HSS-IAD：异构同类别工业异常检测数据集
HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection Dataset
AI总结：
针对现有工业异常检测数据集无法反映真实工厂多结构、同类别零件缺陷的问题，提出HSS-IAD数据集，含8580张类金属零件图像及标注，支持合成异常生成，评估显示其能弥合现有数据集与真实场景的差距。
摘要：
多类别无监督异常检测算法（MUAD）因其较低的部署成本和更高的训练效率而受到越来越多的关注。然而，由于当前工业异常检测（IAD）数据集存在局限性，MUAD方法的实际效果受到质疑。这些数据集包含许多不太可能由同一工厂生产的类别，且未能覆盖多种结构或外观。此外，缺陷未反映真实世界特征。因此，我们引入异构同类别工业异常检测（HSS-IAD）数据集，包含8580张类金属工业零件图像和精确的异常标注。这些零件具有结构和外观的变化，存在与基材高度相似的细微缺陷。我们还提供用于合成异常生成的前景图像。最后，我们在多类别和类别分离设置下评估了流行的IAD方法，证明该数据集有潜力弥合现有数据集与真实工厂条件之间的差距。数据集可在https://github.com/Qiqigeww/HSS-IAD-Dataset获取。
英文摘要：
Multi-class Unsupervised Anomaly Detection algorithms (MUAD) are receiving increasing attention due to their relatively low deployment costs and improved training efficiency. However, the real-world effectiveness of MUAD methods is questioned due to limitations in current Industrial Anomaly Detection (IAD) datasets. These datasets contain numerous classes that are unlikely to be produced by the same factory and fail to cover multiple structures or appearances. Additionally, the defects do not reflect real-world characteristics. Therefore, we introduce the Heterogeneous Same-Sort Industrial Anomaly Detection (HSS-IAD) dataset, which contains 8,580 images of metallic-like industrial parts and precise anomaly annotations. These parts exhibit variations in structure and appearance, with subtle defects that closely resemble the base materials. We also provide foreground images for synthetic anomaly generation. Finally, we evaluate popular IAD methods on this dataset under multi-class and class-separated settings, demonstrating its potential to bridge the gap between existing datasets and real factory conditions. The dataset is available at https://github.com/Qiqigeww/HSS-IAD-Dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
