Computer vision for quality inspection: where to start

How it works

A camera captures each part or product as it passes a fixed point. A trained model checks the image for defects, such as scratches, missing components, wrong labels or misaligned prints, and signals a reject, an alert or a count. Results are logged so you can see defect trends by shift, machine or supplier.

What it needs to succeed

• Consistent lighting and camera position. Most failed projects are lighting problems, not AI problems.
• Clear defect definitions that your quality team agrees on.
• Example images of good parts and of each defect type, labelled by people who know the product.
• A clear action for each result: reject, alert or record.

Run a fair proof stage

Start with one station and one or two defect types. Capture images from real production, label them, and test the model on images it has never seen. Compare its results with your current inspection on the same parts.

Agree the success measures before the test starts: defects caught, false rejects and speed at line rate. That keeps the decision honest for everyone.

Beyond the factory

The same approach works on retail shelves (gaps, pricing and planogram checks), on construction sites (protective equipment and restricted zones) and in warehouses (damage and count checks). In each case the hard work is defining what “good” looks like and capturing the right images.