Empowering safety in Thermal Imaging using Rust

An efficient framework to inference a UAV-based Thermal Imaging detector
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact
8. Acknowledgments

About The Project

ROS 2 package based on Rust to detect object from thermal cameras.

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Built With

• 
• 

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Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps. If you want to use docker skip the installation process.

Installation

1. Install ros2_rust

2. Create workspace folder in your home directory

   mkdir ~/workspace

3. Define the environment variable for ONNX Runtime Version (to be changed for a custom YOLO)

   export ORT_VERSION=1.13.1

4. Clone the repo into the folder ~/workspace

   git clone https://github.com/Pamasi/yolo_tir_rust.git ~/workspace/yolo_tir_rust
   

5. Download ROS2 Dependencies

   vcs import src < ros2_rust_humble.repos
   . /opt/ros/humble/setup.sh

6. Build the ROS 2 workspace

   colcon build 

Docker

1. Install docker

2. Install docker-compose

3. Build the image

   docker build . -t ros2_rust
   

4. Activate docker-compose in the background

   docker-compose up -d

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Usage

Setup the workspace only when the docker volume is created for the first time

. setup_docker.sh

Every time a new onnx file is added

1. Source the environment first

   . ros_entrypoint.sh

2. Build the ROS 2 workspace

   colcon build 

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Roadmap

• Testing
• Docker integration

See the open issues for a full list of proposed features (and known issues).

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE for more information.

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Contact

Paolo Dimasi - paolo.dimasi@outlook.com

Project Link: https://github.com/Pamasi/yolo_tir_rust

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Acknowledgments

• Cross-Domain Object Detection for UAV-based Thermal Imaging
• ros2_rust
• onnxruntime_rs

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