Monitoring Machine Output Using AI Cameras

Define the counting boundary

Choose whether to count moulded parts, packed units, trays or cartons. A machine cycle may produce several parts, and a tray may be only partly filled. Document rejected items, rework and reverse movement. Place the observation point where the intended unit can be distinguished without confusing internal movement with completed output.

Match the capture to line conditions

Survey product sizes, line speed, spacing, glare and available mounting positions. Use representative footage from normal production rather than a staged still image. Check whether guards, operators or tools obstruct the view. If the product is hidden at the chosen point, change the viewpoint or sensing method before training a model.

Turn detections into production records

Track a crossing or completion event instead of adding every frame-level detection. Attach asset, product, timestamp and shift information. Store raw cumulative counts separately from calculated rates so resets and corrections are auditable. Mark camera downtime as missing coverage rather than zero production.

Validate before using the figures

Compare counts with manual samples or a trusted counter over complete runs. Test fast and slow production, changeovers, rework and stoppages. Report absolute count error and missed coverage, not only an attractive overall accuracy figure. Agree how disputed counts will be reviewed before linking the data to planning or incentives.

FAQ