Abstract
Unsupervised anomaly detection plays a pivotal role in industrial defect inspection and medical image analysis, with most methods relying on the reconstruction framework. However, these methods may suffer from over-generalization, enabling them to reconstruct anomalies well, which leads to poor detection performance. To address this issue, instead of focusing solely on normality reconstruction, we propose an innovative Uncertainty-Integrated Anomaly Perception and Restoration Attention Network (URA-Net), which explicitly restores abnormal patterns to their corresponding normality. First, unlike traditional image reconstruction methods, we utilize a pre-trained convolutional neural network to extract multi-level semantic features as the reconstruction target. To assist the URA-Net learning to restore anomalies, we introduce a novel feature-level artificial anomaly synthesis module to generate anomalous samples for training. Subsequently, a novel uncertainty-integrated anomaly perception module based on Bayesian neural networks is introduced to learn the distributions of anomalous and normal features. This facilitates the estimation of anomalous regions and ambiguous boundaries, laying the foundation for subsequent anomaly restoration. Then, we propose a novel restoration attention mechanism that leverages global normal semantic information to restore detected anomalous regions, thereby obtaining defect-free restored features. Finally, we employ residual maps between input features and restored features for anomaly detection and localization. The comprehensive experimental results on two industrial datasets, MVTec AD and BTAD, along with a medical image dataset, OCT-2017, unequivocally demonstrate the effectiveness and superiority of the proposed method.
Abstract (translated)
无监督异常检测在工业缺陷检测与医学图像分析中发挥着关键作用，多数方法依赖于重建框架。然而，这些方法可能因过度泛化而能够良好重建异常样本，导致检测性能下降。针对这一问题，我们不再仅关注正常重建，而是提出了一种创新的不确定性集成异常感知与修复注意力网络（URA-Net），该网络能将异常模式显式修复为对应的正常状态。首先，不同于传统图像重建方法，我们利用预训练卷积神经网络提取多级语义特征作为重建目标。为辅助URA-Net学习异常修复，我们引入了新颖的特征级人工异常合成模块，用于生成训练用的异常样本。随后，我们提出了基于贝叶斯神经网络的新型不确定性集成异常感知模块，用于学习异常与正常特征的分布。这有助于估计异常区域与模糊边界，为后续异常修复奠定基础。接着，我们设计了新型修复注意力机制，利用全局正常语义信息修复检测到的异常区域，从而获得无缺陷的修复特征。最后，我们通过输入特征与修复特征之间的残差图实现异常检测与定位。在工业数据集MVTec AD、BTAD及医学图像数据集OCT-2017上的 comprehensive 实验结果充分证明了所提方法的有效性与优越性。
URL
https://arxiv.org/abs/2603.22840
