Composite defect detection: Datasets & Models
Automated visual inspection of aerospace composite parts to identify surface and structural defects like delamination, voids, and fiber wrinkling from images.
Composite defect detection
This composite defect detection workflow takes images of aerospace composite parts such as laminates, panels, and cured layups and runs object detection to locate and classify defects. It identifies anomalies including delamination, porosity, voids, resin-rich areas, fiber misalignment, wrinkling, and surface cracks, returning bounding boxes for each detected flaw.
Try it out
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Why automate composite defect detection
Manual composite inspection in aerospace is slow, requires highly trained technicians, and is prone to inconsistency and fatigue over long inspection shifts. Small subsurface voids or early delamination can be missed, and defects that escape detection carry serious safety and rework consequences. Automating composite defect detection with computer vision gives you repeatable, documented inspection that flags anomalies consistently and captures a permanent visual record for traceability. It lets your specialists focus their expertise on adjudicating flagged regions rather than scanning every square inch of every part.
Key benefits
Teams gain faster inspection throughput, consistent defect classification, and audit-ready records that support aerospace quality and certification requirements. The model integrates with existing inspection cameras, borescopes, and imaging equipment on the shop floor, so you can deploy without new hardware. As production scales across more part numbers and lines, the same detection pipeline scales with it.
How to use it
- Upload an image: Drop any photo into the widget above to try composite defect detection for free, right on this page.
- Preview instant results: The model detects delamination, void, fiber wrinkle, surface crack, resin-rich region 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.
- 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.
- 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.
Recommended model for composite defect detection
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for composite defect detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Composite Defect Detection project to Roboflow Agent.