Deep learning excels at addressing complex surface and cosmetic defects, like scratches and dents on parts that are turned, brushed, or shiny. 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. For many of these applications, machines cannot compete with humans for their appreciation of complexity.ĭeep 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. For example, humans are much more accurate when dealing with deformed and otherwise hard-to-read characters, complex surfaces, and cosmetic defects. This makes human vision the best choice, in many cases, for the qualitative interpretation of a complex, unstructured scene-especially those with subtle defects and unpredictable flaws. We excel at learning by example and are capable of distinguishing what really matters when itĬomes to slight anomalies between parts. Though limited in the rate at which we can process information, humans are uniquely able to conceptualize and generalize. Unlike traditional machine vision, humans are adept at distinguishing between subtle cosmetic and functional flaws, as well appreciating variations in part appearance that may affect perceived quality. Most problematically, these defects are difficult for a traditional machine vision system to distinguish between. ‘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. Inherent differences or anomalies may or may not be cause for rejection, depending on how the Machine vision systems struggle to appreciate variability and deviation between very visually similar parts. Textures and image quality issues introduce serious inspection challenges. 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.Īlthough machine vision systems tolerate some variability in a part’s appearance due to scale, rotation, and pose distortion, complex surface But algorithms become unwieldy as exceptions and defect libraries grow. They operate via step-by-step filtering and rule-based algorithms that are more cost-effective than human inspection. Traditional machine vision systems perform reliably with consistent, well-manufactured parts.
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