In the "Visual Inspection" focus area, we are dedicated to the application of deep learning and computer vision to support industrial inspection processes. Our research approach is to adapt the latest methods from the state of AI research and develop new solutions to make visual inspections more precise, efficient and transparent.

Our goal is to develop methods for precise quality assessment and defect detection in industrial production processes based on image data. By using advanced models such as vision transformers and normalising flow methods, we focus on the reliable detection of anomalies and the identification of defects.

The challenges of visual inspection in real production environments require scalable and adaptable solutions. We develop multi-level modelling pipelines that are capable of efficiently processing large and high-resolution image data. In doing so, we are researching the generalisation of deep learning methods in order to make the models robust against different environments and disruptive factors. Another focus is on the explainability and visualisation of the results in order to strengthen confidence in the technology and enable continuous optimisation of the models.