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Communication Dans Un Congrès Année : 2023

FABLE : Fabric Anomaly Detection Automation Process

Simon Thomine
  • Fonction : Auteur
  • PersonId : 1262670
Hichem Snoussi
Mahmoud Soua
  • Fonction : Auteur
  • PersonId : 1262671

Résumé

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 retraining .
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Dates et versions

hal-04131483 , version 1 (16-06-2023)

Identifiants

  • HAL Id : hal-04131483 , version 1

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Simon Thomine, Hichem Snoussi, Mahmoud Soua. FABLE : Fabric Anomaly Detection Automation Process. 2023 International Conference on Control, Automation and Diagnosis (ICCAD 23'), May 2023, Rome, Italy. ⟨hal-04131483⟩

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