  Unsupervised texture anomaly detection has been a concerning topic in a vast
amount of industrial processes. Patterned textures inspection, particularly in
the context of fabric defect detection, is indeed a widely encountered use
case. This task involves handling a diverse spectrum of colors and textile
types, encompassing a wide range of fabrics. Given the extensive variability in
colors, textures, and defect types, fabric defect detection poses a complex and
challenging problem in the field of patterned textures inspection. In this
article, we propose a knowledge distillation-based approach tailored
specifically for addressing the challenge of unsupervised anomaly detection in
textures resembling fabrics. Our method aims to redefine the recently
introduced reverse distillation approach, which advocates for an
encoder-decoder design to mitigate classifier bias and to prevent the student
from reconstructing anomalies. In this study, we present a new reverse
distillation technique for the specific task of fabric defect detection. Our
approach involves a meticulous design selection that strategically highlights
high-level features. To demonstrate the capabilities of our approach both in
terms of performance and inference speed, we conducted a series of experiments
on multiple texture datasets, including MVTEC AD, AITEX, and TILDA, alongside
conducting experiments on a dataset acquired from a textile manufacturing
facility. The main contributions of this paper are the following: a robust
texture anomaly detector utilizing a reverse knowledge-distillation technique
suitable for both anomaly detection and domain generalization and a novel
dataset encompassing a diverse range of fabrics and defects.
