AI visual inspection for factories, explained
Learn how camera based defect detection works on a production line, and what makes it succeed
A taste of a lesson
We only have about 30 photos of scratched parts. Is that enough to train a defect detector?
Thirty is thin for a supervised detector, especially if scratches vary a lot in size and direction. Two practical routes. First, anomaly detection: train mostly on good parts, which you probably have in thousands, and flag anything unusual; expect some false alarms on harmless variation that you then tune down. Second, keep collecting scratched parts over the coming weeks while the anomaly system runs with human review. Before either, check the lighting: a low angle light often makes scratches far more visible, which helps any model. Quick check: on your line, which is more costly, a scrapped good part or a scratched part reaching a customer?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Frame an inspection task as classification, detection, segmentation or anomaly detection
- Explain why lighting, optics and camera setup drive inspection quality
- Weigh the costs of false rejects against escaped defects when setting thresholds
- Estimate timing budgets from line speed and explain edge deployment
- Describe how to monitor for drift and keep people in the loop
Lesson plan
- 1 Framing the inspection problem Choose between classification, detection, segmentation and anomaly detection for a product. Start
- 2 Lighting, optics and cameras Understand why a stable, well lit image matters more than the model. Start
- 3 Collecting and labelling data Gather good and defective samples and label them consistently. Start
- 4 The cost of errors Set decision thresholds based on the costs of false rejects and escapes. Start
- 5 Speed and running at the edge Fit inspection into the time each part spends in front of the camera. Start
- 6 Drift, people and responsibility Keep the system reliable over time with monitoring and human review. Start
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About this tutor
For engineers, technicians, managers and students curious about how AI checks products on a production line. You will learn how to frame an inspection problem as classification, detection, segmentation or anomaly detection, and why lighting and camera setup usually matter more than the model. The lessons cover collecting rare defect samples, the different costs of rejecting good parts and passing bad ones, timing budgets set by line speed, running models on devices next to the line, and spotting drift when materials or lighting change. This is an educational course: real deployments, especially safety related ones, need validation by qualified engineers under the standards that apply to your industry.
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About the teacher
Making models fast, small and affordable: hardware, quantisation, serving and edge
9 tutors 355 lessons taught Sample
I teach the engineering side of running models: what GPUs actually do, how memory limits shape every decision, how to quantise or distil a model, and how to serve it efficiently on a server or a small device. I have spent my working life close to hardware, first on embedded systems and later on inference infrastructure, so I tend to...
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