Datasets › AeBAD
AeBAD (Aero-engine Blade Anomaly Detection Dataset)
Unlike previous datasets that focus on detecting the diversity of defect categories (like MVTec AD and VisA), AeBAD is centered on the diversity of domains within the same data category.
The aim of AeBAD is to automatically detect abnormalities in the blades of aero-engines, ensuring their stable operation. AeBAD consists of two sub-datasets: the single-blade dataset (AeBAD-S) and the video anomaly detection of blades (AeBAD-V). AeBAD-S comprises images of single blades of different scales, with a primary feature being that the samples are not aligned. Furthermore, 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. AeBAD-V, on the other hand, includes videos of blades assembled on the blisks of aero-engines, with the aim of detecting blade anomalies during blisk rotation. A distinctive feature of AeBAD-V is that the shooting view in the test set differs from that in the training set.
Benchmarks archive 2025-07-28
All 3 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Anomaly Detection | AeBAD-S | MSFR Detection AUROC 87.1 | Multi-scale feature reconstruction network for... | Ehteshamciitwah/MSFR | 8 | Compare |
| Anomaly Detection | AeBAD-V | MMR Detection AUROC 78.2 | Industrial Anomaly Detection with Domain Shift: A... | zhangzilongc/MMR | 7 | Compare |
| Unsupervised Anomaly Detection | AeBAD-S | MSFR Detection AUROC 87.1 | Multi-scale feature reconstruction network for... | Ehteshamciitwah/MSFR | 1 | Compare |
Papers archive 2025-07-28
8 shown of 8 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 11. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Multi-scale feature reconstruction network for industrial anomaly detection | 1 | 2 | 23 Oct 2024 | not harvested |
| Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction | 1 | 2 | 5 Apr 2023 | not harvested |
| Anomaly Detection via Reverse Distillation from One-Class Embedding | 5 | 2 | 26 Jan 2022 | ran 14 of 18 samples (4 unverified; 15 pointer-only for licence) |
| Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization | 3 | 2 | 30 Sep 2021 | ran 2 of 8 samples (6 unverified) |
| Towards Total Recall in Industrial Anomaly Detection | 18 | 2 | 15 Jun 2021 | ran 5 of 36 samples (31 unverified) |
| Inpainting Transformer for Anomaly Detection | 2 | 2 | 28 Apr 2021 | ran 4 of 4 samples (0 unverified; 4 pointer-only for licence) |
| DRAEM - A Discriminatively Trained Reconstruction Embedding for Surface Anomaly Detection | 2 | 2 | 1 Jan 2021 | not harvested |
| Reconstruction by Inpainting for Visual Anomaly Detection | 2 | 2 | 17 Oct 2020 | not harvested |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
No licence recorded in the archive. Absence here is not a statement about the dataset's terms.
Modalities archive 2025-07-28
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- AeBAD
- AeBAD-S
- AeBAD-V
3 variant names, as the archive lists them.
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