| Popis: |
During this pandemic the problems faced was shortage of testing services, vaccines and poor medical care all these lead to lockdown for a long run which affected our daily lives. During this pandemic the problems faced were shortage of testing services, vaccine and poor medical care all these lead to lockdown for the long run which affected our daily lives. The prevention for this problem was to wear masks in public areas. Facemask detection had seen remarkable growth in the image processing and deep learning domain. Facemask detection is already an existing concept in which they had used different algorithms and techniques to show better results. The proposed system in this project is developed to avoid mask-less people from entering the desired places by detecting face-mask, and sending a signal to an Arduino device that connects to the sensors and counts the people while entering and exiting. This helps to maintain the social distancing inside those places and limit the Total number of persons. If the total number of people count increases, the Arduino microcontroller will give the buzzer alarm to notify the person. In This project, we use live detection and identify either the person wears a mask or not. Therefore, this proposed approach will make a significant change in the public healthcare system. Keywords: Deep Learning, Sensors, Computer Vision, Convolutional Neural Networks (CNNs), public Safety, face mask detection, OpenCV, COVID-19. |
