Aircraft surface inspection: Datasets & Models

Manual aircraft surface inspection is slow, subjective, and dependent on the experience of individual inspectors working under time pressure between flights. Small dents, hairline cracks, or early-stage corrosion are easy to miss with the naked eye, and inconsistent documentation makes it hard to track how a defect evolves over successive checks. Automating surface defect detection with computer vision gives maintenance teams consistent, repeatable coverage of the airframe, creates a timestamped visual record for airworthiness compliance, and lets inspectors focus their expertise on assessing flagged areas rather than scanning every panel from scratch.

Teams get faster, more consistent inspection coverage with an auditable image trail that supports maintenance and regulatory documentation. The model runs on imagery from equipment aerospace operators already use, including tablets, DSLRs, and inspection drones, and scales from a single hangar to a full fleet. Detection results integrate into existing MRO workflows so findings route directly to the right technicians for verification.

1. Upload an image: Drop any photo into the widget above to try aircraft surface inspection for free, right on this page.
2. Preview instant results: The model detects dent, crack, corrosion, missing fastener, paint delamination 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 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.