FLIR San Francisco Regional Thermal Dataset for Algorithm Training

The FLIR Enhanced San Francisco 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.

                     

 

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 & 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 ~10K total images with ~10K from short video segments and random image samples, plus ~6K BONUS images from video
Frame Annotation Labels Car: 96,686
Sign: 31,711
Light: 30,568
Person: 15,987
Truck: 1,992
Bus: 1,579
Hydrant: 994
Bike: 804
Rider: 791
Motor: 410
Dog: 0 
Train: 360
Vehicle Other: 360
Total: 181,882
Weather Clear: 7,526
Partly Cloudy: 954
Overcast: 745
Rainy: 402
Foggy: 6
Total: 9,633
Scene City Street: 5,578
Highway: 3,215
Residential: 717
Parking Lot: 112
Tunnel: 78
Gas Station: 2
Total: 9,702
Hours Day: 8,432
Night: 1,327
Dawn/Dusk: 18
Total: 9,777
Sample Results TBD
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 (86%) and night (14%) driving on San Francisco, CA bay area streets and highways from November 2018 to May 2019 with varying weather conditions.
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)
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)
6. No temperal filter applied
Sample Results mAP score coming soon.
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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