YOLOv8 Object Detection on HoloLens 2: Navigating the Accuracy–Efficiency Trade-Off - Silva, Edvar; Lima, Bruno; Alves, Renalvo; Georgevich, Bruno; Vieira, Tiago (Anais do Simpósio de Realidade Virtual e Aumentada (SVR); 2025: Anais do XXVII Simpósio de Realidade Virtual e Aumentada, 2025)
Object detection is a relevant capability in augmented reality (AR) systems, enabling applications to understand physical environments. However, implementing deep learning-based object detectors like the YOLO (You Only Look Once) family on devices like the Microsoft HoloLens 2 (HL2) is challenging due to limited processing power. This paper investigates this trade-off regarding deploying YOLOv8 variants on the HL2 for real-time object detection in AR scenarios. A two-phase methodology is proposed: theoretical benchmarking using the COCO dataset and empirical validation on the device. Using a multi-criteria decision-making framework based on Euclidean distance optimization, the study identifies optimal configurations balancing inference speed and detection quality. Results show that while larger YOLOv8 models achieve higher accuracy, only lightweight configurations maintain processing rates above 15 FPS with latencies below 100 ms — thresholds necessary for perceptual synchronization in AR. The findings provide practical guidelines and performance metrics for selecting YOLO-based models in resource-constrained AR platforms, contributing to the development of efficient and interactive AR systems.