Computer vision graduated from demos to deployable systems. On production floors the successful projects share one trait: they target a measurable operational metric, not a futuristic wishlist.
The three proven use cases
Defect detection catches surface flaws, assembly errors and packaging problems at line speed. Safety compliance verifies protective equipment and zone violations in real time. Inventory and throughput counting replaces clipboard rounds with continuous telemetry.
What determines success
Lighting and camera placement dominate model choice. A well-lit fixed camera with a modest model beats a deep network fed with blurry motion. Edge inference keeps latency predictable and keeps proprietary footage inside the factory.
Start with the boring camera angle that sees everything, not the clever one that sees some things beautifully.
Rollout pattern that works
- Shadow mode first: model runs silently, humans score it.
- Threshold tuning with the QA team, not the data science team.
- Human-in-the-loop review for borderline confidence bands.
- Weekly retraining on disagreement cases.
Deployed this way, vision systems earn trust on the floor and typically pay for themselves through scrap reduction within the first quarter.
