Comparison of YOLO architectures for face mask detection in images

Traditionally, image recognition has been tackled as a classification problem. You only look once (YOLO) real-time object detection models take a different approach, treating recognition as a regression problem. Models utilize very efficient neural networks, making YOLO models suitable for real-time observation. Recent events have brought to light the importance of medical face masks in preventing the spread of highly contagious pathogens, as well as the extent the individuals are willing to go to avoid wearing them. A robust model for detecting medical masks, capable of running in real-time could help improve the toolset available for preventing the spread of pathogens and reduce the impact contagious diseases could have in the future. This work compares the performance of several lightweight YOLOv8 models suitable for real-time application for mask detection. The advantages and shortcomings of various model architectures are assessed and their potential for use is explored.