From the phones in our pockets to the reality of self-driving cars, the consumer economy has started to tap into the power of deep learning’s neural networks. This technology is now migrating into advanced manufacturing practices for quality inspection and other judgement-based uses. Deep learning-based image analysis combines the specificity and flexibility of human visual inspection with the reliability, consistency, and speed of a computerized system. Deep learning-based software can now perform judgement-based part location, inspection, classification, and character recognition challenges more effectively than humans or traditional machine vision automation. Increasingly, leading manufacturers are turning to deep learning solutions and artificial intelligence to solve their most sophisticated automation challenges.
Cognex deep learning technology, designed specifically for factory automation, makes it possible to automate previously unprogrammable applications, reducing error rates and quickening inspection times. Cognex has published a Deep Learning for Factory Automation Guide allowing readers to learn more about how to deploy deep learning in their operations.
Traditional machine vision systems perform reliably with consistent, well-manufactured parts. They operate via step-by-step filtering and rule-based algorithms that are more cost-effective than human inspection. But algorithms become unwieldy as exceptions and defect libraries grow. Certain traditional machine vision inspections, such as final assembly verification, are notoriously difficult to program due to multiple variables that can be hard for a machine to isolate such as lighting, changes in color, curvature, and field of view. Although machine vision systems tolerate some variability in a part’s appearance due to scale, rotation, and pose distortion, complex surface textures and image quality issues introduce serious inspection challenges. Machine vision systems struggle to appreciate variability and deviation between very visually similar parts. Inherent differences or anomalies may or may not be cause for rejection, depending on how the user understands and classifies them. “Functional” anomalies, which affect a part’s utility, are almost always cause for rejection, while cosmetic anomalies may not be, depending upon the manufacturer’s needs and preference. Most problematically, these defects are difficult for a traditional machine vision system to distinguish between.
Deep learning models can help machines overcome their inherent limitations by marrying the self-learning of a human inspector with the speed and consistency of a computerized system. 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. 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.