Flir Systems has completed its strategic investment of Cvedia, developers of machine learning applications that are used to efficiently enable sensor systems with artificial intelligence.
The company says Cvedia’s SynCity simulator software tool provides realistic, multi-modal, digital environments for autonomous system OEMs and related sensor makers to train their systems in a faster, safer, and more affordable manner than by using traditional data collection techniques.
Cvedia sought to develop SynCity to feature “real-world physics,” simulate a multitude of lighting and environmental conditions, and render objects such as people, animals, and automobiles in a manner that artificial intelligence systems interpret them as real and lifelike.
Flir said this produces datasets that are fed into customer neural network frameworks, materially shortening the time and easing the process of training these deep learning systems.
The strategic investment by Flir in Cvedia, according to the company, will create opportunities for the companies to accelerate the development of thermal spectrum-based deep learning training tools for use by Flir and selected partners in integrating artificial intelligence into Flir sensors and systems.
Flir’s advanced thermal imaging sensors are an ideal technology for detecting living beings, seeing at night and through adverse environmental conditions, and in identifying industrial process abnormalities, making them a key capability in automotive, military, and industrial applications.
The investment will also provide Cvedia with growth capital to enable the expansion of their business.
“This investment in Cvedia will enhance our ability to innovate sensing solutions that enable our customers to more quickly and accurately make their mission-critical decisions,” said Flir president and CEO James Cannon in a statement. “The addition of software algorithms that automatically inform a user or system of critical information is a valuable feature that augments the distinctive and rich data our sensors produce. We see wide applicability of these tools in our innovation of highly advanced solutions, and we look forward to the collaboration with the Cvedia team.”