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

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By Kristin Paetzold-Byhain, Sebastian Schwoch, Maximilian Peter Dammann, Johannes Georg Bartl
| tu-dresden.de
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.
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