One-Class Classification for Anomaly Detection in Wire Ropes with Gaussian Processes in a Few Lines of Code

Anomaly Detection in Wire Ropes is an important problem. Detecting suspicious anomalies in the rope surface is challenging because of the variety of its visual appearance caused by reflections or mud on the rope surface. This hinders the discrimination between uncritical variations and small defects within the rope surface enormously. The fact that nearly no defective samples are available to train a supervised system relates this problem to the concept of one-class classification (OCC). In this work we show how to utilize one-class classification with Gaussian processes (GP) to detect anomalies in wire ropes. The method allows modeling the distribution of non-defective data in a non-parametric manner. Furthermore, it is really easy to implement (few lines of code), embedded in a Bayesian framework, and can be used with arbitrary kernel functions. Therefore, it is suitable for a wide range of defect localization applications. Our experiments, performed on two real ropes, demonstrate that the GP framework for OCC clearly outperforms former approaches for anomaly detection in wire ropes. The obtained results are comparable with or even outperform those obtained with the Support Vector Data Description, which is the state of the art reference in the field of one-class classification.