When a manufacturer decides to automate visual inspection, two offers usually come up: a "smart camera" ready to connect within hours, and an AI project built on a model trained on your own data. Both can work, but they solve a different problem and carry a different maintenance cost over time.
What separates a smart camera from an AI system
A smart camera typically relies on classic machine-vision algorithms — thresholding, edge detection, template matching. It works well for simple, repeatable defects on products with low variability: a missing part, a wrong position, an obvious geometry error under constant lighting. Setup is fast, but any change in product or lighting conditions requires recalibration by an operator or integrator.
An AI system trained on real OK/NOK images handles cases better where the defect is hard to describe with a rule — irregular flash, subtle surface damage, high natural product variability. The entry cost is higher, since it needs representative data and a validation process, but once trained and validated, the model generalizes better to cases that could not be captured by a fixed rule in advance.
Questions worth asking before you choose
- Can the defect be described unambiguously with a geometric rule, or is it more "I know it when I see it"?
- How much natural variability exists between batches and raw-material suppliers?
- Are lighting and product positioning stable, or do they drift over time?
- Do you need a single OK/NOK decision, or classification across several defect types?
- How many NOK examples do you already have — enough for a representative training and test set?
A hybrid approach
In practice, the best results come from combining both approaches: simple, unambiguous geometric checks stay rule-based, while harder cases — irregular surface defects, unusual assembly — go to an AI model. That is why, before recommending a specific approach, AIVQC starts by qualifying the process and the data rather than selling one type of hardware.
If you are not sure which category your case falls into, a good first step is a feasibility pilot on real data from your line — see the pilot section for details.