Deploying YOLO Models on HoloLens 2: Balancing Performance and Efficiency in AR Applications
Year: 2025
Type: article
Abstract: This study explores deploying efficient YOLO object detection models on the Microsoft HoloLens 2, an advanced augmented reality (AR) device. The HoloLens 2's limited computational power, memory, and energy efficiency present challenges for implementing AI models like YOLO. We focus on optimizing FPS and model performance to ensure seamless AR interactions, addressing these resource constraints while enhancing the user experience.
Authors: Pei-Chun Chen, Ying-Yin Huang
Institution: National Taiwan University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412608979