Fuselage crack detection: Datasets & Models

Manual visual inspection of fuselage skin is slow, fatiguing, and inconsistent, and fine hairline cracks near rivets and lap joints are easy to miss under hangar lighting. Automating detection with segmentation gives inspectors a consistent second set of eyes that documents defect location and size in a repeatable way, reducing the chance a critical crack goes unrecorded. It also creates a searchable visual record for tracking how a flaw progresses between inspection intervals. For MRO teams under tight turnaround pressure, this shortens inspection cycles without sacrificing rigor.

Pixel-level masks let you measure crack length and area rather than settle for a simple present/absent label, which feeds directly into airworthiness assessments. The workflow runs on imagery from cameras and drones teams already deploy in the hangar, so it slots into existing inspection routines. As your fuselage crack dataset grows, model coverage across aircraft types, panel geometries, and lighting conditions improves, letting the system scale across an entire fleet.

1. Upload an image: Drop any photo into the widget above to try fuselage crack detection for free, right on this page.
2. Preview instant results: The model detects crack, corrosion, rivet, dent, scratch within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
3. Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR (instance segmentation) on your own images for production accuracy.
4. Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.