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AI擴大機器視覺應用能力

日期:2019-08-23

Deep learning employs neural networks and algorithms that enable machines to learn without being explicitly programmed to perform a certain task. While most consumers aren’t interested in what is inside deep learning’s black box so long as it works, the capabilities illustrated by Google’s Vision AI API have clear implications for the machine vision industry, which has relied for decades on fixed rule-based approaches and pass/fail interpretations of image data.

Where rule-based programming excels at measurement and alignment, deep learning tools enable classification of image data to perform complex cosmetic inspections, distinguish different materials, verify assembly, and generally adapt to unstructured image data. This isn’t to say that deep learning will one day replace traditional machine vision but rather expand its abilities.

For all of its power and adaptability, deep learning will revolutionize machine vision, not replace it. They are complementary technologies. Machine vision’s ability to discern geometric patterns and edges in image data remains the best way to achieve subpixel accuracy for high-precision measurements. Deep learning promises to extend the discipline’s capabilities by introducing a humanlike ability to judge and learn from image data. But deep learning still benefits from a human trainer – especially one knowledgeable in traditional machine vision techniques. Veteran engineers may often find their application expertise is valuable in optimizing a deep learning’s ability to learn.