Visual anomaly detection has emerged as a highly applicable solution in practical industrial manufacturing, owing to its notable effectiveness and efficiency. However, it also presents several challenges and uncertainties. To address the complexity of anomaly types and the high cost associated with data annotation, this paper introduces a self-supervised learning framework called TDAD, based on a two-stage diffusion model. TDAD consists of three key components: anomaly synthesis, image reconstruction, and defect segmentation. It is trained end-to-end, with the goal of improving pixel-level segmentation accuracy of anomalies and reducing false detection rates. By synthesizing anomalies from normal samples, designing a diffusion model-based reconstruction network, and incorporating a multiscale semantic feature fusion module for defect segmentation, TDAD achieves state-of-the-art performance in image-level detection and anomaly localization on challenging and widely used datasets such as MVTec and VisA benchmarks.

中文翻译：

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TDAD：使用两阶段扩散模型进行自我监督工业异常检测


视觉异常检测因其显著的有效性和效率而成为实际工业制造中高度适用的解决方案。然而，它也带来了一些挑战和不确定性。为了解决异常类型的复杂性和与数据注释相关的高成本，本文引入了一种基于两阶段扩散模型的名为 TDAD 的自我监督学习框架。TDAD 由三个关键组件组成：异常合成、图像重建和缺陷分割。它经过端到端训练，目标是提高异常的像素级分割精度并降低误检率。通过从正常样本中合成异常，设计基于扩散模型的重建网络，并结合多尺度语义特征融合模块进行缺陷分割，TDAD 在具有挑战性且广泛使用的数据集（如 MVTec 和 VisA 基准）上实现了最先进的图像级检测和异常定位性能。