rollingcoconut/drone-detection-assignment3
UAV Drone Detection and Tracking Detect and track drones in video using YOLO fine-tuned detector, Kalman Filter w/ tuned noise parameters. Source Videos Video URL drone_video_1 YouTube - DhmZ6W1UAv4 drone_video_2 YouTube - YrydHPwRelI Training Dataset Dataset: lgrzybowski/seraphim-drone-detection-dataset Training images used: 33296 (from ~77K ) Test images: 8349 Detector Model: YOLOv8n fine-tuned on Seraphim… See the full description on the dataset page: https://huggingface.co/datasets/rollingcoconut/drone-detection-assignment3.
UAV Drone Detection and Tracking
Detect and track drones in video using YOLO fine-tuned detector, Kalman Filter w/ tuned noise parameters.
Source Videos
Training Dataset
- Dataset: lgrzybowski/seraphim-drone-detection-dataset
- Training images used: 33296 (from ~77K )
- Test images: 8349
Detector
- Model: YOLOv8n fine-tuned on Seraphim Drone Detection Dataset
- Training: 15 epochs, 640x640, batch 16
- Classes: 1 (drone)
- Performance: Precision 0.93, Recall 0.863
- Frame extraction: 5 fps via ffmpeg
Schema
detections — Frame-Level Drone Detections
One row per video frame that contains at least one drone detection. Confidence threshold >= 0.5.
Example detections value:
[{"bbox": [839.67, 173.83, 919.57, 229.07], "confidence": 0.8177}]NOTE: [x_min, y_min, x_max, y_max] pixel coordinates.