Airplane Door Plug Inspection
High-resolution imagery of aircraft door plug surfaces featuring precise bounding box annotations for structural flaws and surface irregularities. Diverse lighting and material textures enable robust identification of mechanical wear, debris, and manufacturing defects.
Sample Frames
20 annotated samples drawn from the train split. Toggle annotations to inspect bounding-box quality.
Class Distribution
5 annotation classes · 62 total images. Sorted by object count, descending.
Class Balance
Per-class counts, frequency, and average bounding-box area. Sort any column to surface the rarest or most prevalent classes for re-balancing.
| Class | Images↓ with class | Objects total | Per image average | Area % of frame |
|---|---|---|---|---|
| scratch_with_baremetal | 20 | 24 | 1.20 | 0.05% |
| edge_damage | 14 | 14 | 1 | 0.05% |
| scratch_without_baremetal | 11 | 11 | 1 | 0.18% |
| FOD | 9 | 9 | 1 | 2.01% |
| tool_mark | 8 | 8 | 1 | 0.19% |
Co-occurrence Matrix
How frequently pairs of classes appear in the same image. Diagonal cells show standalone image count for that class. Useful for spotting biased or correlated labels.
| FOD | edge_damage | scratch_with_baremetal | scratch_without_baremetal | tool_mark | |
|---|---|---|---|---|---|
| FOD | 9 | ||||
| edge_damage | 14 | ||||
| scratch_with_baremetal | 20 | ||||
| scratch_without_baremetal | 11 | ||||
| tool_mark | 8 |
Average Object Area
Each rectangle is one class, sized by the average area its bounding boxes occupy as a percentage of the frame. Surfaces tiny vs. dominant objects at a glance.
Spatial Distribution
Where annotations of each class tend to fall across the frame. Brighter regions indicate higher density — useful for detecting positional bias in your training data.
Dataset Metadata
| Annotation format | YOLO |
|---|---|
| Total images | 62 |
| Classes | 5 |
| Resolution | Mixed |
| Last updated | 2026-05 |
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