Using Artificial Intelligence to Detect Defects on Surfaces Better and Faster

BMBF Project SynosIs – Synthetic, Optically Realistic Image Data of Surface Structures for AI-Based Inspection Systems

In the SynosIs project, we are working with our research partners to develop an inspection system based on artificial intelligence (AI) that detects defects on surfaces quickly and automatically. The project is funded by the German Federal Ministry of Education and Research (BMBF).

Image processing systems for quality control in production are solving increasingly complex tasks and, due to short development cycles, must react ever more quickly to new products and defect patterns. Typical inspection tasks include the detection of surface defects and deviations from the target geometry. Artificial intelligence (AI) is used successfully in image recognition, processing and understanding to some extent. However, training a robust, automatic AI-based inspection system requires a large amount of manually annotated image data, especially one that is representative of all defect types.


Challenges of an AI-Based Inspection System:

  1. Many defects occur very infrequently. In particular, defects that are critical to component safety are observed less frequently because they are avoided as much as possible on the production side.
  2. Manual annotation of exemplary defects is therefore not only time-consuming, but also leads to inconsistent results in some cases.
  3. In particular, the decisions of the trained system may be biased by the lack of defect images in the training.

It is therefore desirable to automatically generate realistic synthetic image data together with the corresponding annotations. Currently, the simulation of such image data requires multiple manual interactions. Moreover, synthetic generation of surface defects is an open research topic.

Approaching Reality Automatically With AI and Building up an Image Data Portfolio

This interdisciplinary project combines methods from physics, mathematics and computer science to generate synthetic images of typical defects on metallic surfaces with unprecedented realism. This image data will be made available to the general public and can be used for training and validation of AI models. This simplifies and accelerates the development of AI models for optical surface inspection.

For the first time, the image data portfolio will provide guaranteed correct and objectively annotated defect images. In addition, this synthetic data enables an objective quantitative comparison of different solutions as well as the prediction of the probability of detection (POD) of surface defects depending on defect, component and inspection system.

Our Expertise: Define Defects, Classify, Train AI

In the initial stage, our ITWM experts, in close cooperation with Fraunhofer IOF, design two free-form specimens with complex geometry for validation. The defects to be introduced are defined in such a way that, on the one hand, typical defect classes such as impact points, scratches, and (oil) contamination are represented, and, on the other hand, the manufacturing effort remains reasonable. In the next step, the researchers train the AI-based inspection system using synthetic image data. Another task is to validate the inspection solution and simulate a representative portfolio of synthetic image data in collaboration with the TU Kaiserslautern.

This project has a clear application scenario, but is still designed as a basic research project because we are developing new methods in all three disciplines. In addition to data synthesis, the project addresses the topic of »parameters for measuring suitability, quality or bias-freeness of the data«, since suitability, quality and bias-freeness of the data are continuously checked and guaranteed for both the defect-free surfaces and the defects to be introduced.