When More References Hurt: Contamination-Aware DINOv2 Memory Banks for Few-Shot Steel Defect Detection

2026-08-22

AI Summary

研究通过筛选和合并可信正常图像的补丁来减少工业图像中未验证参考库的污染，提高钢铁缺陷检测的准确性。

Abstract

Patch-memory anomaly detectors assume that their reference bank is normal, an assumption that is difficult to guarantee when additional industrial images are unverified. We study whether a few trusted normal images can safely recover useful normal patches from such references without defect masks. Starting from the DINOv2 patch-memory formulation used by AnomalyDINO, we score candidate patches by distance to a clean seed bank, discard the most suspicious 20%, merge the retained patches with the seed, and enforce a fixed budget by greedy coreset selection. On Severstal, naive additional references contain 9.46% anomalous patches; the proposed trim rejects 78.1\% of them and reduces residual contamination to 2.59%. At an equal 51,200-patch development budget, the proposed bank reaches 0.1084 AUPRC versus 0.0950 for naive expansion, 0.0952 for random removal, and 0.1030 for eight clean images. Injecting only 0.5\% anomalous patches into a clean bank reduces AUPRC from 0.1030 to 0.0759. On all five completed held-out pairs, the proposed bank improves over naive expansion, with a mean gain of 0.0142 AUPRC. Reference purity is therefore a first-order design variable, and unverified images are useful only when their contribution is filtered explicitly.

Problem

• patch-memory anomaly detectors
• reference bank
• unverified industrial images
• trusted normal images
• defect masks

Innovation

• Contamination-Aware
• DINOv2 Memory Banks
• Few-Shot Learning
• Steel Defect Detection
• Anomaly Detection