FLIR Santa Barbara Regional Thermal Dataset for Algorithm Training

The FLIR Santa Barbara regional 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 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.

Dataset Details

Annotations
Car  
Sign  
Light  
Person  
Truck  
Bus  
Hydrant  
Bike  
Rider  
Motor  
Dog  
Train  
Vehicle Other  
Total  
Weather
Clear  
Partly Cloudy  
Overcast  
Rainy  
Foggy  
Total  
Scene
City Street  
Highway  
Residential  
Parking Lot  
Tunnel  
Gas Station  
Total  
Hours
Day  
Night  
Dawn / Dusk  
Total  
Sample Results
TBD

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.
Driving Conditions Day (60%) and night (40%) driving on Santa Barbara, CA area streets and highways from 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, Computer 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 score of 0.587 (50% IoU) was obtained by fine-tuning RefineDetect512 with this dataset and testing using holdout validation set. Details further explained in readme.
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.

 

Related Products

FLIR ADK™
FLIR ADK™

Thermal Vision Automotive Development Kit (ADK)

PathFindIR™ II
PathFindIR™ II

Driver Vision Enhancement System