| Procedural synthetic training data generation for AI-based defect detection in industrial surface inspection O Schmedemann, M Baaß, D Schoepflin, T Schüppstuhl Procedia CIRP 107, 1101-1106, 2022 | 8 | 2022 |
| Anomaly detection for industrial surface inspection: Application in maintenance of aircraft components F Kähler, O Schmedemann, T Schüppstuhl Procedia CIRP 107, 246-251, 2022 | 4 | 2022 |
| Configuration and enablement of vision sensor solutions through a combined simulation based process chain J Gierecker, D Schoepflin, O Schmedemann, T Schüppstuhl Annals of Scientific Society for Assembly, Handling and Industrial Robotics …, 2022 | 3 | 2022 |
| Development of new means regarding sensor positioning and measurement data evaluation–automation of industrial endoscopy L Bath, O Schmedemann, T Schüppstuhl wt Werkstattstechnik online 111 (9), 644-649, 2021 | 3 | 2021 |
| Deep anomaly detection for endoscopic inspection of cast iron parts O Schmedemann, M Miotke, F Kähler, T Schüppstuhl International Conference on Flexible Automation and Intelligent …, 2022 | 1 | 2022 |
| Adapting synthetic training data in deep learning-based visual surface inspection to improve transferability of simulations to real-world environments O Schmedemann, S Schlodinski, D Holst, T Schüppstuhl Automated Visual Inspection and Machine Vision V 12623, 25-35, 2023 | | 2023 |
| Industrial Segment Anything--a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul K Moenck, A Wendt, P Prünte, J Koch, A Sahrhage, J Gierecker, ... arXiv preprint arXiv:2307.12674, 2023 | | 2023 |
Thorsten SchüppstuhlProfessor Produktionstechnik, TU HamburgBestätigte E-Mail-Adresse bei tuhh.de
