Choosing rule-based vision or AI inspection

Rule-based vision is usually the starting point

Edge detection, thresholds, geometry, pattern matching and code readers solve many industrial inspections. These methods are explainable and maintainable when the product and environment are sufficiently stable. Start by defining the feature and permitted variation.

Where fixed rules reach their boundary

Inspection becomes more difficult when acceptable products vary widely in texture, shape or print and that variation cannot practically be described with rules. Collect acceptable products, known defects and borderline cases from actual production.

When AI is a practical choice

AI requires a representative, labelled image set. Define who reviews new images, who retrains a model and how a new version is released. Also determine how changes in material, supplier, lighting or camera are detected.

The VisionMaster platform includes modules for classification, object detection, character recognition and segmentation. Test the required module on actual parts and at the required cycle time; the presence of a function does not establish production performance.

A combination may be suitable

Use fixed geometry to locate an inspection region and apply AI only to the feature that cannot be handled adequately with rules. Keep PLC communication, exception handling and image retention explicit regardless of the selected method.

Practical comparison

Use the same image set and the same definitions of acceptable, defective and borderline for both approaches. Measure false rejects, missed defects, processing time and sensitivity to product and environmental variation. Define a fallback procedure and version control before release.

See also AI inspection and quality inspection.