Computer Vision Quality Inspection: What Works on a Real Production Line

Most computer vision pilots succeed and most rollouts fail. The reason is rarely the model, it is the imaging chain.

The Imaging Chain

• Camera (resolution, frame rate, sensor type), the most under specified link.
• Lens (focal length, aperture), must be matched to the working distance.
• Lighting (intensity, colour, polarisation), controls > 60% of the model’s accuracy.
• Mechanical fixturing, the part must present consistently.
• Trigger, PLC or photo eye driven, not software polled.

Spend twice as long on the imaging chain as you do on the model. The model can be retrained. The imaging chain decides whether the model has any signal to learn from.

Model Architecture

• Object detection (defect location): YOLO v8 / v10 variants.
• Classification (defect type): ResNet / EfficientNet.
• Anomaly detection (unknown defects): autoencoders, PatchCore.
• Multi task heads where the same image needs both detection and classification.

Integration with MES

1. Vision system receives the trigger and the part ID from MES.
2. Inference runs at the edge, result returned in < 200 ms.
3. Reject decision is published to the line controller (PLC).
4. Reject reason and image are logged to MES quality module for genealogy.
5. Continuous evaluation runs against a held out labelled set every shift.