FLIR Systems announced the FLIR Firefly camera family, the deep learning inference-enabled machine vision camera. The FLIR Firefly, which integrates the Intel Movidius Myriad 2 Vision Processing Unit (VPU), is designed for image analysis professionals using deep learning for more accurate decisions, and faster, easier system development.
Traditional rules-based software is ideal for straightforward tasks such as barcode reading or checking a manufactured part against specifications. The FLIR Firefly combines a new, affordable machine vision platform with the power of deep learning to address complex and subjective problems such as recognizing faces or classifying the quality of a solar panel.
The FLIR Firefly leverages the Intel Movidius Myriad 2 VPU’s advanced capabilities in a compact and low-power camera, ideal for embedded and handheld systems. Machine makers can load their trained neural networks directly onto the Firefly’s integrated VPU. Additionally, Intel Movidius Neural Compute Stick users can easily deploy their existing networks directly onto the Firefly. This unique design reduces system size and improves speed, reliability, power efficiency, and security.
“Automated analysis of images captured by machines is a key part of our day-to-day lives that few of us think about,” said James Cannon, President and CEO of FLIR. “The quality, affordability, and speed-to-market of items like our smartphones or the food on our tables are made possible by systems using cameras doing both inspection and automated production. With the FLIR Firefly powered by Intel Movidius Myriad 2 VPU, we are enabling the designers of these systems to leverage deep learning faster and at lower costs.”
“The Intel Movidius Neural Compute Stick enabled FLIR to rapidly prototype, streamlining the early development of machine learning in the Firefly,” said Adam Burns, Director of Computer Vision Products at Intel. “Now the FLIR Firefly uses the compact, efficient Intel Movidius Myriad 2 VPU to perform real-time inference in the camera, without compromising the amazing levels of miniaturization that FLIR has achieved in this device.”
