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:

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.

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.