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Authors: Muhammad Aqeel ; Shakiba Sharifi ; Marco Cristani and Francesco Setti

Affiliation: Dept. of Engineering for Innovation Medicine, University of Verona, Strada le Grazie 15, Verona, Italy

Keyword(s): Robust Anomaly Detection, Self-Supervised Learning, Iterative Refinement Process, Industrial Quality Control.

Abstract: This study introduces the Self-Supervised Iterative Refinement Process (IRP), a robust anomaly detection methodology tailored for high-stakes industrial quality control. The IRP leverages self-supervised learning to improve defect detection accuracy by employing a cyclic data refinement strategy that iteratively removes misleading data points, thereby improving model performance and robustness. We validate the effectiveness of the IRP using two benchmark datasets, Kolektor SDD2 (KSDD2) and MVTec-AD, covering a wide range of industrial products and defect types. Our experimental results demonstrate that the IRP consistently outperforms traditional anomaly detection models, particularly in environments with high noise levels. This study highlights the potential of IRP to significantly enhance anomaly detection processes in industrial settings, effectively managing the challenges of sparse and noisy data.

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Paper citation in several formats:
Aqeel, M., Sharifi, S., Cristani, M. and Setti, F. (2025). Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3; ISSN 2184-4321, SciTePress, pages 173-183. DOI: 10.5220/0013178100003912

@conference{visapp25,
author={Muhammad Aqeel and Shakiba Sharifi and Marco Cristani and Francesco Setti},
title={Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={173-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013178100003912},
isbn={978-989-758-728-3},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control
SN - 978-989-758-728-3
IS - 2184-4321
AU - Aqeel, M.
AU - Sharifi, S.
AU - Cristani, M.
AU - Setti, F.
PY - 2025
SP - 173
EP - 183
DO - 10.5220/0013178100003912
PB - SciTePress

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