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A $600 camera and edge inference replace a quality inspector who gets tired.
Manual visual inspection is the last analogue bottleneck in otherwise automated production. An inspector working a 4-hour shift catches roughly 85% of defects in the first hour — and 60% in the fourth. The defects that escape cost 10–50x more to address after shipping than on the floor.
Every unit that leaves the line has been seen by a system that never blinks, never loses focus, and logs every anomaly with a timestamp and image. Your quality manager reviews exceptions rather than standing at a conveyor. Returns and warranty claims drop. Your production data becomes an asset — a dataset that improves over time rather than walking out the door with the inspector at the end of the shift.
A compact camera module mounted at the inspection point feeds a lightweight convolutional neural network running entirely on a Jetson Nano or equivalent edge compute board — no cloud dependency, no latency, no per-inference cost.
The model is trained on a dataset of known-good and defective units from your specific production run. Initial training requires 200–500 labelled images; the system improves with production data over the first weeks. Anomalies are flagged in real time with a confidence score, routed to a simple dashboard, and optionally fed back to upstream process controls.
The full system runs on 10W of power, mounts in a standard enclosure, and integrates with existing conveyor or station layouts without line modification.