North American machine vision sales up 10% in first quarter 2014

Sales of machine vision components and systems in North America grew 10% in the first quarter of 2014, according to new statistics released by the AIA.

Total sales include the sales of machine vision components and vision systems, which include application-specific systems and smart cameras. Sales of machine vision components were the major driver of growth in overall sales for the quarter, having grown at 29%. Total sales of vision systems grew 6% in the quarter. This trend is the opposite of the numbers reported by the AIA for 2013 when machine vision systems were the main driver of growth.

Each component and system category saw in increase in sales in the first quarter, with cameras (36% growth), lighting (32% growth), and software (25% growth), leading the way. Jeff Burnstein, AIA President, noted that it was "particularly promising to see machine vision components post such a strong quarter," and that he hopes for the trend to continue.

Reports like this one are an indicator that the vision industry continues to grow, and is likely to continue to do so in the coming years. Here are some market forecasts that project strong growth going forward:

• High-speed camera market predicted to hit $274.46 million by 2020
• Machine vision sales in North America double expectations in 2013
• Infrared detector market predicted to hit $704.8 million by 2020
• VDMA predicts strong growth for German, worldwide photonics industry

"Our most recent survey of industry experts shows that AIA membership is indeed optimistic about machine vision components," said Alex Shikany, AIA’s Director of Market Analysis in an AIA press release. "Respondents expect every component category, except for imaging boards, to grow over the next six months."

See other articles from our June 9 e-newsletter.

Also check out:
(Slideshow) Five unique examples of industrial machine vision inspection
AUTOMATICA 2014: Innovations in automation and robotics
Lenses, lights, camera and action: Applying cinematography lessons to machine vision systems

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