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2019
Conference Paper
Titel

Handling Work Complexity with AR/Deep Learning

Abstract
Complexity is a fundamental part of product design and manufacturing today, owing to increased demands for customization and advances in digital design techniques. Assembling and repairing such an enormous variety of components means that workers are cognitively challenged, take longer to search for the relevant information and are prone to making mistakes. Although in recent years deep learning approaches to object recognition have seen rapid advances, the combined potential of deep learning and augmented reality in the industrial domain remains relatively under explored. In this paper we introduce AR-ProMO, a combined hardware/software solution that provides a generalizable assistance system for identifying mistakes during product assembly and repair.
Author(s)
Dhiman, Hitesh
TH OWL
Büttner, Sebastian
TH OWL
Röcker, Carsten
IOSB-INA
Reisch, Raphael
Resolto Informatik GmbH
Hauptwerk
OZCHI '19: Proceedings of the 31st Australian Conference on Human-Computer-Interaction
Konferenz
Australian Conference on Computer-Human Interaction (OZCHI) 2019
Thumbnail Image
DOI
10.1145/3369457.3370919
Language
English
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Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Tags
  • Augmented reality

  • Deep Learning

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