2026-04-30T21:23:47Zhttps://www.radar-service.eu/oai/OAIHandler10.35097/13352023-11-15T14:44:56Zradar4kit
10.35097/1335
Schlagenhauf, Tobias
Tobias
Schlagenhauf
Institut für Produktionstechnik
Domain-Shift-Dataset of Defects on Metallic Surfaces (MSD-Shift)
Karlsruhe Institute of Technology
2022
2023
Engineering
Transfer Learning
Domain Shift
Domain Generalization
Domain Adaption
Machine Learning
Deep Learning
Metallic Surfaces
Defects
Open Access
Creative Commons Attribution Non Commercial No Derivatives 4.0 International
Schlagenhauf, Tobias
The dataset maps two different surfaces (domains) from mechanical engineering (Surfaces of Ball Screw Drives (BSD); Surface of Metallic Semi-finished Products (SEV)). The domains each contain image data with and image data without surface defects. The surfaces differ, but the defect features are similar across the domains. The dataset is thus suitable for investigating questions in the context of domain shift, domain generalization, and transfer learning. The dataset is structured as follows: BSD with defect (5240); BSD without defect (1896) SEV with defect (2018); SEV without defect (21806). Attention: The Domains are not balanced over classes. The SEV images are each 224x224 pixel RGB PNG files. THE BSD images are each 150x150 pixel RGB PNG files. The SEV data are excerpts from the Severstal dataset.
10.5445/IR/1000147763
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