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Towards a process for the creation of synthetic training data for AI-computer vision models utilizing engineering data

Published online by Cambridge University Press:  16 May 2024

Sebastian Schwoch ,
Maximilian Peter Dammann ,
Johannes Georg Bartl ,
Maximilian Kretzschmar ,
Bernhard Saske  and
Kristin Paetzold-Byhain
Sebastian Schwoch*
Affiliation:
Technische Universität Dresden, Germany
Maximilian Peter Dammann
Affiliation:
Technische Universität Dresden, Germany
Johannes Georg Bartl
Affiliation:
Technische Universität Dresden, Germany
Maximilian Kretzschmar
Affiliation:
Technische Universität Dresden, Germany
Bernhard Saske
Affiliation:
Technische Universität Dresden, Germany
Kristin Paetzold-Byhain
Affiliation:
Technische Universität Dresden, Germany
*
sebastian.schwoch@tu-dresden.de

Abstract

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Artificial Intelligence-based Computer Vision models (AI-CV models) for object detection can support various applications over the entire lifecycle of machines and plants such as monitoring or maintenance tasks. Despite ongoing research on using engineering data to synthesize training data for AI-CV model development, there is a lack of process guidelines for the creation of such data. This paper proposes a synthetic training data creation process tailored to the particularities of an engineering context addressing challenges such as the domain gap and methods like domain randomization.

Keywords

artificial intelligence (AI)synthetic training dataannotation automationdomain gapprocess improvement

Information

Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2237 - 2246
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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