Defect definition comes before model choice
If defect categories are vague or inconsistent, the model will not fix the underlying ambiguity. Publicly credible inspection projects start with clear defect language and review rules.
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What Is Most Often Misjudged in AI Visual Inspection Projects
AI inspection projects often sound like model-selection problems. In practice, the harder part is usually defect definition, image consistency, review logic, and how the inspection result is tied back into the process.
Image conditions shape result quality
Lighting, reflections, focus, part presentation, and color variation all influence the usable quality of the inspection pipeline. That boundary belongs to the project, not only to the algorithm.
The inspection result must fit the production loop
AI inspection becomes useful when the result is linked to rejection logic, review logic, process adjustment, and traceability. Otherwise it remains a demonstration rather than a production asset.