DroneWatch — hackathon submission - #2
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- detect_drones.py: real-time detection with persistent track IDs, permanent drone lock (no downgrade on bird overlap), audio alert, annotated video saving, bytetrack/botsort support, frame skip for performance, BGR color fix - retrain.py: one-command fine-tuning pipeline - configs/drone.yaml: dataset config for 418-image drone dataset - configs/dronewatch.yaml: tuned BoT-SORT tracker (90-frame buffer, sparseOptFlow GMC, low thresholds for distant drones) - models/yolo26n-drone.safetensors: fine-tuned weights (Bird/Drone/Plane) - README.md: full project docs, quick start, architecture, CLI reference Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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DroneWatch — On-Device Drone Detection for Law Enforcement
Real-time drone detection running entirely on Apple Silicon. No cloud. No $50k contract.
What's included
detect_drones.py— live detection app with persistent tracking, drone lock, audio alert, video saveretrain.py— one-command fine-tuning pipelinemodels/yolo26n-drone.safetensors— fine-tuned weights (Bird/Drone/Plane, 418 images)configs/dronewatch.yaml— tuned BoT-SORT tracker configREADME.md— full docs and quick startRun it
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