Combining Structure and Appearance for Anomaly Detection in Wire Ropes

We present a new approach for anomaly detection in the context of visual surface inspection. In contrast to existing, purely appearancebased approaches, we explicitly integrate information about the object geometry. The method is tested using the example of wire rope inspection as this is a very challenging problem. A perfectly regular 3d model of the rope is aligned with a sequence of 2d rope images to establish a direct connection between object geometry and observed rope appearance. The surface appearance can be physically explained by the rendering equation. Without a need for knowledge about the illumination setting or the reflectance properties of the material we are able to sample the rendering equation. This results in a probabilistic appearance model. The density serves as description for normal surface variations and allows a robust localization of rope surface defects. We evaluate our approach on real-world data from real ropeways. The accuracy of our approach is comparable to that of a human expert and outperforms all other existing approaches. It has an accuracy of 95% and a low false-alarm-rate of 1.5%, whereupon no single defect is missed.