PHASE 1 · AI SKY TRACKER

A low-cost visual sensing platform, starting with one passive sky-tracking node.

A camera, a model and a pan-tilt mount that watch the sky, follow what they see and log every sighting.

View the code See where it stands

About $113 in new parts for the sensing hardware (camera, servos, mount, power), before the computer that runs the model.

01The math is backwards

A small drone costs a few hundred dollars. Systems built to detect one cost tens of thousands.

A small drone
$100s
Typical detection system
$10,000s
Skynode sensing parts, before the computer
~$113

Price comparison only, not a capability comparison. Skynode is one passive camera node and has not run on real hardware yet.

Skynode is a first step toward making that sensing layer cheap enough to put anywhere.

USE CASES

Early use cases I'm exploring: farms, small airports, stadiums, and power substations. First hypothesis to test: small general-aviation airfields.

02System

How it works

  1. 01 28,526 IMAGES

    SENSE

    A YOLO object-detection model looks at the camera feed and picks out aircraft and drones. It is training now on a dataset of 28,526 labeled images.

  2. 02 P92.5 T47.0

    TRACK

    A Raspberry Pi Pico drives two small servos on a pan-tilt mount, turning the camera to keep the target centered.

  3. 03 TIME · CLASS · CONF

    LOG

    Every sighting will be saved with the time, what it was, how confident the model was, and which way the camera was pointing.

SAMPLE · SENSOR VIEW · SIMULATED NODE-01
SAMPLE: a simulation of the overlay the node draws on its camera feed (brain/overlay.py). The locked target gets the bold box and a solid label; anything else gets a thin box. The camera turns to keep the lock in the centre ring.
3D render of the Skynode pan-tilt mount from its CAD files: a base holding the pan servo, a yoke on top, and a camera arm between the yoke's uprights.

PAN-TILT MOUNT

  • BASE

    Holds the pan servo, shaft up, and screws down with four M3 screws.

  • YOKE

    Sits on the pan horn, holds the tilt servo, and carries the M3 pivot.

  • CAMERA ARM

    Webcam cradle on the tilt horn and pivot.

Rendered live from the CAD files in this repo, with the two servos drawn as outlines, and moved through its pan and tilt axes. The physical build is next.

TEST RANGE

The test range is free: real air traffic passes overhead all day. Planes publicly broadcast their positions (ADS-B), so I can compare what the camera saw with what actually flew over and report real accuracy numbers.

03Where it stands

Status

  1. Pico servo firmware: smooth motion, calibration, command protocol

    DONE

  2. Detection and tracking loop, working in simulation, 226 automated tests

    DONE

  3. Pan-tilt mount designed in CAD, with STEP and STL files

    DONE

  4. Wiring diagram and bill of materials

    DONE

  5. Sighting logger (built and tested in simulation)

    DONE

  6. Live dashboard (built and tested in simulation)

    DONE

  7. Detection model v3 with drone and aircraft classes (in training)

    IN PROGRESS

  8. Run the logger and dashboard on real hardware

    NEXT

  9. Build the physical hardware

    NEXT

  10. Outdoor test

    NEXT

  11. Test against real flight data (ADS-B) and publish accuracy results

    NEXT

  12. Pi 4 + solar deployment

    NEXT

Results so far

The v2 model, run on 43 real-world videos of planes, military jets and drones. This tests the model on recorded video. It is not a hardware test.

False-drone rate on real aircraft
7.5%
798 of 10,657 frames were called a drone
Civilian planes called drones
0.8%
Military jets called drones
10.8%
Mostly distant F-35s

Model v2, 43 real-world videos. v3 (drone and aircraft classes, 960 px input) is in training. There are no v3 results yet.

04Honest limits

Known limits

  • RANGE · ESTIMATE

    A wide-lens webcam detects small drones only at short range, likely tens of meters (an estimate, still to be measured). Aircraft are detectable much farther.

  • CONDITIONS

    Visual sensing is weaker in darkness, fog and rain.

  • NOT REMOTE ID

    It can see drones that broadcast nothing, unlike Remote ID, but it does not replace Remote ID or RF sensors.

  • NOT BUILT YET

    Nothing has run on real hardware yet.

05About me

I'm Braden, I'm 14, and I like building things that work in the real world. I started with Sunnode, a small server that runs entirely on solar power from a panel on my fence. Then I got curious about drones, trained my first detection model, and Skynode was the next step. I've run a backyard gardening business, and I shelved a software startup after learning how hard it is to sell to companies when you're 13. I'm fascinated by geopolitics and technology, and I want to build an aerospace and defense company someday. Skynode is where I'm starting.

“You only live once so go out there and make the most of every moment by chasing your dreams and living without regret”

— Braden

06Roadmap

From one node to a company

This is Phase 1. The long-term goal is autonomy for missile and drone detection and defense systems.

  1. PHASE 1 · NOW

    One working node

    A camera that detects, tracks, and logs aircraft and drones, with real accuracy numbers.

  2. PHASE 2

    A network of nodes

    Several nodes working together can work out where a drone is and where it is heading.

  3. PHASE 3

    Autonomous drones

    For inspection, search and rescue, and drone detection and defense systems, built on the same sensing and tracking brain.

  4. PHASE 4

    A company

    An aerospace and defense company building autonomy for missile and drone detection and defense systems, once I'm old enough to start one.

Phase 1 is passive by design. It carries no payloads, never jams, and never transmits on aircraft or drone frequencies. It only watches.