Performance Evaluation of Deep Learning Approaches: Face Mask Detection Perspective
Keywords:
YOLO model, YAML configurationAbstract
This research focuses on developing a real-time face mask detection system using a deep learning approach with the YOLO model. It presents a real-time face mask detection system built using the YOLOv8-based YOLO11n object detection model from Ultralytics. The primary objective is to classify individuals into three categories: wearing a mask, not wearing a mask, and wearing a mask incorrectly, addressing a significant health compliance need during public outbreaks. The model leverages the YOLO11n architecture, a lightweight and efficient variant, trained on a custom-labeled dataset defined through a YAML configuration. This technology has significant implications for public health and safety, particularly in monitoring adherence to mask-wearing guidelines in public spaces.
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