FABLE : Fabric Anomaly Detection Automation Process

• URL: http://arxiv.org/abs/2306.10089v1
• Date: Fri, 16 Jun 2023 13:35:46 GMT
• Title: FABLE : Fabric Anomaly Detection Automation Process
• Authors: Simon Thomine, Hichem Snoussi and Mahmoud Soua
• Abstract summary: Unsupervised anomaly in industry has been a concerning topic and a stepping stone for high performance industrial automation process. In this paper, we propose an automation process for industrial fabric texture defect detection with a specificity-learning process.
• Score: 4.243356707599485
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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