面向工业异常检测与定位的噪声到正常重建方法
Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
AI总结：
本文提出一种基于噪声到正常范式的M-net重建方法，结合多尺度融合与残差注意力模块，在MPDD和VisA数据集上实现了更优的工业异常检测与定位性能。
摘要：
异常检测具有广泛的应用，在工业质量检测中尤为重要。目前，许多性能领先的异常检测模型依赖特征嵌入方法。然而，这些方法在物体位置变化较大的数据集上表现不佳。基于重建的方法利用重建误差来检测异常，而不考虑样本之间的位置差异。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不变重建。我们的重建网络基于M-net，并融合了多尺度融合和残差注意力模块，以实现端到端的异常检测与定位。实验表明，该方法能够有效地将异常区域重建为正常模式，并实现准确的异常检测与定位。在MPDD和VisA数据集上，我们提出的方法取得了比最新方法更具竞争力的结果，并在MPDD数据集上设立了新的最优标准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
