One persistent obstacle in industrial quality inspection is the detection of anomalies. In real-world use cases, two problems must be addressed: anomalous data is sparse and the same types of anomalies need to be detected on previously unseen objects. Current anomaly detection approaches can be trained with sparse nominal data, whereas domain generalization approaches enable detecting objects in previously unseen domains. Utilizing those two observations, we introduce the hybrid task of domain generalization on sparse classes. To introduce an accompanying dataset for this task, we present a modification of the well-established MVTec AD dataset by generating three new datasets. In addition to applying existing methods for benchmark, we design two embedding-based approaches, Spatial Embedding MLP (SEMLP) and Labeled PatchCore. Overall, SEMLP achieves the best performance with an average image-level AUROC of 87.2 % vs. 80.4 % by MIRO. The new and openly available datasets allow for further research to improve industrial anomaly detection.

翻译：工业质量检测中一个长期存在的障碍是异常检测。在实际应用场景中，必须解决两个问题：异常数据稀疏，且需要在先前未见过的物体上检测相同类型的异常。当前的异常检测方法可以利用稀疏的正常数据进行训练，而领域泛化方法则能够检测先前未见过的领域中的物体。基于这两点观察，我们引入了稀疏类别领域泛化的混合任务。为了为此任务提供一个配套数据集，我们通过对成熟的MVTec AD数据集进行修改，生成了三个新的数据集。除了应用现有方法进行基准测试外，我们设计了两种基于嵌入的方法：空间嵌入多层感知机（SEMLP）和带标签的PatchCore。总体而言，SEMLP取得了最佳性能，其平均图像级AUROC为87.2%，而MIRO为80.4%。这些新公开可用的数据集为进一步研究以改进工业异常检测提供了条件。