FLIR European Regional Thermal Dataset for Algorithm Training
The FLIR Enhanced European Thermal Dataset is available for sale to automotive developers. It enables 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.
The dataset was acquired via a thermal camera mounted on a vehicle. It contains a total of 14,353 annotated thermal images with 3,554 images sampled from short videos and 10,799 images from a continuous 360-second video. Videos were taken at a variety of locations, light conditions, and weather conditions (see "extra_info" in the images section of the annotations json).
The videos were captured at the following locations London (England); Paris (France); Madrid, Toledo, Granada, Malaga (Spain)
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 be used to 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.
- 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.
Dataset Details & Specifications
|
Content |
Synced annotated thermal imagery and annotated RGB imagery for reference. Camera centerlines approximately 2 inches apart and collimated to minimize parallax. |
|
Images |
Frames were sampled from 191 different videos: |
|
Frame Annotation Labels |
Bike: 542 |
|
Weather |
Clear |
|
Scene |
City Street: 2,050 |
|
Time of Day |
Day: 57% |
|
Sample Results |
Accuracy (mAP) |
|
Image Capture Refresh Rate |
Recorded at 30Hz. Dataset sequences sampled at 2 frames/sec or 1 frame/ second. Video annotations were performed at 30 frames/sec recording. |
|
Location |
The videos were captured in London ( England ); Paris ( France ); Madrid, Toledo, Granada, Malaga ( Spain ) |
|
Capture Camera Specifications |
IR Tau2 640x512, 13mm f/1.0 (HFOV 45°, VFOV 37°) FLIR BlackFly (BFS-U3-51S5C-C) 1280x1024, 4-8mm f/1.4-16 megapixel lens (FOV set to match Tau2) |
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Dataset File Format |
1. Thermal - 14-bit TIFF (no AGC) |
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FLIR ADK Training and Development Settings |
Use the FLIR ADK with default settings to begin data collection |
Have questions or want a larger dataset?
Please contact the FLIR ADAS team at ADAS-Support@flir.com for assistance.
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FLIR Boson Software IDD
FLIR ADK Quickstart Guide
FLIR ADK Datasheet