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

翻译：本文介绍了一种在增强现实（AR）环境中利用机器学习（ML）实现实时目标检测的软件架构。该方法采用当前最先进的YOLOv8网络，在微软HoloLens 2头戴式显示器（HMD）上实现机载运行。本研究的核心动机是借助可穿戴、免手持的AR平台，使先进ML模型能够应用于增强感知与态势感知领域。我们展示了YOLOv8模型图像处理流水线，以及使其在头显资源受限的边缘计算平台上实现实时运行所采用的技术。实验结果表明，该方案无需将任务卸载至云端或任何外部服务器即可实现实时处理，同时在常规mAP指标与定性性能评估中保持令人满意的准确度。