Real-Time Onboard Object Detection for Augmented Reality: Enhancing Head-Mounted Display with YOLOv8
This enables enhanced perception for AR users with wearable devices, though it is incremental as it applies an existing model to a new platform.
This paper tackled real-time object detection on a head-mounted AR display by implementing YOLOv8 on the Microsoft HoloLens 2, achieving real-time processing without cloud offloading while maintaining satisfactory accuracy.
This paper introduces a software architecture for real-time object detection using machine learning (ML) in an augmented reality (AR) environment. Our approach uses the recent state-of-the-art YOLOv8 network that runs onboard on the Microsoft HoloLens 2 head-mounted display (HMD). The primary motivation behind this research is to enable the application of advanced ML models for enhanced perception and situational awareness with a wearable, hands-free AR platform. We show the image processing pipeline for the YOLOv8 model and the techniques used to make it real-time on the resource-limited edge computing platform of the headset. The experimental results demonstrate that our solution achieves real-time processing without needing offloading tasks to the cloud or any other external servers while retaining satisfactory accuracy regarding the usual mAP metric and measured qualitative performance