FABLE : Fabric Anomaly Detection Automation Process
Simon Thomine Hichem Snoussi Mahmoud Soua
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.