When classical vision fails — and AI earns its place

Classical first — on purpose

Edges, thresholds, geometry, pattern match and code tools still solve the majority of industrial stations. They are explainable, fast to tune and cheap to maintain when the product is stable. If you can describe the defect in a sentence a process engineer understands, start classical.

Where classical breaks

Cosmetic grades on textured surfaces. Deformable packs. Print that drifts with ink and substrate. “Unknown unknown” rejects operators cannot encode as rules. Ambient light and product variation that look like defects to a threshold but not to a human.

When AI earns its place

You can collect representative OK, NOK and borderline samples. You accept labelling as a process, not a one-off. You will monitor drift after go-live. Without ownership, a model becomes a black box that production learns to bypass.

Hikrobot’s VisionMaster platform includes deep-learning modules for classification, object detection, character recognition and segmentation — with a graphical annotation path from collection to training inside the same environment as classical operators. That hybrid layout matches how we deploy: classical locate first, AI only on the hard class.

Hybrid is normal

Locate with geometry, classify with a model. Read the code classically, use AI OCR only on damaged print. Keep the PLC handshake boring either way.

Mistakes we see

Training on ten images. Shipping without a fallback. Using AI to paper over bad lighting. Measuring success only in the lab.

See also AI inspection and quality inspection.