Friday, April 19, 2024

Thermal Imaging for FLIR Boson Guided by AI

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In a recent announcement by Movidius, a strategic collaboration with FLIR systems is to take place. FLIR’s Latest thermal imaging camera core will now come with the Movidius Myraid 2 Vision Processing Unit. This step would make the FLIR Boson, an intelligent thermal imaging solution.

thermal imaging camera
image courtesy: FLIR systems

This first time step by FLIR, to combine its Long Wave Infrared (LWIR) technology, and Modivius’ VPU into a single thermal imaging core. This enables the Boson for advanced image processing at low power dissipation, and introduce AI to thermal Imaging. The features in the new Boson after integration with Myraid 2, would implement Advanced image processing, super resolution, noise filtering and bending algorithms, while still significant resources on chip for analytical algorithms.

Myraid 2 is a dedicated vision processor capable of advanced computer vision operations in the field of deep learning, spatial computing and depth extraction. Due to its small size, FLIR is able to embed this vision processor into the thermal imaging core while still meeting the size, weight and power goals.

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“The state-of-the-art in computer vision has come a tremendous way in the last few years,” according to Movidius CEO, Remi El-Ouazzane. “FLIR has developed the first-ever integrated solution that now allows customers to take advantage of these new advancements while continuing to leverage FLIR’s unique thermal imaging technology and support ecosystem. FLIR will be instrumental in deploying advanced computer vision in applications such as home security, personal vision systems, drones, law enforcement and defense.”

“We’re pleased to work with Movidius to deliver this amount of compute power into the size, weight and power requirements of the product you see today,” said Andy Teich, President and CEO of FLIR Systems. “With system-on-a-chip interfaces and machine intelligence capability available through Movidius, we enable FLIR cores to make greater sense of the rich information being captured by our sensors.”


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