Artificial intelligence (AI) is used very successfully in image recognition, processing and understanding. However, training an AI-based inspection system for industrial quality assurance requires large amounts of representative annotated image data for all defect types. Manual annotation is laborious and error-prone. Many defects, especially safety-critical ones, occur very rarely. Realistic synthetic image data help circumvent these problems.

We combine physics, mathematics and computer science to generate synthetic images of typical defects on metallic surfaces with unprecedented realism. These defect images that are guaranteed to be correctly and objectively annotated are available for training and validation of AI systems for optical surface inspection after the end of the project.

This project is supported by the Federal Ministry of Education and Research.

You will find more information about this and other projects of the Statistics Group on our research-website.