earticle

한국어

The metaverse is an expansive digital realm with endless potential for growth and has introduced unique safety challenges, especially when users are using head-mounted dis - plays (HMDs) that limit their awareness of real-world hazards. This paper emphasizes the urgent need for real-time risk detection in Unity-based augmented reality (AR) environments to improve user safety in the metaverse. We propose a new system architecture that integrates the YOLOv8 object detection model within a Unity environment to enable real-time processing of video feeds and to identify and respond to nearby risks. This system is designed to assist industrial workers in recognizing and addressing potential hazards on-site and enhance patient safety by identifying risk factors during medical procedures. As HMD usage extends to outdoor environments, assessing the safety implications of outdoor use becomes increasingly important. Our study aims to advance real-time risk detection capabilities in the metaverse by leveraging YOLOv8 to enhance both AR technology and risk management, with anticipated outcomes including significant contributions to AR development and improved safety protocols in various contexts.