AI Visual Inspection Readiness
intermediate · 3 lessons · 3 min read
The prerequisites that actually determine whether AI visual inspection works — imaging, labeled examples, and rule-based decisions.
This course is free to read below. Enrollment, progress, quizzes, and certificates open with community membership.
Imaging comes first
A model can only be as consistent as the images it is given.
Controlled lighting, a fixed camera-to-part geometry, and a fixture that presents the feature the same way every time matter more than the choice of model. If a person would struggle to judge the part from the image, the model will too. Imaging is the foundation, not a detail to fix later.
Labeled examples of the decision
Why saved observations from manual inspection are the material a model needs.
- Clearly acceptable parts.
- Clearly unacceptable parts.
- The borderline cases your own inspectors disagree on — the most valuable of all.
- For each, the reason it was judged that way, tied to the written criterion.
This is why running manual inspection as an evidence process pays off before any automation: those saved images and verdicts are exactly what a model is evaluated against. Readiness is largely a byproduct of good record-keeping.
Rules decide, the model detects
Keeping the accept/reject decision in explicit, versioned rules.
The safest architectures use the model as a detector and let explicit, versioned rules make the accept/reject call. That separation is what makes the system auditable — you can show which rule fired, on which detection, for which part — and it lets the model improve without silently changing what counts as a pass. A model that only sees is easy to check; one that both sees and decides is hard to trust.