通过伪异常感知提升零样本缺陷检测准确率
PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness
- 用伪异常构建记忆库，区分真实缺陷与正常纹理
- 在MVTec AD和VisA上优于现有零样本方法
- 适合复杂表面工业品的高精度缺陷检测
工业缺陷检测中，如何在多样异常和不同成像条件下准确识别缺陷仍是一大挑战。传统方法常因误将正常阴影和表面形变判为缺陷，导致高误报率，尤其在结构复杂的表面产品上问题更显著。为此，我们提出PA-CLIP，一种基于伪异常感知的零样本缺陷检测方法。该方法融合多尺度特征聚合策略以捕获全局与局部细节，采用两个记忆库区分背景信息（包括正常模式和伪异常）与真实异常特征，并设计决策模块以降低环境变化引起的误报，同时保持高缺陷敏感性。在MVTec AD和VisA数据集上的实验表明，PA-CLIP优于现有零样本方法，为工业缺陷检测提供稳健解决方案。
原文摘要 · Abstract (English)
In industrial anomaly detection (IAD), accurately identifying defects amidst diverse anomalies and under varying imaging conditions remains a significant challenge. Traditional approaches often struggle with high false-positive rates, frequently misclassifying normal shadows and surface deformations as defects, an issue that becomes particularly pronounced in products with complex and intricate surface features. To address these challenges, we introduce PA-CLIP, a zero-shot anomaly detection method that reduces background noise and enhances defect detection through a pseudo-anomaly-based framework. The proposed method integrates a multiscale feature aggregation strategy for capturing detailed global and local information, two memory banks for distinguishing background information, including normal patterns and pseudo-anomalies, from true anomaly features, and a decision-making module designed to minimize false positives caused by environmental variations while maintaining high defect sensitivity. Demonstrated on the MVTec AD and VisA datasets, PA-CLIP outperforms existing zero-shot methods, providing a robust solution for industrial defect detection.
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