Deep learning has become a useful tool in the integration and implementation of machine vision systems in the automation environment.

Artificial intelligence, machine learning, and deep learning are interrelated concepts involved with computer-based learning from vast amounts of data – and then making predictions based on that information. This article will show how these technologies can provide good alternatives to traditional image processing, and how software works to make this happen. 

“There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035,” predicted the techno-futurist philosopher Gray Scott. But the truth is more nuanced: automation will create as many opportunities for humans as it reduces. Here’s how manufacturers can greatly enhance their processes—and address the U.S.’s skills shortage.

A study by McKinsey & Company found that AI-driven quality testing can increase productivity by up to 50% and defect detection rates by up to 90% compared to human inspection.

Artificial intelligence (AI) is one of the most hyped technologies of recent years, and while it promises new cost and process benefits for inspection applications, deployment remains a challenge.

Machine vision quality assurance systems have excelled at automating the location, identification, and inspection of manufactured components through computational image analysis.

Trying out different behaviours is one of the classic learning methods. Success or failure decides which behaviour is adopted. This principle can be transferred to the world of robots.