---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Andreas
      foaf_name: Besginow, Andreas
      foaf_surname: Besginow
      foaf_workInfoHomepage: http://www.librecat.org/personId=61743
  - foaf_Person:
      foaf_givenName: Sebastian
      foaf_name: Büttner, Sebastian
      foaf_surname: Büttner
      foaf_workInfoHomepage: http://www.librecat.org/personId=61868
  - foaf_Person:
      foaf_givenName: Carsten
      foaf_name: Röcker, Carsten
      foaf_surname: Röcker
      foaf_workInfoHomepage: http://www.librecat.org/personId=61525
  bibo_doi: https://doi.org/10.1007/978-3-030-50344-4_14
  bibo_volume: 12203
  dct_date: 2020^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/978-3-030-50343-7
  dct_language: eng
  dct_publisher: Springer@
  dct_subject:
  - Object detection
  - Synthetic datasets
  - Machine learning
  - Deep learning
  dct_title: Making Object Detection Available to Everyone - A Hardware Prototype
    for Semi-automatic Synthetic Data Generation@
...