MAY-JUN 2019

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26 INTECH MAY/JUNE 2019 WWW.ISA.ORG A relook at machine vision system design A lthough machine vision may seem like a new concept, we can trace its origins to the 1960s. Back then, machine vision existed as raw image files. A paradigm shift hap- pened with the advent of digital photography. Slowly, companies began developing a software ecosystem that could import these digital photo- graphs and process them in multiple ways. Alongside superior cameras and greater image processing capability came automation that en- abled machines to independently capture images without human intervention. Today, advanced artificial intelligence (AI) and deep learning tools have taken this one step further by augmenting machines with the intelligence to make right deci- sions based on the images captured. As machine hardware on the shop floor goes through transformational upgrades, manufactur- ers want the ancillary things around the hardware to keep pace. Hence, machine vision systems that once took several minutes to compute images are now expected to do the same computation in seconds (real-time computing). A prime example of this is bottle inspections in a soda manufac- turing facility. Soda companies use an advanced four-camera system that can inspect a whopping 72,000 bottles per hour. Why does real-time machine vision need more flexibility? Engineering real-time machine vision is an ex- tremely expensive process. Several companies have accomplished it by either building dedicated field-programmable gate array (FPGA) systems or installing custom camera systems that have prefed, application-specific integrated circuits (ASICs). The limiting factor here is the need for a dedicated design architecture for every single application. This is expensive and not scalable. Going back to the soda bottle inspection system, By Gineesh Sukumaran Can reconfigurable computing be the answer to our woes?

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