Podcast: The ins and outs of machine vision — Unlocking new opportunities in manufacturing

In manufacturing, precision is paramount. Manufacturers are constantly seeking technologies that can enhance quality control, improve efficiency, and reduce human error. In this episode, New Equipment Digest editor-in-chief Laura Davis discusses the ins and outs of machine vision including what it is, its benefits, its challenges, its differences from other technologies, and more.

This information was originally written by John D. Thrailkill, marketing manager at Advanced Illumination. Bringing over a decade of experience, John initially started in engineering working on product designs which was followed by time spent in the applications lab, giving him firsthand insight into both the technical aspects and practical applications of machine vision technology.

Below is an excerpt from the podcast:

In simple terms, machine vision enables computers to see. Most authorities in the industry define machine vision as the replacement of human visual perception and judgment with a camera, computer, and software to provide autonomous, noncontact image acquisition, analysis, and decision-making to obtain desired data to enhance or control an automated process.

The industry generally assigns the basic machine vision use cases into the four following categories:

Category number one is inspection: A visual inspection includes identifying objects and features, verifying assembly, detecting defects, and counting.

Category number two is location and guidance: As in vision-guided robotics, the location of individual objects in a scene can be determined either relative to the scene or relative to a real-world coordinate system.

Category number 3 is measurement: In 2D and 3D space, online metrology provides real-world measurements of objects/features to a specified precision.

And category number 4 is identification, sorting, and reading. This category includes identifying and sorting objects and features in a scene, reading and understanding characters and code symbols, and sorting and counting objects according to geometry, color, or other characteristics.

Machine vision has a few key differentiators relative to other technologies. First, it's automated and noncontact. It always provides both automated acquisition and analysis. Other technologies, in contrast, involve only acquisition or only analysis.

Computer vision, for example, provides only analysis, while imaging involves only acquisition. Machine vision, which provides both, is virtually always a one-to-one image-to-process relationship. It's not typically a streaming technology, such as applied in autonomous driving, where the application involves streaming images and doing real-time processing.

Machine vision involves automated image acquisition and real-time processing based on a set of rules and parameters. By using digital cameras to acquire properly illuminated images and processing them in real time, machine vision systems output decisions such as pass or fail, that are based on a defect that the vision system detected.

So what's the difference between machine vision and computer vision?

Computer vision refers to the processing of images, with an emphasis on image analysis. While machine vision always involves the automated capture of images, that's not the case for computer vision. Computer vision does not necessarily involve capturing an image, but it can interpret data from saved images and produce a result or set of results.

While computer vision emphasizes the ability to fully understand images by analyzing them in their totality, machine vision systems may only process a part of the image. Machine vision is always part of a larger machine system and provides vision capabilities for existing technologies, such as those used in support of manufacturing applications in production or quality assurance.

About the Podcast
Great Question: A Manufacturing Podcast offers news and information for the people who make, store and move things and those who manage and maintain the facilities where that work gets done. Manufacturers from chemical producers to automakers to machine shops can listen for critical insights into the technologies, economic conditions and best practices that can influence how to best run facilities to reach operational excellence.