Combining Structure and Appearance for Anomaly Detection in Wire Ropes

Abstract. We present a new approach for anomaly detection in the con-text of visual surface inspection. In contrast to existing, purely appearance-based 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