English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper introduces a software architecture for real-time object detectionusing machine learning (ML) in an augmented reality (AR) environment. Ourapproach uses the recent state-of-the-art YOLOv8 network that runs onboard onthe Microsoft HoloLens 2 head-mounted display (HMD). The primary motivationbehind this research is to enable the application of advanced ML models forenhanced perception and situational awareness with a wearable, hands-free ARplatform. We show the image processing pipeline for the YOLOv8 model and thetechniques used to make it real-time on the resource-limited edge computingplatform of the headset. The experimental results demonstrate that our solutionachieves real-time processing without needing offloading tasks to the cloud orany other external servers while retaining satisfactory accuracy regarding theusual mAP metric and measured qualitative performance

Real-Time Onboard Object Detection for Augmented Reality: Enhancing Head-Mounted Display with YOLOv8