The production specs on our homepage are the target. This page shows what's working right now — a real prototype running real-time drone detection on edge hardware today.
What's proven
Real-time detection pipeline
19 FPS YOLO inference on Raspberry Pi 5
Dual camera system
IMX296 global shutter + IMX708 NoIR operational
Autonomous gimbal tracking
PID-controlled pan/tilt servo lock-on
Live video stream
MJPEG with bounding box overlay
Multi-object tracking
ByteTrack with Kalman prediction
Full Rust stack
Zero-dependency edge deployment
Detection in Action
Screen captures from the live prototype. All inference runs on-device — no cloud, no latency.
Screenshots and demo footage coming soon — we're capturing real detection runs from the prototype.
Building Detection
The prototype runs our first custom-trained drone detection model — purpose-built to detect small drones against sky and terrain backgrounds. We're iterating on the training data and model architecture to improve range and reduce false positives.
Generic object detection models aren't optimized for the drone detection problem. Drones are small, fast, and viewed against cluttered backgrounds at long range. Our purpose-built model is trained on real drone imagery to maximize detection range and minimize false positives.
The training dataset combines real-world drone footage, our own test captures from the prototype's cameras, and synthetic data augmentation to cover edge cases like different lighting, weather, and backgrounds.
Collect footage. Annotate drone positions frame by frame. Train a YOLO model on the annotated data. Export to ONNX and deploy to the Pi. Test in the field. Review failures, collect more targeted data, and iterate. Each cycle improves real-world performance on the actual hardware the model will run on.
The first version of our custom drone detection model is running on the prototype. The full pipeline — capture, detect, track, and gimbal control — runs in real time on edge hardware. We're actively iterating: testing in the field, reviewing failures, collecting more targeted data, and retraining to improve performance each cycle.
POC Specifications
Compute
Sensors
Software
Live
The POC prototype streams live video with bounding box overlays. Connect to the same network to watch real-time detection.
Demo video coming soon.
Roadmap