Industrial anomaly detection (IAD) is crucial for automating industrial
quality inspection. The diversity of the datasets is the foundation for
developing comprehensive IAD algorithms. Existing IAD datasets focus on the
diversity of data categories, overlooking the diversity of domains within the
same data category. In this paper, to bridge this gap, we propose the
Aero-engine Blade Anomaly Detection (AeBAD) dataset, consisting of two
sub-datasets: the single-blade dataset and the video anomaly detection dataset
of blades. Compared to existing datasets, AeBAD has the following two
characteristics: 1.) The target samples are not aligned and at different
scales. 2.) There is a domain shift between the distribution of normal samples
in the test set and the training set, where the domain shifts are mainly caused
by the changes in illumination and view. Based on this dataset, we observe that
current state-of-the-art (SOTA) IAD methods exhibit limitations when the domain
of normal samples in the test set undergoes a shift. To address this issue, we
propose a novel method called masked multi-scale reconstruction (MMR), which
enhances the model's capacity to deduce causality among patches in normal
samples by a masked reconstruction task. MMR achieves superior performance
compared to SOTA methods on the AeBAD dataset. Furthermore, MMR achieves
competitive performance with SOTA methods to detect the anomalies of different
types on the MVTec AD dataset. Code and dataset are available at
this https URL

本文提出了 AeBAD 工业异常检测数据集，探讨了当前最优算法在检测测试集中正常样本的领域移位问题，并提出了一种名为 MMR 的新方法，通过遮蔽重构任务增强模型对正常样本中补丁之间因果关系的推断能力。该方法在 AeBAD 数据集上表现出优秀的性能。