---
res:
  bibo_abstract:
  - "\r\n\r\nThis 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.\r\n\r\nIf 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\r\n@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Nico
      foaf_name: Baumgart, Nico
      foaf_surname: Baumgart
      foaf_workInfoHomepage: http://www.librecat.org/personId=88090
  - foaf_Person:
      foaf_givenName: Markus
      foaf_name: Lange-Hegermann, Markus
      foaf_surname: Lange-Hegermann
      foaf_workInfoHomepage: http://www.librecat.org/personId=71761
  - foaf_Person:
      foaf_givenName: Mike
      foaf_name: Mücke, Mike
      foaf_surname: Mücke
  bibo_doi: 10.5281/ZENODO.16080102
  dct_date: 2024^xs_gYear
  dct_publisher: Zenodo@
  dct_subject:
  - Object Detection
  - Image Synthesis
  - Domain Randomization
  - Domain Gap
  - Terminal Strip
  dct_title: Synthetic Training Dataset for Real-World Terminal Strip Object Detection@
...