{"citation":{"apa":"Baumgart, N., Lange-Hegermann, M., & Mücke, M. (2024). Synthetic Training Dataset for Real-World Terminal Strip Object Detection. Zenodo. https://doi.org/10.5281/ZENODO.16080102","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. Synthetic Training Dataset for Real-World Terminal Strip Object Detection. Zenodo, 2024, https://doi.org/10.5281/ZENODO.16080102.","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":"Baumgart, Nico ; Lange-Hegermann, Markus ; Mücke, Mike: Synthetic Training Dataset for Real-World Terminal Strip Object Detection : Zenodo, 2024","ieee":"N. Baumgart, M. Lange-Hegermann, and M. Mücke, Synthetic Training Dataset for Real-World Terminal Strip Object Detection. Zenodo, 2024. doi: 10.5281/ZENODO.16080102.","bjps":"Baumgart N, Lange-Hegermann M and Mücke M (2024) Synthetic Training Dataset for Real-World Terminal Strip Object Detection. Zenodo.","chicago":"Baumgart, Nico, Markus Lange-Hegermann, and Mike Mücke. Synthetic Training Dataset for Real-World Terminal Strip Object Detection. Zenodo, 2024. https://doi.org/10.5281/ZENODO.16080102.","chicago-de":"Baumgart, Nico, Markus Lange-Hegermann und Mike Mücke. 2024. Synthetic Training Dataset for Real-World Terminal Strip Object Detection. Zenodo. doi:10.5281/ZENODO.16080102, .","ufg":"Baumgart, Nico/Lange-Hegermann, Markus/Mücke, Mike: Synthetic Training Dataset for Real-World Terminal Strip Object Detection, o. O. 2024.","ama":"Baumgart N, Lange-Hegermann M, Mücke M. Synthetic Training Dataset for Real-World Terminal Strip Object Detection. Zenodo; 2024. doi:10.5281/ZENODO.16080102"},"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"}],"author":[{"full_name":"Baumgart, Nico","first_name":"Nico","id":"88090","last_name":"Baumgart"},{"full_name":"Lange-Hegermann, Markus","first_name":"Markus","id":"71761","last_name":"Lange-Hegermann"},{"last_name":"Mücke","first_name":"Mike","full_name":"Mücke, Mike"}],"title":"Synthetic Training Dataset for Real-World Terminal Strip Object Detection","department":[{"_id":"DEP5015"}],"year":"2024","keyword":["Object Detection","Image Synthesis","Domain Randomization","Domain Gap","Terminal Strip"],"doi":"10.5281/ZENODO.16080102","user_id":"83781","_id":"13824","type":"research_data","date_updated":"2026-06-16T14:11:28Z"}