Reverse Distillation Based Detection of Anomalies on a Newly Developed Fabric Dataset

Neural networks have become a main tool for anomaly detection in images. One idea is to use an auto encoder for detecting irregularities in textures, and knowledge distillation to calculate an anomaly score. The method of (Thomine and Snoussi, 2024), trained on the MVTec carpet dataset, is our starting point. We modify this method for our new Textile Stripe (TS) real-world textile dataset. TS contains images of anomalies of different shapes in a textile. It differs from the MVTec carpet class in properties such as image dimensions, illumination, or alignment of the texture to the image edges. We propose a newly developed cropping and anomaly map reconcatenation technique with overlapping and test the self-ensembling method of (Meininger and Tim-ofte, 2024). The modifications made are also cross-validated on the MVTec dataset. For generalization experiments, two smaller test datasets, called G1FW and G2SR, containing normal and anomalous images on different fabrics are provided. The approaches delivered adequate performance in terms of AUROC.