Earticle

초록

영어

This study presents a real-time interactive VR logistics training system by integrating YOLOv8-based object detection and tracking into a Unity + Sentis platform. To support realistic logistic scenarios, we constructed a custom VR logistics dataset consisting of approximately 15,000 images across six key object categories: boxes, forklifts, conveyors, people, AGVs, and robotic arms. Leveraging a YOLOv8 model trained on our domain-specific logistics dataset, we integrated real-time object detection with the Unity 3D coordinate system to enable synchronized interactions such as AGV obstacle avoidance and robotic arm operations. Experimental results demonstrate that the proposed system achieves high detection accuracy and real-time inference speeds exceeding 30 FPS (achieving 50-60 FPS on Apple M3 GPU with MPS acceleration), ensuring smooth performance in immersive VR environments. This approach addresses the limitations of real-time object recognition and responsiveness in conventional VR logistics training systems, expanding the potential for an intelligent and immersive training platform that dynamically adapts to learner behavior and training scenarios. Our research provides the foundation for establishing a digital twin/metaverse-based smart logistics education ecosystem and contributes to cultivating next-generation logistics professionals with enhanced safety awareness and operational efficiency.

목차

Abstract
1. Introduction
2. Related Work
2.1 VR-Based Logistics Education and Training Environments
2.2 Object Detection and Tracking Technologies
2.3 YOLO Evolution
3. Proposed Method
3.1 System Overview
3.2 YOLO Model Architecture and Training Dataset
3.3 Object Tracking Pipeline
4. Experiments and Results
4.1 Experimental Environment
4.2 Evaluation of YOLOv8 for Real-Time Object Detection in VR Logistics
4.3 Object Recognition Speed in VR Environments
4.4 Case Analysis
4.5 Summary of Results
5. Discussion
5.1 Practicality and Limitations
5.2 Application and Extension in VR
5.3 Future Directions
6. Conclusion
References

키워드

저자

• Hyun-ji Kim [ Student, Department of Media Software, Sungkyul University, Anyang, Korea ]
• Ji-won Jeong [ Student, Department of Media Software, Sungkyul University, Anyang, Korea ]
• Hyejin Park [ Visiting Professor, Department of Media Software, Sungkyul University, Anyang, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380