Challenging Anomaly Detection in Wire Ropes Using Linear Prediction Combined with One-class Classification

Automatic visual inspection has gathered a high importance in many fields of industrial applications. Especially in security relevant applications visual inspection is obligatory. Unfortunately, this task currently bears also a risk for the human inspector, as in the case of visual rope inspection. The huge and heavy rope is mounted in great height, or it is directly connected with running machines. Furthermore, the defects and anomalies are so inconspicuous, that even for a human expert this is a very demanding task. For this reason, we present an approach for the automatic detection of defects or anomalies in wire ropes. Features, which incorporate context-information from the preceding rope region, are extracted with help of linear prediction. These features are then used to learn the faultless and normal structure of the rope with help of a oneclass classification approach. Two different learning strategies, the K-means clustering and a Gaussian mixture model, are used and compared. The evaluation is done on real rope data from a ropeway. Our first results considering this demanding task show that it is possible to exclude more than 90 percent of the rope as faultless.