학술논문

Facemask detection using Deep Learning
Document Type
Conference
Source
2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS) Intelligent Systems for Communication, IoT and Security (ICISCoIS), 2023 International Conference on. :533-537 Feb, 2023
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
COVID-19
Deep learning
Government
Cameras
Security
Task analysis
Intelligent systems
HAAR-CASCADE technique
faces with mask
faces
without a mask
Language
Abstract
COVID-19 was raised in the year 2020 which became more dangerous to society. According to the medical results, 100 million confirmed cases and 6 million deaths. This virus became an obstacle to gathering people in public places. This virus has spread all over the world. So, the Government has implemented a facemask policy to prevent the hazardous virus. It is a very difficult task to observe manually in crowded places. Most people are not wearing facemasks properly in public a place which causes the increase of the virus. So, the proposed model will detect the face mask whether the people are wearing it or not. By using, the HAAR-CASCADE technique we can able to detect whether the people are wearing the mask or not. By using this algorithm, we can able to prevent affecting of the virus to the person. This algorithm works effectively for detecting facemasks. The system compares faces with masks and faces without the mask. If people are not wearing a mask, the system detects through the camera and alerts by the alarm sound. The experiment results show the proposed technique achieves a 95% accuracy rate.