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Deep Learning

Deep Learning Vs. Machine Vision and Human Inspection

Choosing between Machine Vision and Deep Learning

Deep learning is both flexible and robust

For decades, machine vision systems have taught computers to perform inspections that detect defects, contaminants, functional flaws, and other irregularities in manufactured products. Human visual inspection prevails, however, in situations that require learning by example and appreciating acceptable deviations from the control. Machine vision, by contrast, offers the speed and robustness that only a computerized system can.

Machine Vision

Machine vision excels at the quantitative measurement of a structured scene because of its speed, accuracy, and repeatability. A machine vision system built around the right camera resolution and optics can easily inspect object details too small to be seen by the human eye, and inspect them with greater reliability and less error. On a production line, machine vision systems can inspect hundreds or thousands of parts per minute reliably and repeatedly, far exceeding the inspection capabilities of humans.

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. Though limited in the rate at which they can process information, humans are uniquely able to conceptualize and generalize. Humans excel at learning by example and are capable of distinguishing what really matters when it comes to slight anomalies between parts. 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.

Human Inspectors

Deep learning technology uses neural networks which mimic human intelligence to distinguish anomalies, parts, and characters while tolerating natural variations in complex patterns. In this way, deep learning combines the flexibility of human visual inspection with the speed and robustness of a computerized system.

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