Real-time Mask Detector (Monitoring COVID-19) - Vrushali Patil; Tukaram Biradar; Bhaiyasaheb Kamble (International Journal of Radio Frequency Innovations, 2024)
This study presents the development and implementation of a real-time mask detection system designed to monitor and enforce mask-wearing policies during the COVID-19 pandemic. Utilizing a convolutional neural network (CNN) and a dataset consisting of annotated images, our system can accurately detect the presence or absence of masks on individuals in various environments. The proposed system achieves high accuracy and can be deployed in public spaces to help mitigate the spread of COVID-19. Key results demonstrate the system's effectiveness in real-world scenarios, providing a valuable tool for public health monitoring. In order to protect public health and safety, the COVID-19 pandemic has brought attention to the necessity for creative technical solutions. One of the most important tools for ensuring mask compliance in public areas is the implementation of real-time mask detection systems. With an emphasis on their technological architecture, machine learning methods, and deployment in various contexts, this study investigates the creation and application of real-time mask detectors. We go over their effects on public health, how they integrate with surveillance and IoT systems, and issues like accuracy, privacy, and flexibility in a variety of settings. The results highlight the value of these technologies in halting the spread of viruses and their potential for wider use in pandemics in the future.
Keywords: COVID-19; Deep Learning; Face detection; face mask detection; transfer learning