The rapid spread of infectious diseases has emphasized the importance of wearing face masks to reduce the transmission of viruses and protect public health. However, manually monitoring whether people wear face masks in crowded places in difficult, time –consuming and prone to human error. To address this problem, this project proposes an automated Face Mask Detection System using the YOLOv8 (You Only Look Once Version 8) deep learning algorithm. The system is trained on a Kaggle face mask detection dataset to accurately identify and classify people as “Mask” or “No Mask” in real time. This project is designed for use in hospitals, educational institutions, airports, railway stations, shopping malls, offices, industries and other public places where continuous monitoring of mask compliance is required. It assists security personnel, healthcare workers and authorities in ensuring that safety protocols are followed efficiently without the need for constant manual supervision.The trained model achieved a Precision of 87.4%, Recall of 82.9% and mAP@50 of 86.7%, demonstrating reliable performance in detecting face masks. The proposed system offers several benefits, including improved public safety, reduced human effort, faster and more accurate monitoring, costeffective surveillance and the ability to integrate with CCTV cameras for real-time detection. Furthermore, it can be expanded to detect other personal protective equipment such as helmets and safety vests, making it useful in healthcare, industrial and public safety applications. Overall, this project demonstrates how artificial intelligence, computer vision and deep learning can provide an efficient, accurate and scalable solution for automated face mask detection, contributing to safer environments and supporting the development of smart surveillance systems.

Naga Saranya Harini K, Shahithya N, Ram Kumar P, Pradeep P,"YOLOv8 Assisted Face Mask Detection" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(4): Page(465-483) July - August 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.