用双分布建模和扩散生成，提升工业缺陷检测精度
ExDD: Explicit Dual Distribution Learning for Surface Defect Detection via Diffusion Synthesis
- 显式建模正常与异常双重特征分布，打破均匀异常假设
- 用扩散模型生成100个合成缺陷样本，使检测准确率达97.7% P-AUROC
- 适合工业缺陷检测场景，尤其数据稀缺时效果显著
工业缺陷检测系统在单类异常检测范式下面临严峻挑战，该范式假设异常分布均匀，在真实制造环境中常因数据稀缺而表现不佳。我们提出ExDD（显式双分布学习）框架，通过显式建模正常与异常特征的双重分布，克服了传统方法对异常分布均匀性的错误假设。采用并行记忆库捕捉正常与异常模式的统计差异，并利用领域特定文本条件的潜在扩散模型，生成符合工业上下文的内分布合成缺陷，缓解数据稀缺问题。引入邻域感知比率评分机制，融合距离度量的互补信息，增强既偏离正常又与已知缺陷相似区域的信号。在KSDD2数据集上的实验表明，ExDD性能优异（I-AUROC达94.2%，P-AUROC达97.7%），最优增广样本数为100个。
原文摘要 · Abstract (English)
Industrial defect detection systems face critical limitations when confined to one-class anomaly detection paradigms, which assume uniform outlier distributions and struggle with data scarcity in real-world manufacturing environments. We present ExDD (Explicit Dual Distribution), a novel framework that transcends these limitations by explicitly modeling dual feature distributions. Our approach leverages parallel memory banks that capture the distinct statistical properties of both normality and anomalous patterns, addressing the fundamental flaw of uniform outlier assumptions. To overcome data scarcity, we employ latent diffusion models with domain-specific textual conditioning, generating in-distribution synthetic defects that preserve industrial context. Our neighborhood-aware ratio scoring mechanism elegantly fuses complementary distance metrics, amplifying signals in regions exhibiting both deviation from normality and similarity to known defect patterns. Experimental validation on KSDD2 demonstrates superior performance (94.2% I-AUROC, 97.7% P-AUROC), with optimal augmentation at 100 synthetic samples. https://github.com/aqeeelmirza/ExDD-Defect-Detection
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