<![CDATA[The use of face masks in healthcare settings is a crucial measure in preventing the spread of infectious diseases, particularly since the outbreak of the COVID-19 pandemic. However, public compliance with mask-wearing remains a challenge despite the implementation of various regulations. This study aims to design and develop an automatic mask-wearing detection system by leveraging the YOLOv11 algorithm, which is renowned for its superior speed and accuracy in object detection. The methodology involved collecting a dataset of facial images with and without masks, data labeling, model training using YOLOv11, and evaluating the system's performance in real-world conditions. Test results demonstrate that the system can perform real-time mask detection with a mean Average Precision (mAP) of 0.9, establishing it as an effective solution for supporting health protocol monitoring in medical facilities. Consequently, this system not only enhances monitoring efficiency but also has the potential to minimize the risk of infection spread through an intelligent technological approach.]]>
