[{"date_updated":"2026-06-16T14:11:28Z","doi":"10.5281/ZENODO.16080102","keyword":["Object Detection","Image Synthesis","Domain Randomization","Domain Gap","Terminal Strip"],"user_id":"83781","_id":"13824","type":"research_data","author":[{"full_name":"Baumgart, Nico","last_name":"Baumgart","id":"88090","first_name":"Nico"},{"full_name":"Lange-Hegermann, Markus","last_name":"Lange-Hegermann","first_name":"Markus","id":"71761"},{"full_name":"Mücke, Mike","last_name":"Mücke","first_name":"Mike"}],"title":"Synthetic Training Dataset for Real-World Terminal Strip Object Detection","department":[{"_id":"DEP5015"}],"year":"2024","citation":{"ama":"Baumgart N, Lange-Hegermann M, Mücke M. <i>Synthetic Training Dataset for Real-World Terminal Strip Object Detection</i>. Zenodo; 2024. doi:<a href=\"https://doi.org/10.5281/ZENODO.16080102\">10.5281/ZENODO.16080102</a>","ufg":"<b>Baumgart, Nico/Lange-Hegermann, Markus/Mücke, Mike</b>: Synthetic Training Dataset for Real-World Terminal Strip Object Detection, o. O. 2024.","chicago":"Baumgart, Nico, Markus Lange-Hegermann, and Mike Mücke. <i>Synthetic Training Dataset for Real-World Terminal Strip Object Detection</i>. Zenodo, 2024. <a href=\"https://doi.org/10.5281/ZENODO.16080102\">https://doi.org/10.5281/ZENODO.16080102</a>.","chicago-de":"Baumgart, Nico, Markus Lange-Hegermann und Mike Mücke. 2024. <i>Synthetic Training Dataset for Real-World Terminal Strip Object Detection</i>. Zenodo. doi:<a href=\"https://doi.org/10.5281/ZENODO.16080102\">10.5281/ZENODO.16080102</a>, .","ieee":"N. Baumgart, M. Lange-Hegermann, and M. Mücke, <i>Synthetic Training Dataset for Real-World Terminal Strip Object Detection</i>. Zenodo, 2024. doi: <a href=\"https://doi.org/10.5281/ZENODO.16080102\">10.5281/ZENODO.16080102</a>.","bjps":"<b>Baumgart N, Lange-Hegermann M and Mücke M</b> (2024) <i>Synthetic Training Dataset for Real-World Terminal Strip Object Detection</i>. Zenodo.","apa":"Baumgart, N., Lange-Hegermann, M., &#38; Mücke, M. (2024). <i>Synthetic Training Dataset for Real-World Terminal Strip Object Detection</i>. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.16080102\">https://doi.org/10.5281/ZENODO.16080102</a>","van":"Baumgart N, Lange-Hegermann M, Mücke M. Synthetic Training Dataset for Real-World Terminal Strip Object Detection. Zenodo; 2024.","mla":"Baumgart, Nico, et al. <i>Synthetic Training Dataset for Real-World Terminal Strip Object Detection</i>. Zenodo, 2024, <a href=\"https://doi.org/10.5281/ZENODO.16080102\">https://doi.org/10.5281/ZENODO.16080102</a>.","short":"N. Baumgart, M. Lange-Hegermann, M. Mücke, Synthetic Training Dataset for Real-World Terminal Strip Object Detection, Zenodo, 2024.","havard":"N. Baumgart, M. Lange-Hegermann, M. Mücke, Synthetic Training Dataset for Real-World Terminal Strip Object Detection, Zenodo, 2024.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Baumgart, Nico</span> ; <span style=\"font-variant:small-caps;\">Lange-Hegermann, Markus</span> ; <span style=\"font-variant:small-caps;\">Mücke, Mike</span>: <i>Synthetic Training Dataset for Real-World Terminal Strip Object Detection</i> : Zenodo, 2024"},"status":"public","date_created":"2026-06-16T14:07:26Z","publisher":"Zenodo","abstract":[{"text":"\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","lang":"eng"}]}]