AI and deep learning: when traditional vision falls short
Traditional machine vision works very well for predictable defects. But what if the defect is subtle, varies in shape or is hard to describe? That is where AI comes in.
Some inspection tasks push classic machine vision to its limits. Think of surface defects on cast metal parts, irregular inclusions in plastics, or colour deviations that are subtle but noticeable to the end user.
The problem with rule-based systems
Classic vision software works with explicit rules: thresholds, edge detection, pattern matching. That works well as long as the defect is consistent in shape and size. In practice, that is rarely the case.
A crack in an injection-moulded part can be 0.1 mm or 5 mm long. It can run horizontally or diagonally. A rule-based system needs a separate rule for every variant, and defects still slip through.
How deep learning does it differently
A deep learning model does not learn from rules but from examples. You show it thousands of images of good and rejected products, and the model works out the difference itself.
The result: the model generalises. It recognises a new type of crack it has never seen before, because it has learned the underlying characteristics.
In practice
We implement deep learning vision systems that:
- Train on your own product images
- Keep improving as more data becomes available
- Report transparently: every rejected product gets a confidence score and an image marking
- Integrate with your existing line PLC or MES
The threshold to get started is lower than you might expect: a few hundred labelled images are often enough for a first model.