Machine vision has come a long way since it was first introduced in the early 1970s to help factories and growers sort food. Sure, it’s a simple task now but at the time, the concept was revolutionary. It’s now over 50 years later and machine vision is being used in ways that most could never imagine. Here are some of the latest machine vision advancements.
How Machine Vision Got to Where It Is Today
By adding a camera and image processing, nearly any automated system can be given the gift of machine vision. With a newfound ability to see, the system can make decisions based on what it observes in its environment. Throughout the last century, engineers have used machine vision to come up with creative solutions to longtime challenges.
From quality inspection to sorting products to guiding robots, machine vision has helped automation technologies save human workers from dangerous, dull, and dirty jobs. Machine vision has even provided threat detection for surveillance systems, guided autonomous vehicles, and inspected much of the world’s infrastructure.
But the newest advances in machine vision are the best yet. Combining machine vision with powerful technologies like machine learning and artificial intelligence results in a system that doesn’t simply make preprogrammed decisions. These new machine vision systems can mimic human intelligence and make smart choices based on huge datasets.
Recent Advancements in Machine Vision
3D Imaging. The scanning laser profilometer (3D profiler) uses laser line triangulation to acquire and create a high-precision profile of the surface of a part, often with the sensor or part in motion.
Camera Improvements. Demand for higher-resolution imaging and increased process throughput have pushed higher frame rates for large-data images further and high-speed interfacing between the camera and processor.
Embedded Systems. New smart cameras have GPU-based systems with additional FPGA support to perform edge AI. Self-contained, server-level computing systems execute both training and inference for deep learning within the smart camera format.
Retail. Stores are seeing an increase in the prevalence of computer vision technology during 2022. Cashier-less retail outlets are equipped with cameras that recognize which items customers are taking from the shelves allowing them to skip the checkout line.
Edge computing. With AI and machine learning making use of huge datasets, it’s become more important than ever to process the data as close to the source as possible. A blend of decision-making in the cloud and on the device allows these systems to “think” immediately.
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