WIT-UAS: A Wildland-fire Infrared Thermal Dataset to Detect Crew Assets From Aerial Views

About

• This dataset contains bounding box annotated Wildland-fire Infrared Thermal (WIT) images for crew assets detection with Unmanned Aerial Systems (UAS). It is captured during prescribed burns. The associated paper can be found here.

• Available Labels

Download Dataset

pip install minio
python scripts/download_data.py

Test Object Detection

Clone the repo locally:

git clone --recursive https://github.com/castacks/WIT-UAS-Dataset.git

Environment Setup

There are 2 options:

• Docker (recommended):

  1. Install Docker and nvidia-docker2

  2. Run the pre-built docker image (automatically pulls when missing):

     ./scripts/run.sh

  3. Attach to the running container:

     docker attach wit-uas-dataset

• Conda/Mamba environment manager:

  1. Install Anaconda or Mamba

  2. Create environment for WIT:

     mamba env create -f environment.yaml

  3. Activate WIT:

     mamba activate wit-uas

Visualization

• Visualization using wandb is optional, but when needed:

  1. Register an account on wandb.ai

  2. Login to your wandb account locally:

     wandb login

Send an email to Mukai (Tom Notch) Yu if you are not invited to team cmu-ri-wildfire.

Run training on WIT dataset

To train the models: ./model/yolo/train.py or ./model/ssd/train.py

You may need to adjust the batch size according to your GPU memory by using the --batch-size argument

How To Contribute

1. Clone the repo locally

   git clone --recursive https://github.com/castacks/WIT-UAS-Dataset.git

2. Create a new branch for your work:

   git checkout -b <branch-name>

3. Setup required development environment

   ./scripts/setup.sh

Citation

If you find this repository useful for your work, please cite the following paper:

@INPROCEEDINGS{jong2023wit,
  author={Jong, Andrew and Yu, Mukai and Dhrafani, Devansh and Kailas, Siva and Moon, Brady and Sycara, Katia and Scherer, Sebastian},
  booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={WIT-UAS: A Wildland-Fire Infrared Thermal Dataset to Detect Crew Assets from Aerial Views}, 
  year={2023},
  pages={11464-11471},
  url = {https://arxiv.org/pdf/2312.09159},
  doi={10.1109/IROS55552.2023.10341683}
}