@inproceedings{52816,
  abstract     = {{Manufacturing companies face the challenge of reaching required quality standards. Using
optical sensors and deep learning might help. However, training deep learning algorithms
require large amounts of visual training data. Using domain randomization to generate synthetic
image data can alleviate this bottleneck. This paper presents the application of synthetic
image training data for optical quality inspections using visual sensor technology. The results
show synthetically generated training data are appropriate for visual quality inspections.}},
  author       = {{Gräßler, Iris and Hieb, Michael}},
  booktitle    = {{Lectures}},
  keywords     = {{synthetic training data, machine vision quality gates, deep learning, automated inspection and quality control, production control}},
  location     = {{Nuremberg}},
  pages        = {{253--524}},
  publisher    = {{AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany}},
  title        = {{{Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing}}},
  doi          = {{10.5162/smsi2023/d7.4}},
  year         = {{2023}},
}