Surface anomaly detection is a vital component in manufacturing inspection. Current discriminative methods follow a two-stage architecture composed of a reconstructive network followed by a discriminative network that relies on the reconstruction output. Currently used reconstructive networks often produce poor reconstructions that either still contain anomalies or lack details in anomaly-free regions. Discriminative methods are robust to some reconstructive network failures, suggesting that the discriminative network learns a strong normal appearance signal that the reconstructive networks miss. We reformulate the two-stage architecture into a single-stage iterative process that allows the exchange of information between the reconstruction and localization. We propose a novel transparency-based diffusion process where the transparency of anomalous regions is progressively increased, restoring their normal appearance accurately while maintaining the appearance of anomaly-free regions using localization cues of previous steps. We implement the proposed process as TRANSparency DifFUSION (TransFusion), a novel discriminative anomaly detection method that achieves state-of-the-art performance on both the VisA and the MVTec AD datasets, with an image-level AUROC of 98.5% and 99.2%, respectively. Code: https://github.com/MaticFuc/ECCV_TransFusion

翻译：表面异常检测是制造检测中的关键组成部分。当前判别式方法采用两阶段架构，由重建网络和依赖重建输出的判别网络组成。现有重建网络常产生质量不佳的重建结果，可能仍包含异常区域或在正常区域缺乏细节。判别式方法对某些重建网络失效具有鲁棒性，这表明判别网络学习到了重建网络遗漏的强正常外观信号。我们将两阶段架构重构为单阶段迭代过程，允许重建与定位之间进行信息交换。我们提出一种新颖的基于透明度的扩散过程，通过逐步增加异常区域的透明度，在利用前序步骤定位线索保持正常区域外观的同时，准确恢复异常区域的正常外观。我们将该过程实现为TRANSparency DifFUSION（TransFusion），这是一种新型判别式异常检测方法，在VisA和MVTec AD数据集上均达到最先进性能，图像级AUROC分别达到98.5%和99.2%。代码：https://github.com/MaticFuc/ECCV_TransFusion