Analysis of Structural Dependencies for the Automatic Visual Inspection of Wire Ropes

Automatic visual inspection is an arising field of research. Especially in security relevant applications, an automation of the inspection process would be a great benefit. For wire ropes, a first step is the acquisition of the curved surface with several cameras located all around the rope. Because most of the visible defects in such a rope are very inconspicuous, an automatic defect detection is a very challenging problem. As in general there is a lack of defective training data, most of the presented ideas for automatic rope inspection are embedded in a one-class classification framework. However, none of these methods makes use of the context information which results from the fact that all camera views image the same rope. In contrast to an individual analysis of each camera view, this work proposes the simultaneous analysis of all available camera views with the help of a vector autoregressive model. Moreover, various dependency analysis methods are used to give consideration to the regular rope structure and to deal with the high dimensionality of the problem. These dependencies are then used as constraints for the vector autoregressive model, which results in a sparse but powerful detection system. The proposed method is evaluated by using real wire rope data and the conducted experiments show that our approach clearly outperforms all previously presented methods.