HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection Dataset
This dataset addresses the gap between existing industrial anomaly detection datasets and real factory conditions for researchers and practitioners in the field.
The authors tackled the problem of limited real-world effectiveness in multi-class unsupervised anomaly detection (MUAD) methods by introducing the HSS-IAD dataset, which contains 8,580 images of metallic-like industrial parts with precise anomaly annotations and variations in structure and appearance.
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.