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 https://github.com/zhangzilongc/MMR.

翻译：工业异常检测（IAD）对于自动化工业质量检验至关重要。数据集的多样性是开发全面IAD算法的基础。现有IAD数据集侧重于数据类别的多样性，而忽视了同一数据类别内域的多样性。为弥补这一不足，本文提出航空发动机叶片异常检测（AeBAD）数据集，包含两个子数据集：单叶片数据集和叶片视频异常检测数据集。与现有数据集相比，AeBAD具有以下两个特点：1）目标样本未对齐且尺度不同；2）测试集中正常样本的分布与训练集之间存在域偏移，该偏移主要由光照和视角变化引起。基于该数据集，我们观察到当测试集中正常样本的域发生偏移时，当前最先进的IAD方法表现出局限性。为解决此问题，我们提出一种名为掩码多尺度重建（MMR）的新方法，通过掩码重建任务增强模型推断正常样本中补丁间因果关系的能力。在AeBAD数据集上，MMR相比SOTA方法取得了更优性能。此外，在MVTec AD数据集上，MMR在检测不同类型异常时与SOTA方法具有竞争力的性能。代码和数据集可在https://github.com/zhangzilongc/MMR获取。