FLIR Systems, Inc. has announced the release of its open-source machine learningthermal dataset for advanced driver assistance systems (ADAS) and self-driving vehicle researchers, developers, and auto manufacturers.
Featuring a compilation of more than 10,000 annotated thermal images of day and nighttime scenarios, the dataset is designed to accelerate testing of thermal sensors on self-driving systems, according to FLIR. The dataset includes annotations for cars, other vehicles, people, bicycles, and dogs, and enables developers to begin testing and evolving convolutional neural networks (CNN) with the FLIR Automotive Development Kit (ADK).
FLIR’s ADK includes the company’s Boson thermal camera core in an enclosure, a mountain bracket, and a 2m USB cable. The Boston is available with either a 320 x 256 or 640 x 512 uncooled VOx microbolometer with a 12 µm pixel pitch and spectral sensitivity from 8 to 14 µm. The IP67-rated ADK provides a detection range of >100 m and a selectable field of view (24°, 34°, or 50°) for flexibility in range and awareness.
The new dataset, according to FLIR, will enable users to quickly evaluate thermal sensors on next-generation algorithms.
"This free, open-source dataset is a subset of what FLIR has to offer, and it provides a critical opportunity for the automotive community to expand the dataset to make ADAS and self-driving cars more capable in various conditions," said Frank Pennisi, President of the Industrial Business at FLIR. "Furthermore, recent high-profile autonomous-driving related accidents show a clear need for affordable, intelligent thermal sensors. With the potential for millions of autonomous-enabled vehicles, FLIR thermal sensor costs will decrease significantly, which will encourage wide-scale adoption and ultimately enable safer autonomous vehicles."
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