Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control
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...
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Zusammenfassung: | 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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DOI: | 10.48550/arxiv.2408.11561 |