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Chapter

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Title

Using AR and YOLOv8-Based Object Detection to Support Real-World Visual Search in Industrial Workshop: Lessons Learned from a Pilot Study

Authors

[ 1 ] Dziekanat Wydziału Informatyki i Telekomunikacji, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ 2 ] Instytut Robotyki i Inteligencji Maszynowej, Wydział Automatyki, Robotyki i Elektrotechniki, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.2] Automation, electronics, electrical engineering and space technologies
[2.3] Information and communication technology

Year of publication

2023

Chapter type

chapter in monograph / paper

Publication language

english

Keywords
EN
  • human-centered computing
  • visualization
  • visualization techniques
  • treemaps
  • visualization design and evaluation methods
Abstract

EN Visual search requires focused attention to distinguish the target object from its surroundings. This task is increasingly more difficult when searching for a given object within a messy industrial workshop. Thus, we explored how we can support visual search with the help of an augmented reality (AR) head-mounted display (HMD) running YOLOv8 object detection model. We report on the results from a pilot user study with eleven participants tasked with searching for a series of objects in a real-world workshop. Consequently, we reflect on lessons learned concerning the experimental setup with AR interface enhanced with Segment Anything Model (SAM) for YOLOv8 training.

Pages (from - to)

154 - 158

DOI

10.1109/ISMAR-Adjunct60411.2023.00039

URL

https://ieeexplore.ieee.org/document/10322214/keywords#keywords

Comments

841,05; środki finansowe przyznane na realizację projektu w zakresie badań naukowych lub prac rozwojowych

Book

IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)

Presented on

IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), 16-20.10.2023, Sydney, Australia

Ministry points / chapter

20

Ministry points / conference (CORE)

200

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