Machine vision technology has become ubiquitous and is experiencing a surge in adoption. A recent survey commissioned by a vision systems provider revealed that over the next two years, the use of machine vision is expected to grow by 37%, with survey respondents reporting expected growth from the current 46% to an impressive 63% of potential applications.
Across the board, decision-makers are looking at transformative technologies that go beyond traditional solutions to improve visibility across operations. According to Gartner research, “by 2027, 50% of companies with warehouse operations will leverage AI-enabled vision systems to replace traditional scanning-based cycle-counting processes.” Gartner further suggested that there will not be a singular vendor or solution that fits all possible use cases.
That timing is just around the corner and underscores the competitive pressure for industry players to either design their own or seek out solutions they can integrate. None of this was a surprising when Machine Design asked several sources to explain the basic differences between machine vision and computer vision. It’s a deceptively simple question because the scope of current technologies provides a wide lens and, as anticipated, elicited nuanced responses.
There were a few overlapping truths, of course. Each article in our vision systems package builds further insight into the range of approaches the industry is exposed to. Applications demonstrate various levels of maturity presented by veteran players, innovators and novices who are developing value-added workarounds and solutions.
A full complement of vision-related articles and video interviews have been posted online. And by the time you read this issue, Machine Design’s editors will have curated another package of articles focusing on sensors technology. You may access the coverage on machinedesign.com starting Oct. 7.
Design is the first step toward manufacturing. Designers of machinery use their talents to solve user-centric problems holistically. The most successful are those who have the talents and qualities to perform in today’s increasingly multi-disciplinary environment.
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