Elastic Shape Analysis for Anomaly Detection in Fabric Images

In this paper, the problem of quality control in the textile industrial field is addressed. Because of the general unavailability of labelled data from real production plants and the imbalanced nature of the problem, this task is faced with novelty detection methods that monitor the behaviour of the system and identify whether shifts from the nominal conditions arise. In particular, we utilize techniques from Elastic Shape Analysis to analyse the shapes created by the yarns intersections of the fabrics and to extract features used to define distance metrics that quantify the shapes variability. The proposed approach is applied to images of four different textiles, where only some defect free images are needed for the training phase. The results of this preliminary study confirm the effectiveness of the proposed approach.