@misc{13824,
  abstract     = {{

This dataset provides synthetic training data for the real-world industrial application of terminal strip object detection to investigate the sim-to-real generalization performance of modern object detectors based on state-of-the-art image synthesis methods. It consists of 30.000 randomly generated synthetic images of terminal strips covering 36 different terminal blocks in five colors and additional accessories such as plug-in bridges, test adapters, end covers and markings. Except from the markings and the DIN rail all objects of the terminal strips are labeled with a bounding box and the respective object class for supervised learning. Additionally, 300 real images of terminal strips were taken and manually labeled for the real-world test.

If you use this datset for your research, please consider citing this: Investigation of the Impact of Synthetic Training Data in the Industrial Application of Terminal Strip Object Detection
}},
  author       = {{Baumgart, Nico and Lange-Hegermann, Markus and Mücke, Mike}},
  keywords     = {{Object Detection, Image Synthesis, Domain Randomization, Domain Gap, Terminal Strip}},
  publisher    = {{Zenodo}},
  title        = {{{Synthetic Training Dataset for Real-World Terminal Strip Object Detection}}},
  doi          = {{10.5281/ZENODO.16080102}},
  year         = {{2024}},
}

@inproceedings{4097,
  abstract     = {{The capabilities of object detection are well known, but many projects don’t use them, despite potential benefit. Even though the use of object detection algorithms is facilitated through frameworks and publications, a big issue is the creation of the necessary training data. To tackle this issue, this work shows the design and evaluation of a prototype, which allows users to create synthetic datasets for object detection in images. The prototype is evaluated using YOLOv3 as the underlying detector and shows that the generated datasets are equally good in quality as manually created data. This encourages a wide adoption of object detection algorithms in different areas, since image creation and labeling is often the most time consuming step.}},
  author       = {{Besginow, Andreas and Büttner, Sebastian and Röcker, Carsten}},
  booktitle    = {{22nd International Conference on Human-Computer Interaction}},
  isbn         = {{978-3-030-50343-7}},
  keywords     = {{Object detection, Synthetic datasets, Machine learning, Deep learning}},
  location     = {{Copenhagen, Denmark}},
  pages        = {{178--192}},
  publisher    = {{Springer}},
  title        = {{{Making Object Detection Available to Everyone - A Hardware Prototype for Semi-automatic Synthetic Data Generation}}},
  doi          = {{https://doi.org/10.1007/978-3-030-50344-4_14}},
  volume       = {{12203}},
  year         = {{2020}},
}