Inspection Power Line Asset Dataset
InsPLAD is a Dataset for Power Line Asset Inspection containing 10,607 high-resolution Unmanned Aerial Vehicles colour images. It contains 17 unique power line assets captured from real-world operating power lines. Some of those assets (five, to be precise) are also annotated regarding their conditions. They present the following defects: corrosion (4 of them), broken/missing cap (1 of them), and bird's nest presence (1 of them).
Three image-level computer vision tasks covered by InsPLAD:
Object detection, evaluated through the AP metric
Defect classification, evaluated through Balanced Accuracy
Anomaly detection, evaluated through the AUROC metric
Variants: InsPLAD
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Anomaly Detection | AttentDifferNet (SENet-AlexNet) | Attention Modules Improve Image-Level Anomaly … | 2023-11-05 |
| Anomaly Detection | RD++ (CBAM-ResNet-18) | Attention Modules Improve Image-Level Anomaly … | 2023-11-05 |
| Anomaly Detection | RD++ (SENet-ResNet-18) | Attention Modules Improve Image-Level Anomaly … | 2023-11-05 |
| Anomaly Detection | DifferNet | Same Same But DifferNet: Semi-Supervised … | 2020-08-28 |
Recent papers with results on this dataset: