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 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 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 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 |
| 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 temporal 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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FLIR ADK Quickstart Guide
FLIR ADK Datasheet