Edge Drone Detection Uses Audio Visual Fusion
This project designs an edge-AI drone detection system combining audio and visual sensor fusion, prioritizing on-device inference for low-latency detection. It uses a microphone for continuous audio classification and a camera to verify detections, merging both modalities on an Arduino UNO Q via Edge Impulse to reduce false positives. Currently in proposal and design, hardware integration and model training will begin upon selection in the Sensor Fusion Challenge.
Key Points
- 1Implements on-device audio classification and visual verification using Arduino UNO Q and Edge Impulse.
- 2Addresses vision limitations in low light and long-range scenarios by detecting drones via distinctive acoustic signatures.
- 3Enables incremental development and real-time edge deployment for low-latency, privacy-preserving drone detection systems.
Scoring Rationale
Practical, reproducible edge sensor-fusion approach drives usefulness, limited by early-stage proposal and lack of trained models.
Sources
Public references used for this report.
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