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Leading Technology

Deep Learning-Based Image Analysis

Artificial intelligence for machine vision

Deep-Learning-Based-Image-Analysis2

Related Products

ViDi

VisionPro ViDi

A breakthrough in complex inspection, part location, classification, and OCR

VisionPro

I sensori di visione In-Sight® 2000 uniscono tutta la potenza di un sistema In-Sight alla semplicità e alla convenienza di un normale sensore di visione.

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.

Manufacturing advantages

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 like VisionPro ViDi can now can perform judgment-based part location, inspection, classification, and character recognition challenges more effectively than humans or traditional machine vision solutions.

Easy, fast set-up and operation

Unlike machine vision systems, which operate via step-by-step filtering and rule-based algorithms, deep learning-based image analysis software learns by example—as a human would—from a set of annotated training data and images which represent a part’s known features, anomalies, and classes. During training, the software develops neural networks that can model a part’s normal appearance and defects. During run-time, the software locates parts, extracts anomalies, classifies parts, and even deciphers hard-to-read characters with speed, robustness, and precision.

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