Real-Time Face Mask Detection with Deep Learning for Pandemic Safety

• Published in: 2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) pp. 213 - 217
• Main Authors: Sudthongkhong, Chudanat, Intarapasan, Budsakayt, Wongsheree, Thitima, Thanasuan, Kejkaew, Pattanapipat, Bennapa, Suksai, Putawan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 08-11-2023
• Subjects: Computer vision; COVID-19; Data analysis; Data Analytic; Infrared Thermometer sensor; Pandemics; Real-time systems; Systematics; Thermometers
• DOI: 10.1109/SITIS61268.2023.00040

Amid the COVID-19 pandemic, efficient screening of individuals has become imperative. Our project aims to develop an automated system that combines Computer Vision, Infrared Thermometer sensors, and Data Analytics to streamline This process while ensuring public safety. Our objectives encompass designing and implementing an automated screening system, improving data handling and storage, and rigorously testing system efficiency. This system surpasses traditional temperature gauges with a remarkable 93.67% accuracy and excels in detecting sanitary mask compliance with a rate of 96.78%. Additionally, it contributes to environmental sustainability by completely eliminating outdoor waste generation. The user-friendly interface and systematic data storage in a database enable retrospective analysis and efficient data management. Expert assessments and performance tests ensure the system's quality and practicality. In summary, our project introduces an automated system that enhances user screening during the COVID-19 outbreak, offering high accuracy, efficiency, and data management capabilities. This system plays a crucial role in safeguarding public health and community well-being. In the future research, the accuracy results comparison between this method and other methods will be report in the next conference.