Robustness of Different Features for One-class Classification and Anomaly Detection in Wire Ropes

Automatic visual inspection of wire ropes is an important but challenging task. Anomalies in wire ropes usually are unobtrusive and their detection is a difficult job. Certainly, a reliable anomaly detection is essential to assure the safety of the ropes. A one-class classification approach for the automatic detection of anomalies in wire ropes is presented. Different well-established features from the field of textural defect detection are compared to context-sensitive features extracted by linear prediction. They are used to learn a Gaussian mixture model which represents the faultless rope structure. Outliers are regarded as anomaly. To evaluate the robustness of the method, a training set containing intentionally added, defective samples is used. The generalization ability of the learned model, which is important for practical life, is exploited by testing the model on different data sets from identically constructed ropes. All experiments were performed on real-life rope data. The results prove a high generalization ability, as well as a good robustness to outliers in the training set. The presented approach can exclude up to 90 percent of the rope as faultless without missing one single defect.