FLIR Thermal Datasets for Algorithm Training

The FLIR starter thermal datasets enable developers to start training convolutional neural networks (CNN), empowering the automotive community to create the next generation of safer and more efficient ADAS and driverless vehicle systems using cost-effective thermal cameras from FLIR.

NEW Regional Data Set Options

FLIR now offers two thermal datasets to enable researchers and developers to accelerate localized testing of thermal sensors on self-driving systems.  Additional cities will be added.

 FREE Starter DatasetEnhanced San Francisco Dataset
Annotated Images ~14,000 ~10,000
Weather Sun and Cloud Sun, Cloud, Rain, and Fog
Classes 5 - Car, Bike, Person, Dog, and Other Vehicle 11 - Car, Sign, Light, People, Truck, Bus, Hydrant, Bike, Rider, Motorcycle, and Train

FREE Starter Thermal

ADAS Dataset

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San Francisco Enhanced Thermal ADAS Dataset

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Why Use FLIR Thermal Sensing for ADAS?

The ability to sense thermal infrared radiation, or heat, within the ADAS context provides both complementary and distinct advantages to existing sensor technologies such as visible cameras, Lidar and radar systems:

  • With over 15 years of experience working with Veoneer to make the only automotive-qualified thermal camera, FLIR’s thermal sensors are deployed in over 600,000 cars today for driver warning systems.
  • The FLIR thermal cameras can detect and classify objects in challenging conditions including total darkness, fog, smoke, inclement weather and glare, providing a supplemental dataset beyond LiDAR, radar and visible cameras. The detection range is four times farther than typical headlights.
  • When combined with visible light data and distance scanning data from LiDAR and radar, thermal data paired with machine learning creates a more comprehensive detection and classification system.