Surface defect detection

Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of visual inspection. Deep-learning methods have become the most suitable approaches for this task. They allow the inspection system to learn to detect the surface anomaly by simply showing it a number of exemplar images.

Our research explores deep-learning methods for different industrial applications of surface-defect detection:

Industrial surface defect detection

The developed methods allow specialization for large defect detection on various indistrual items such as cracks, smudges, imperfections etc. The methods are learning-based and are thus robust, run realtime and are applicable to a wide range of real problems.

Defect detection for reflective surfaces

We are designing novel deep architectures for detection of smooth deformations on reflective surfaces like dents. The methods are learning-based and are thus robust, run realtime and are applicable to a wide range of real problems.