Deep Learning for Complex Inspections
Deep learning marries the self-learning of a human inspector with the speed and consistency of a computerized system
Whether used to locate, read, inspect, or classify features of interest, deep learning-based image analysis differs from traditional machine vision in its ability to conceptualize and generalize a part's appearance based upon its distinguishing characteristics—even when those characteristics subtly vary or sometimes deviate.
Deep learning-based image analysis is especially well-suited for cosmetic surface inspections that are complex in nature: patterns that vary in subtle but tolerable ways, and where position variants can preclude the use of methods based on spatial frequency. Deep learning excels at addressing complex surface and cosmetic defects, like scratches and dents on parts that are turned, brushed, or shiny.
Deep learning technology uses neural networks which mimic human intelligence to distinguish between cosmetic anomalies 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 like Cognex Deep Learning can now can perform judgment-based inspection challenges more effectively than humans or traditional machine vision solutions.