# Uncrewed Aerial Vehicle (UAV) Blueprint


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UAV Blueprint demonstrates how AI-based object detection can be integrated with UAV
flight controller telemetry on a companion compute platform.

Based on DL Streamer Pipeline Server, the application processes video from a UAV-mounted
camera or a simulated video file, detects objects across ten object classes, and outputs an
RTSP stream annotated with MAVLink telemetry (GPS, altitude, speed, heading). The stream is
consumable by any capable client, such as QGroundControl (QGC), VLC, and ffplay.
It runs the YOLOv8n-VisDrone, a model designed to recognize imagery typical for drone video.

![UAV drone](./_assets/fedaero-drone.drawio.svg)

The application supports two deployment modes depending on whether an external SDK is available.

![  uav vision analytics application architecture](./_assets/FedAero-uav-vision-architecture.drawio.svg)

| Component                                      | Role                                                                                                    |
| ---------------------------------------------- | ------------------------------------------------------------------------------------------------------- |
| RTSP / Video File / Live Camera Streams        | Input video source — UAV camera feed, a recorded video file, or a simulated RTSP stream                 |
| MAVLink UAV Telemetry                          | Telemetry input — GPS, altitude, speed, and heading received from the flight controller over UDP        |
| DL Streamer Pipeline Server (CPU / GPU / NPU)  | Core inference engine — runs YOLOv8n-VisDrone object detection and renders the telemetry overlay on each frame |
| RTSP Stream with Detection & Telemetry Overlay | Annotated output stream — processed video with bounding boxes and telemetry overlay, served over RTSP   |


## Deployment Modes

The application supports two deployment modes.

### 1. Standalone Mode (pymavlink)

Self-contained mode using PX4 SITL simulation and pymavlink for MAVLink communication. No external dependencies required.

[Get Started — Standalone Mode](./get-started/get-started-standalone.md)

### 2. UAV Mission Compute SDK Mode

Integration mode that connects to a running instance of the UAV Mission Compute SDK, enabling full mission control and multi-camera pipeline management.

[Get Started — UAV Mission Compute SDK Mode](./get-started/get-started-uavsdk.md)


To learn more about the application and how to use it, see
[How to use UAV Vision Analytics Application](./how-to-guides.md).

## Installation Guide

Follow the steps below to set up and run the UAV Blueprint:

1. [Infrastructure Setup](./infrastructure-setup.md) — Build the OS image, flash it to a bootable USB, and validate the provisioned platform.
2. [Install OEP SDKs](./get-started/install-oep-sdks.md) — Verify hardware accelerators and install the UAV Mission Compute SDK on the provisioned target.
3. [Install UAV Vision Analytics Application](install-uav-app.md) — Choose and follow the deployment mode that matches your setup (Standalone or UAV Mission Compute SDK).
4. [How to use UAV Vision Analytics Application](./how-to-guides.md) — Learn how to configure models, cameras, QGroundControl, and more.
5. [Benchmarks](./benchmark.md) — Measure stream density and hardware utilization.


## AI Agent Skills

This application supports AI agent skills for GitHub Copilot and compatible coding agents.
Skills cover operational tasks (running pipelines, benchmarking, troubleshooting) and
application creation (scaffolding new pymavlink or UAVSDK stacks). See
[Agent SKILLs](agents.md) for details.

## Intended and Responsible Use

### Intended Use

This project is intended to demonstrate the capabilities of Intel Edge AI for UAV object detection and live telemetry overlay. It is provided for reference and demonstration purposes only and is not intended to be deployed as-is or for alternate use cases or applications. This reference does not support authentication, TLS, or access controls between the app and external interfaces. These security measures should be provided by the solution builder.

### Responsible Use

Intel is committed to respecting human rights and avoiding complicity in human rights abuses.
See [Intel's Global Human Rights Principles](https://www.intel.com/content/www/us/en/policy/policy-human-rights.html).
Intel's products and software are intended only to be used in applications that do not cause
or contribute to a violation of an internationally recognized human right.

If you or anyone on your team becomes aware of instances of potentially inappropriate use,
regardless of severity, notify
[responsible-ai@intel.com](mailto:responsible-ai@intel.com) or use the
[Ethics Reporting Portal](https://www.intel.com/content/www/us/en/corporate-responsibility/ethics-and-compliance.html)
immediately.


:::{toctree}
:hidden:

Infrastructure Setup <./infrastructure-setup.md>
Install OEP SDKs <./get-started/install-oep-sdks.md>
Install UAV Vision Analytics Application <install-uav-app.md>
How to use UAV Vision Analytics Application <./how-to-guides.md>
Benchmarks <./benchmark.md>
Agent SKILLs <./agents.md>
Release Notes <./release-notes.md>

:::