AN ENHANCED YOLOV9 FRAMEWORK FOR DETECTING MASK-WEARING COMPLIANCE

Abstract

The widespread use of face masks plays a vital role in minimizing the spread of infectious diseases, making reliable mask-wearing compliance detection a crucial task. This project proposes An Enhanced YOLOv9 Framework for Detecting MaskWearing Compliance, designed to achieve high accuracy and robustness in diverse environments. Leveraging the powerful YOLOv9 architecture, the framework integrates optimization strategies to improves mall-object detection, reduce false positives, and enhance feature learning in crowded or complex scenarios. The model is trained on a benchmark Mask-PPE dataset, enabling it to classify individuals into categories such as mask, no mask, and improper mask usage. Experimental evaluations demonstrate that the enhanced YOLOv9 framework achieves superior detection performance compared to conventional approaches, ensuring efficient monitoring in real-world applications such as healthcare facilities, workplaces, and public spaces. This system provides a scalable solution for supporting public safety initiatives through intelligent surveillance technologies. Index Terms – Mask Detection, YOLOv9, Compliance Monitoring, Object Detection, Computer Vision, Deep Learning, Real-Time Detection, Face Recognition, Public Safety, Artificial Intelligence.