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. The Addition of C o l o r greatly increases the amount of data available which allows for the Deep learning of the Cognex VIDI to perform more sophisticated decision making. 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. Adapt to new examples without re-programming core algorithms.Maintain applications and re-train on the factory floor.
Handle confusing backgrounds and poor image quality.Solve vision applications too difficult to program with rules-based algorithms.Deep learning offers an advantage over traditional machine vision approaches, which struggle to appreciate variability and deviation between very visually similar parts.ĭeep learning-based software optimized for factory automation can: Cognex ViDi is the first deep learning-based software designed to solve these complicated applications for factory automation.ĭeep learning technology uses neural networks which mimic human intelligence to distinguish anomalies, parts, and characters while tolerating natural variations in complex patterns. 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. Artificial Intelligence for Machine Vision
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