Quality inspection.
Lighting first.

A vision system that works in the pilot cell and fails on the line has usually not encountered a harder problem — it has encountered a different one. Ambient light changing through the day. A part presented at a slightly different angle. A camera slowly drifting out of focus from vibration. Each one shifts the input distribution away from what the model was trained on, and the model degrades without announcing it.

Controlled illumination, consistent fixturing and a fixed camera position remove most of that variance for a fraction of what is spent on model development. Most experienced practitioners in this field will tell you the same thing, and it remains the most commonly skipped step because it is a mechanical engineering job in the middle of a software project.

The second reality is class imbalance. A well-run line produces very few defects, which is the point — and it means the interesting class is rare. Anomaly detection trained on normal parts is frequently more practical than classification trained on defect types, and augmenting a handful of real defect images does not substitute for having seen the failure mode.

Overall accuracy is close to meaningless on an imbalanced line — a model that passes everything scores well and catches nothing. What matters is recall on each defect class separately, false reject rate at the chosen operating point, and the cost-weighted combination of the two using your actual scrap and complaint costs.

Rare defect classes need reporting individually and honestly, including the confidence interval. A class with eleven training examples has a recall estimate with an interval so wide it barely constrains anything, and presenting it as a point number is the kind of overstatement that ends badly at the first escape.