What is computer vision?
Extracting information from images and video — and why the accuracy target matters more than the model.
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What is computer vision?
Computer vision is the branch of AI that extracts information from images and video — reading text from documents, detecting defects on a production line, counting stock, recognising number plates or monitoring safety conditions. It replaces visual checking that is repetitive and error-prone when done manually.
Accuracy is a product decision, not a technical one
Every vision system has an accuracy ceiling and a cost of being wrong. A defect detector at 94% is excellent if a human reviews flagged items and dangerous if it silently passes rejects. The right target follows from what each error type costs, not from a benchmark figure.
The design question that matters is what happens on an uncertain result. Routing low-confidence cases to a person is what makes these systems safe to deploy.
Training data must match deployment conditions
A model trained on clean studio images will fail on a warehouse floor in poor light, or on a crumpled form photographed with a mid-range phone. Representative data matters far more than volume — 300 varied examples usually beat 5,000 near-identical ones.
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