Aye Min Myat’s research while affiliated with University of Technology Yatanarpon Cyber City and other places

The ongoing global pandemic has underscored the importance of effective preventive measures such as wearing face masks in public spaces. In this paper, we propose a deep learning-based approach for real-time face mask detection to aid in enforcing mask-wearing protocols. Our system utilizes convolutional neural networks (CNNs) to automatically detect whether individuals in images or video streams are wearing masks or not. The proposed system consists of three main stages: face detection, face mask classification, and real-time monitoring. Firstly, faces are localized in the input image or video frame using a proposed face detection model. Then, the detected faces are fed into a proposed CNN model for mask classification, which determines whether each face is covered with a mask or not. Finally, the system will provide real-time monitoring and alerts authorities or stakeholders about non-compliance with mask-wearing guidelines. We evaluate the performance of our system on publicly available datasets and demonstrate its effectiveness in accurately detecting face masks in various scenarios. Additionally, we discuss the challenges and limitations of deploying such a system in real-world settings, including issues related to privacy, bias, and scalability. Overall, our proposed face mask detection system offers a promising solution for automated monitoring and enforcement of face mask policies, contributing to public health efforts in mitigating the spread of contagious diseases.

A novel deep learning-based face mask detection system is designed to enhance public safety in various environments. With the ongoing global health concerns, the need for efficient and accurate methods to identify individuals wearing or not wearing face masks has become crucial. By utilizing convolutional neural networks (CNNs) and transfer learning techniques, proposed model achieves impressive accuracy while maintaining high-speed processing capabilities. This paper outlines the architecture, training process, and performance evaluation of the proposed deep learning-based face mask detection system, highlighting its promising role in contributing to a safer and healthier society. The development of a vision-based safety system, the transfer of a small YOLO object detection model, and the creation of a CNN-based classification model are the key objectives of this study. According to experimental findings, proposed system is capable of real-time face mask detection and classification with an accuracy of over 98%.