让缺陷生成更真实，背景不影响背景，提升工业缺陷检测效果
Background-Aware Defect Generation for Robust Industrial Anomaly Detection
- 用解耦损失分离背景修复与缺陷生成，实现可控合成
- 在MVTec AD和Loco上生成缺陷更真实，检测准确率更高
- 适合需要高精度缺陷检测的工业视觉场景
工业异常检测因标注异常数据稀缺而困难。生成模型可通过合成真实缺陷样本缓解此问题，但现有方法常忽略缺陷与背景间的相互作用，导致异常不真实，尤其在需上下文一致性的逻辑异常场景中。为此，本文提出一种新的背景感知缺陷生成框架，使背景影响缺陷去噪过程，却不改变自身，从而在保持结构完整性的同时实现真实合成。该方法利用解耦损失分离背景去噪与缺陷生成，通过DDIM反演实现可控缺陷生成。理论证明该方法可保持背景保真度并生成上下文一致的缺陷。在MVTec AD和MVTec Loco基准上的大量实验验证了其在缺陷生成质量和异常检测性能上优于现有技术。
原文摘要 · Abstract (English)
Detecting anomalies in industrial settings is challenging due to the scarcity of labeled anomalous data. Generative models can mitigate this issue by synthesizing realistic defect samples, but existing approaches often fail to model the crucial interplay between defects and their background. This oversight leads to unrealistic anomalies, especially in scenarios where contextual consistency is essential (i.e., logical anomaly). To address this, we propose a novel background-aware defect generation framework, where the background influences defect denoising without affecting the background itself by ensuring realistic synthesis while preserving structural integrity. Our method leverages a disentanglement loss to separate the background' s denoising process from the defect, enabling controlled defect synthesis through DDIM Inversion. We theoretically demonstrate that our approach maintains background fidelity while generating contextually accurate defects. Extensive experiments on MVTec AD and MVTec Loco benchmarks validate our mehtod's superiority over existing techniques in both defect generation quality and anomaly detection performance.
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