Rule-Based Algorithms versus Deep Learning: How to Choose

The decision is usually framed as a matter of ambition. In practice it is settled by one question: what varies between a part that should pass and a part that should fail, and can that variation be written down as a rule? If the answer is a number — a width, a diameter, a position, a count, a grey-level difference between two defined regions — then classical image processing will express it directly, and it will do so in a way a process engineer can read and check. If the answer is a description of appearance that has no measurable form, a model is the only thing that will do the job.

Four questions that decide it

How the two fail, and why it matters more than accuracy

The two approaches fail differently, and the difference is more consequential than any comparison of accuracy figures. A rule fails loudly. When the lighting drifts or the part moves, the measured value jumps outside its tolerance and the station either rejects everything or obviously misbehaves, and an engineer can open the rule, read it, and see which threshold stopped making sense. The failure announces itself.

A model fails quietly. A network trained on samples from last year will continue to produce confident verdicts when the surface finish changes, the supplier changes the coating, or the camera is replaced. Because the output is a probability rather than a measurement, there is no threshold to inspect and no obviously wrong number to point at. The verdicts simply become slightly worse, in a way that only shows up in the escape rate or, more often, in the customer complaint. This is why a model deployment needs a measurement plan from the first day: a held-out sample set, a periodic re-measurement, and a defined level of degradation at which retraining is triggered. The same discipline as model drift monitoring.