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.
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FREE Starter Dataset |
Enhanced San Francisco Dataset |
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Annotated |
~14,000 |
~10,000 |
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Weather |
Sun and Cloud |
Sun, Cloud, Rain, and Fog |
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Classes |
5 - Car, Bike, Person, Dog, and Other Vehicle |
11 - Car, Sign, Light, People, Truck, Bus, Hydrant, Bike, Rider, Motorcycle, and Train |
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FREE Starter ThermalADAS Dataset |
San Francisco Enhanced
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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 in automotive, FLIR has the only automotive-qualified thermal sensor that is deployed in over 600,000 cars today for driver warning systems.
- The FLIR thermal sensors 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.