---
_id: '52816'
abstract:
- lang: eng
  text: "Manufacturing companies face the challenge of reaching required quality standards.
    Using\r\noptical sensors and deep learning might help. However, training deep
    learning algorithms\r\nrequire large amounts of visual training data. Using domain
    randomization to generate synthetic\r\nimage data can alleviate this bottleneck.
    This paper presents the application of synthetic\r\nimage training data for optical
    quality inspections using visual sensor technology. The results\r\nshow synthetically
    generated training data are appropriate for visual quality inspections."
author:
- first_name: Iris
  full_name: Gräßler, Iris
  id: '47565'
  last_name: Gräßler
  orcid: 0000-0001-5765-971X
- first_name: Michael
  full_name: Hieb, Michael
  id: '72252'
  last_name: Hieb
citation:
  ama: 'Gräßler I, Hieb M. Creating Synthetic Training Datasets for Inspection in
    Machine Vision Quality Gates in Manufacturing. In: <i>Lectures</i>. AMA Service
    GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany; 2023:253-524. doi:<a href="https://doi.org/10.5162/smsi2023/d7.4">10.5162/smsi2023/d7.4</a>'
  apa: Gräßler, I., &#38; Hieb, M. (2023). Creating Synthetic Training Datasets for
    Inspection in Machine Vision Quality Gates in Manufacturing. <i>Lectures</i>,
    253–524. <a href="https://doi.org/10.5162/smsi2023/d7.4">https://doi.org/10.5162/smsi2023/d7.4</a>
  bibtex: '@inproceedings{Gräßler_Hieb_2023, title={Creating Synthetic Training Datasets
    for Inspection in Machine Vision Quality Gates in Manufacturing}, DOI={<a href="https://doi.org/10.5162/smsi2023/d7.4">10.5162/smsi2023/d7.4</a>},
    booktitle={Lectures}, publisher={AMA Service GmbH, Von-Münchhausen-Str. 49, 31515
    Wunstorf, Germany}, author={Gräßler, Iris and Hieb, Michael}, year={2023}, pages={253–524}
    }'
  chicago: Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets
    for Inspection in Machine Vision Quality Gates in Manufacturing.” In <i>Lectures</i>,
    253–524. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023.
    <a href="https://doi.org/10.5162/smsi2023/d7.4">https://doi.org/10.5162/smsi2023/d7.4</a>.
  ieee: 'I. Gräßler and M. Hieb, “Creating Synthetic Training Datasets for Inspection
    in Machine Vision Quality Gates in Manufacturing,” in <i>Lectures</i>, Nuremberg,
    2023, pp. 253–524, doi: <a href="https://doi.org/10.5162/smsi2023/d7.4">10.5162/smsi2023/d7.4</a>.'
  mla: Gräßler, Iris, and Michael Hieb. “Creating Synthetic Training Datasets for
    Inspection in Machine Vision Quality Gates in Manufacturing.” <i>Lectures</i>,
    AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2023, pp.
    253–524, doi:<a href="https://doi.org/10.5162/smsi2023/d7.4">10.5162/smsi2023/d7.4</a>.
  short: 'I. Gräßler, M. Hieb, in: Lectures, AMA Service GmbH, Von-Münchhausen-Str.
    49, 31515 Wunstorf, Germany, 2023, pp. 253–524.'
conference:
  end_date: 2023-05-11
  location: Nuremberg
  name: SMSI 2023. Sensor and Measurement Science International
  start_date: 2023-05-08
date_created: 2024-03-25T10:16:24Z
date_updated: 2024-03-25T11:05:53Z
department:
- _id: '152'
doi: 10.5162/smsi2023/d7.4
keyword:
- synthetic training data
- machine vision quality gates
- deep learning
- automated inspection and quality control
- production control
language:
- iso: eng
page: 253-524
publication: Lectures
publication_status: published
publisher: AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany
quality_controlled: '1'
status: public
title: Creating Synthetic Training Datasets for Inspection in Machine Vision Quality
  Gates in Manufacturing
type: conference
user_id: '5905'
year: '2023'
...