Papers › Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control
Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani, Francesco Setti
This study introduces the Iterative Refinement Process (IRP), a robust anomaly detection methodology designed for high-stakes industrial quality control. The IRP enhances defect detection accuracy through a cyclic data refinement strategy, iteratively removing misleading data points to improve model performance and robustness. We validate the IRP's effectiveness 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 IRP's potential to significantly enhance anomaly detection processes in industrial settings, effectively managing the challenges of sparse and noisy data.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Self-Supervised Anomaly Detection | KolektorSDD2 | IRP | AUROC | 94.0 | #2 of 2 | Archive leaderboard | report |
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