Automated defect detection with vision AI
What is automated defect detection?
Automated defect detection uses cameras and vision AI to find flaws in products as they move through production: cracks, dents, contamination, and assembly errors. Instead of checking a sample of each lot, a model trained on your own images inspects every unit in real time and triggers an action the moment a defect appears. Over 16,000 organizations, including half of the Fortune 100, build with Roboflow.
Surface defect detection
Catch scratches, cracks, and abrasions at line speed, including the kind that only catch the light at a specific angle, before units ship.
Dents and deformation
Flag dents, warping, and dimensional deviation on formed and machined parts as they occur, so damaged goods never reach your customers.
Missing components and assembly errors
Verify every build against the expected configuration: missing fasteners, wrong parts, and unseated connectors caught before the next station.
Contamination and foreign objects
Detect contamination and foreign objects before sealing and shipping, and stop a single missed inclusion from becoming a recall.
Runs on the cameras you already have
No proprietary hardware, no new capex, no line redesign. Roboflow runs defect detection on your existing GigE and USB industrial cameras, smart cameras, and edge devices, on-prem or air-gapped where your IT requires it. When the product or the defect changes, you retrain on new images and redeploy in days, not procurement cycles.
Act on every defect in real time
A detection is only useful if the line reacts. Roboflow Workflows sends pass/fail to your PLC to drive reject gates, marking systems, and line stops, alerts operators, and logs every detection with an audit trail your quality team can stand behind under ISO 9001, IATF 16949, and FDA 21 CFR Part 11.
Models that get smarter after deployment
Hardware inspection systems are fixed at install. Roboflow improves in production: active learning mines the images your line generates, your team labels the edge cases, and every retrain compounds accuracy. One building materials manufacturer saves $20,000 every time a defect is caught before it ships.
Train an accurate defect detection model with less data
You don't need millions of defect images to start. Manufacturers train production models from a few hundred labeled examples, with AI-assisted labeling that cuts manual work by about 95%.
AI-assisted labeling
Label defect images with model-assisted annotation and cut manual labeling work by about 95%, so your quality team's time goes to judgment calls, not clicks.
Active learning
Automatically collect the production images your model finds hardest, review them, and retrain, so accuracy compounds after deployment instead of drifting.
Edge deployment
Run inference at production line speeds on edge devices next to the camera, with cloud, on-prem, VPC, and air-gapped options from the same platform.
Audit-ready traceability
Every detection is logged with image, timestamp, and result, supporting the records your ISO 9001, IATF 16949, and FDA 21 CFR Part 11 audits expect.