Anomaly detection is widely applied due to its remarkable effectiveness and efficiency in meeting the needs of real-world industrial manufacturing. We introduce a new pipeline, DiffusionAD, to anomaly detection. We frame anomaly detection as a ``noise-to-norm'' paradigm, in which anomalies are identified as inconsistencies between a query image and its flawless approximation. Our pipeline achieves this by restoring the anomalous regions from the noisy corrupted query image while keeping the normal regions unchanged. DiffusionAD includes a denoising sub-network and a segmentation sub-network, which work together to provide intuitive anomaly detection and localization in an end-to-end manner, without the need for complicated post-processing steps. Remarkably, during inference, this framework delivers satisfactory performance with just one diffusion reverse process step, which is tens to hundreds of times faster than general diffusion methods. Extensive evaluations on standard and challenging benchmarks including VisA and DAGM show that DiffusionAD outperforms current state-of-the-art paradigms, demonstrating the effectiveness and generalizability of the proposed pipeline.

翻译：异常检测因其在满足工业制造现实需求方面的高效性与有效性而得到广泛应用。我们提出了一种名为DiffusionAD的新型异常检测流水线，将异常检测问题框架化为“噪声到常态”范式——通过对比查询图像与其无缺陷近似图像间的差异来识别异常区域。该流水线在保持正常区域不变的前提下，通过从带噪受损的查询图像中重建异常区域实现上述目标。DiffusionAD包含去噪子网络与分割子网络，二者协同工作，以端到端方式直接实现直观的异常检测与定位，无需复杂后处理步骤。值得关注的是，该框架在推理阶段仅需一次扩散逆向过程步骤即可获得满意性能，其速度较通用扩散方法提升数十至数百倍。在VisA、DAGM等标准及高难度基准测试上的广泛评估表明，DiffusionAD的性能优于当前最先进的范式体系，验证了所提流水线的有效性与泛化能力。