AI Enabled Smart Campus for Health Safety and Monitoring

COVID 19 has had a major effect on society. In order to keep people's spacing, new requirements have been placed in place regarding the amount of users authorized in individual rooms in offices, shops, etc. Along with social distance, regular temperature verification at mall entrances are indeed permitted. An excellent embedded machine learning system is proposed in this work to identify face masks automatically and detect the body's temperature in a real-time application. The proposed system, in particular, utilizes a raspberry pi camera to capture real-time video simultaneously by identifying face masks with the help of a classification technique. The face mask detector is constructed by utilizing mobilenetv2 and imaging net pre-trained weights to consider three scenarios: wearing a mask correctly, wearing a mask incorrectly, and not wearing any at all. By placing a temperature gauge on a Raspberry Pi, a framework has also been developed for determining a person's body temperature. The numerical outcomes show the feasibility and performance of our integrated devices in compared to many cutting-edge research. This temperature and facemask detection device monitors a person's body heat and detects whether or not that person is wearing a facemask. Consequently, any organization's entrance could contain this device. In this study, the door is only released if the temperature is below 99° F, which would be calculated by the Electro Selective Pattern-32 images, the MLX sensor, and the fact that a person's face is 80% protected by a facemask.