Real-time vision inspection system for a production line

  • Services:
  • Industries: Industrial manufacturing
  • Engagement: Build
  • Timeline: Q1-Q2 2024

About the project

A European enterprise industrial manufacturer was catching surface defects through manual visual inspection at the end of the line. Inspectors missed about one in four escapes under shift fatigue, and rework costs were climbing. We built a real-time computer vision case study system: cameras mounted at two inspection stations, a Python inference service running on edge hardware, and a dashboard that flagged rejects before they reached packaging. Escape rate dropped by about 60% within the first full quarter of operation.

Background

The production line ran at high speed with tight dimensional tolerances. Manual inspection was the only gate before parts entered the packaging stream, and it depended on individual operator attention across a twelve-hour shift. The manufacturer had tried a rules-based machine vision tool a few years earlier and abandoned it because it could not handle the variation in surface finish across product batches. A modern deep learning approach had not been evaluated.

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We design and ship production-ready vision systems for industrial manufacturers.

What we built

A production-line computer vision inspection system that classifies parts in real time and feeds reject signals to the existing PLC.

Edge inference service Icon

Edge inference service

A Python service runs a distilled CNN checkpoint on a compact GPU module mounted at the inspection station. Each frame is scored within 80ms, well inside the 200ms PLC signal budget.

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Batch-aware model training

We trained on annotated production images grouped by surface finish variant. Batch-stratified sampling kept accuracy consistent across finish types, which was the failure mode of the previous rules-based system.

PLC integration layer Icon

PLC integration layer

A lightweight integration module translates inference output into a digital reject signal compatible with the existing conveyor controller. No line shutdown was needed for installation.

Quality dashboard Icon

Quality dashboard

A web dashboard streams live defect-rate and throughput metrics to the quality team. Operators can review flagged frames with bounding overlays to confirm or dismiss rejects and contribute corrections back to the training set.

Takeaway

This computer vision case study shows that a deep learning approach solves the surface-variation problem that rules-based machine vision cannot. The key design decision was training on the client's own production images grouped by finish variant, which gave the model the diversity it needed to generalize across batches. Mounting inference at the edge kept latency inside the PLC budget without a cloud dependency. We bring the same pattern to any manufacturer that needs a reliable, maintainable inspection system built around its actual production conditions.

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