FABLE : Fabric Anomaly Detection Automation Process
This addresses the problem of fabric defect detection for industrial automation, enabling efficient handling of diverse textiles without production stoppages, though it appears incremental by building on existing domain-generalization methods.
The paper tackles fabric anomaly detection in industrial settings by proposing an automation process that combines domain-generalized anomaly detection with a specificity-learning process, achieving state-of-the-art performance for fast and precise detection and segmentation.
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.