FREE FLIR Thermal Dataset for Algorithm Training

The FLIR starter thermal dataset 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.



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 500,000 cars today for driver warning systems.
  • The FLIR thermal sensors can detect and classify pedestrians, bicyclists, animals and vehicles 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.

Dataset Specifications

Content Synced annotated thermal imagery and non-annotated RGB imagery for reference. Camera centerlines approximately 2 inches apart and collimated to minimize parallax
Images >14K total images with >10K from short video segments and random image samples, plus >4K BONUS images from a 140 second video
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.
Frame Annotation Label Totals 10,228 total frames and 9,214 frames with bounding boxes.
1. Person (28,151)
2. Car (46,692)
3. Bicycle (4,457)
4. Dog (240)
5. Other Vehicle (2,228)
Video Annotation Label Totals 4,224 total frames and 4,183 frames with bounding boxes.
1. Person (21,965)
2. Car (14,013)
3. Bicycle (1,205)
4. Dog (0)
5. Other Vehicle (540)
Driving Conditions Day (60%) and night (40%) driving on Santa Barbara, CA area streets and highways during November to May with clear to overcast weather.
Capture Camera Specifications IR Tau2 640x512, 13mm f/1.0 (HFOV 45°, VFOV 37°) FLIR BlackFly (BFS-U3-51S5C-C) 1280x1024, Computar 4-8mm f/1.4-16 megapixel lens (FOV set to match Tau2)
Dataset File Format 1. Thermal - 14-bit TIFF (no AGC)
2. Thermal 8-bit JPEG (AGC applied) w/o bounding boxes embedded in images
3. Thermal 8-bit JPEG (AGC applied) with bounding boxes embedded in images for viewing purposes
4. RGB - 8-bit JPEG
5. Annotations: JSON (MSCOCO format)
Sample Results

mAP scores were obtained for People (0.794), Bicycle (0.580), and Car (0.856) categories. was used for accuracy assessment criteria.

FLIR ADK Training and Development Settings Use the FLIR ADK with default settings to begin data collection

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