary purposes during the spread of COVID-19 and has the potential to mitigate the spread of infectious diseases.