FABLE : Fabric Anomaly Detection Automation Process

TL;DR

This paper introduces FABLE, an automated, domain-generalized fabric anomaly detection system that quickly adapts to new textile types without extensive retraining, achieving state-of-the-art results.

Contribution

It presents a novel automation process combining domain-generalization with specific learning for fabric defect detection, enabling rapid and precise anomaly identification.

Findings

Achieved state-of-the-art performance in fabric anomaly detection

Enabled fast adaptation to new fabric types without retraining

Developed a self-evaluation method for defect creation and re-training prevention

Abstract

Unsupervised anomaly in industry has been a concerning topic and a stepping stone for high performance industrial automation process. The vast majority of industry-oriented methods focus on learning from good samples to detect anomaly notwithstanding some specific industrial scenario requiring even less specific training and therefore a generalization for anomaly detection. The obvious use case is the fabric anomaly detection, where we have to deal with a really wide range of colors and types of textile and a stoppage of the production line for training could not be considered. In this paper, we propose an automation process for industrial fabric texture defect detection with a specificity-learning process during the domain-generalized anomaly detection. Combining the ability to generalize and the learning process offer a fast and precise anomaly detection and segmentation. The main contributions of this paper are the following: A domain-generalization texture anomaly detection method achieving the state-of-the-art performances, a fast specific training on good samples extracted by the proposed method, a self-evaluation method based on custom defect creation and an automatic detection of already seen fabric to prevent re-training.