Matthew Dale explores how thermal imaging can be used to avoid machine failure and emergency maintenance in industrial environments
Flir Systems has posted revenue of $434.1 million for the second quarter of 2017, up 8 per cent over the second quarter of 2016
More and more, machine vision systems are expected to make dynamic, automated decisions based on variable conditions. The amount of time and effort to develop these systems can be daunting. Today, the advent of deep learning is changing this landscape and putting automation within the reach of many. Resources such as open-source libraries, Nvidia hardware, and FLIR cameras are helping to make this change happen FLIR cameras have advanced features that minimize the image pre-processing required for neural network training, work seamlessly with platforms such as NVidia Jetson TX-2 and Drive PX 2, and offer 24/7 reliability for trouble-free deployment.
The next generation of high-performance CMOS sensors will open the door to exciting new vision applications, but taking advantage of them will depend on whether your interface can keep up. Ethernet and USB interfaces are increasing their transfer rates to 10Gb/s, adding to the strengths that make them dominant camera interfaces. What are these new technologies and are they ready for machine vision use?
FLIR Systems has announced that James J. ("Jim") Cannon has been appointed President and Chief Executive Officer, effective June 19, 2017
FLIR Systems has announced the launch of the FLIR ETS320 thermal imaging solution for electronics testing in engineering benchtop environments
Flir Systems has announced that Paul Sale will join the company as senior vice president and chief human resources officer
Flir Systems has produced its one millionth Flir Lepton microcamera core
Flir Systems will show a wide variety of thermal imaging cameras for machine vision applications
Flir Systems has posted annual revenue of $1.66 billion in 2016, up 7 per cent compared to 2015
Image credit: Fit Ztudio/Shutterstock.com
Deep learning has helped to make great strides in machine vision technology, but there are additional data-centric tools that can help new applications come to life. Find out more...
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One of SweGreen's eight vertical farm units that it operates across Sweden. Credit: SweGreen
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