On the Suitability of Different Features for Anomaly Detection in Wire Ropes

Automatic visual inspection of wire ropes is an important but challenging task, as anomalies in the rope are usually unobtrusive. 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. Furthermore, the performance of different well-established features from the field of textural defect detection are compared with respect to this task. The faultless rope structure is thereby modeled by a Gaussian mixture model and outliers are regarded as anomaly. To prove the practical applicability, a careful evaluation of the presented approach is performed on real-life rope data. In doing so, a special interest was put on the robustness of the model with respect to unintentional outliers in the training and on its generalization ability given further data from an identically constructed rope. The results prove good recognition rates accompanied by a high generalization ability and robustness to outliers in the training set.