Machine vision users adopting cloud computing

Machine vision systems produce enormous amounts of image data in their daily operations. As systems increase in resolution, complexity, and speed, the volume of image data continues to increase. Many companies today are turning to cloud computing to handle these data flows.

The storage of image data is an appealing aspect of the cloud, but recently internet service providers have improved upload speeds, making cloud computing an even more practical decision. While leveraging the cloud has many benefits for machine vision applications, end users are integrating it to varying degrees.

Edge and cloud computing in machine vision

Some machine vision users who are wary of storing data off-site in the cloud have been relying on edge computing. This is where data is processed at the edge of the network where it’s being generated instead of a centralized environment.

Edge computing’s primary advantage is it facilitates real-time data processing, allowing users to respond as data is being generated. However, there are several drawbacks when it comes to using edge computing. The biggest is the volume of data in today’s machine vision applications are too large to be handled entirely through edge computing. Additionally, the cloud can perform far more advanced computing functions that edge computing devices can.

Cloud adoption on the rise among machine vision users

Once machine vision users see the full potential of the cloud, they’re far more likely to invest in cloud computing. The earliest adopters of cloud computing are those using 3-D vision for analysis, which generates large amounts of data. It is likely end users deploying deep learning and artificial intelligence in logistics, material science calculations, and preventive or predictive maintenance will be the next major adopters of cloud computing.

Each of these applications requires computing large amounts of data. However, there are other benefits of the cloud. The cloud’s vast storage capacity allows machine vision users to retroactively test historical images to verify process quality and trace back product faults.

Machine vision users are turning to the cloud to handle large amounts of data and to leverage advanced computing capabilities. As more machine vision users see the limitations of edge computing, the cloud will see a continued rise in adoption.

This article originally appeared in Vision Online. AIA is a part of the Association for Advancing Automation (A3), a CFE Media content partner. Edited by Chris Vavra, production editor, CFE Media, cvavra@cfemedia.com.

Original content can be found at www.visiononline.org.

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