URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration Attention Network for Unsupervised Anomaly Detection

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.

Figures (15)

• Figure 1: Comparison of different unsupervised anomaly detection methods. (a) DRAEM draem. (b) MemAE MemAE. (c) The proposed method (URA-Net). URA-Net employs the U-I (Uncertainty-Integrated) Anomaly Perception module to roughly estimate anomalous regions (Mean) and ambiguous boundaries (Uncertainty). Subsequently, The Restoration Attention module utilizes global normal semantic information to restore the detected anomalies, ultimately resulting in defect-free restored images. It is noteworthy that our proposed method relies on feature reconstruction. The reconstructed images are generated by training a decoder, which is exclusively employed for visualization.
• Figure 2: Overall architecture of our URA-Net. It primarily consists of three modules: feature-level artificial anomaly synthesis module (FASM), uncertainty-integrated anomaly perception module (UIAPM), and restoration attention module (RAM). First, a pre-trained backbone is employed to transform input images into multi-level features. FASM is utilized to generate artificial anomalies at the feature level for training. Subsequently, UIAPM roughly estimates anomalous regions (Mean) and ambiguous boundaries (Uncertainty). Then RAM leverages global normal semantic information to restore detected anomalous regions and ambiguous boundaries, yielding defect-free restored features. Finally, the residuals between input features and restored ones are utilized for anomaly detection and localization.
• Figure 3: Synthesis process for feature-level artificial anomalies.
• Figure 4: Illustration of uncertainty-integrated anomaly perception module (UIAPM). UIAPM works as a probabilistic model to roughly estimate anomalous regions and ambiguous boundaries.
• Figure 5: The effectiveness of UIAPM in anomaly perception. From left to right are: anomalous image, ground truth, mean map $U$, uncertainty map $V$, mask $M_U$ generated by $U$, and the final mask $M_{final}$ combined with uncertainty.