Providing “Deep Learning” to the Automotive Industry

Deep learning PC VisionAcrovision has been awarded 2 separate contracts to supply “Deep Learning” based Vision Inspection systems into the Automotive Sector.

Both customers have requirements for a multi-camera setup to inspect:

  • Correct Part Installed Confirmation – could be colour, could be for specific model versions
  • Correct Installation Sequence
  • Checking for damage / scratches etc.

A user-friendly front-end screen was also provided by Acrovision for operator interaction

Acrovision is using Cognex ViDi Deep Learning PC Vision software to carry out these tasks.

Increasingly, industry is turning to deep learning technology to solve manufacturing inspections that are too complicated, time-consuming, and costly to program using traditional machine vision. Cognex ViDi is the first deep learning-based software designed to solve these complicated applications for factory automation.

Deep Learning technology uses neural networks which mimic human intelligence to distinguish anomalies, parts, and characters while tolerating natural variations in complex patterns. Deep Learning offers an advantage over traditional machine vision approaches, which struggle to appreciate variability and deviation between very visually similar parts.

Deep Learning-based software optimized for factory automation can:

  • Solve vision applications too difficult to program with rules-based algorithms
  • Handle confusing backgrounds and poor image quality
  • Maintain applications and re-train on the factory floor
  • Adapt to new examples without re-programming core algorithms
  • Be used by non-vision experts

In factory automation, Deep Learning-based software can now perform judgment-based part location, inspection, classification, and character recognition challenges more effectively than humans or traditional machine vision solutions.

Acrovision foresees AI / Deep Learning as a significant breakthrough in the Industrial Vision world, opening up applications and tasks not previously achievable with traditional vision.

Paul Cunningham


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