Anomaly detection has a wide range of applications and is especially important in industrial quality inspection. Currently, many top-performing anomaly-detection models rely on feature-embedding methods. However, these methods do not perform well on datasets with large variations in object locations. Reconstruction-based methods use reconstruction errors to detect anomalies without considering positional differences between samples. In this study, a reconstruction-based method using the noise-to-norm paradigm is proposed, which avoids the invariant reconstruction of anomalous regions. Our reconstruction network is based on M-net and incorporates multiscale fusion and residual attention modules to enable end-to-end anomaly detection and localization. Experiments demonstrate that the method is effective in reconstructing anomalous regions into normal patterns and achieving accurate anomaly detection and localization. On the MPDD and VisA datasets, our proposed method achieved more competitive results than the latest methods, and it set a new state-of-the-art standard on the MPDD dataset.

翻译：异常检测具有广泛的应用场景，在工业质量检测中尤为重要。目前，许多高性能异常检测模型依赖于特征嵌入方法。然而，当数据集中的目标位置存在较大变化时，这些方法的表现并不理想。基于重建的方法通过重建误差来检测异常，但未考虑样本间的位置差异。本研究提出了一种基于“噪声到正常”范式的重建方法，该方法避免了异常区域的恒等重建。我们采用M-net作为重建网络的基础结构，并融合多尺度融合模块与残差注意力模块，从而实现端到端的异常检测与定位。实验结果表明，该方法能够有效地将异常区域重建为正常模式，并实现精准的异常检测与定位。在MPDD和VisA数据集上，我们提出的方法取得了比现有最新方法更具竞争力的结果，并在MPDD数据集上树立了新的最优性能基准。