From Computer Vision To Deep Learning: How AI Is Augmenting Manufacturing Posted on : Jun 16 - 2020

In the race to enable manufacturing plants to increase production in the face of an intermittent human workforce, manufacturers are looking at how to supplement their cameras with AI to give human inspectors the ability to spot defective products immediately and correct the problem.

While machine vision has been around for more than 60 years, the recent surge in the popularity of deep learning has elevated this sometimes misunderstood technology to the attention of major manufacturers globally. As CEO of a deep learning software company, I've seen how deep learning is a natural next step from machine vision, and has the potential to drive innovation for manufacturers.

How does deep learning differ from machine vision, and how can manufacturers leverage this natural evolution of camera technology to cope with real-world demands?

Machine Vision: When Simple Is Just Too Simple

In the 1960s, several groups of scientists, many of them in the Boston area, set forth to solve "the machine vision problem." The approach was simple but powerful: Scientists proposed a framework where machine vision systems were characterized by two steps.

In the first, the scientist decides which simple features — edges, curves, color patches, corners and other salient key points in images — are important for an image. In the second, they devise a classifier, usually hand-tuning several "thresholds" (for instance, how much "red" and "curvature" classify an object as a "red apple") that automatically weighs these features and decides to which object they belong. While this approach was nowhere near a complete characterization of the power of human vision, it was simple and effective enough to survive for 50 years virtually unchanged.

In this original form, it enabled a plethora of real-world applications, and became a critical part of manufacturing applications, powering quality control deployments ever since.

In a visual inspection example, a machine vision system may be deployed to search for defects in an image of a product. The first step will usually sample images of the product by computing contrast, edges, colors and other features, as they may be indicative of defects in the object. The classifier — the second step — will be hand-tuned by the quality inspector to determine if the product has enough "suspicious features" to make a final determination of damage. View More