/RODNet

RODNet: Radar object detection network

Primary LanguagePythonMIT LicenseMIT

RODNet: Radar Object Detection Network

This is the official implementation of our RODNet papers at WACV 2021 and IEEE J-STSP 2021.

[Arxiv] [Dataset] [ROD2021 Challenge] [Presentation] [Demo]

RODNet Overview

Please cite our paper if this repository is helpful for your research:

@inproceedings{wang2021rodnet,
  author={Wang, Yizhou and Jiang, Zhongyu and Gao, Xiangyu and Hwang, Jenq-Neng and Xing, Guanbin and Liu, Hui},
  title={RODNet: Radar Object Detection Using Cross-Modal Supervision},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  month={January},
  year={2021},
  pages={504-513}
}

@article{wang2021rodnet,
  title={RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization},
  author={Wang, Yizhou and Jiang, Zhongyu and Li, Yudong and Hwang, Jenq-Neng and Xing, Guanbin and Liu, Hui},
  journal={IEEE Journal of Selected Topics in Signal Processing},
  volume={15},
  number={4},
  pages={954--967},
  year={2021},
  publisher={IEEE}
}

Installation

Create a conda environment for RODNet. Tested under Python 3.6, 3.7, 3.8.

conda create -n rodnet python=3.* -y
conda activate rodnet

Install pytorch. Note: If you are using Temporal Deformable Convolution (TDC), we only tested under pytorch<=1.4 and CUDA=10.1. Without TDC, you should be able to choose the latest versions. If you met some issues with environment, feel free to raise an issue.

conda install pytorch=1.4 torchvision cudatoolkit=10.1 -c pytorch  # if using TDC
# OR
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch  # if not using TDC

Install cruw-devkit package. Please refer to cruw-devit repository for detailed instructions.

git clone https://github.com/yizhou-wang/cruw-devkit.git
cd cruw-devkit
pip install -e .
cd ..

Setup RODNet package.

pip install -e .

Note: If you are not using TDC, you can rename script setup_wo_tdc.py as setup.py, and run the above command. This should allow you to use the latest cuda and pytorch version.

Prepare data for RODNet

python tools/prepare_dataset/prepare_data.py \
        --config configs/<CONFIG_FILE> \
        --data_root <DATASET_ROOT> \
        --split train,test \
        --out_data_dir data/<DATA_FOLDER_NAME>

Train models

python tools/train.py --config configs/<CONFIG_FILE> \
        --data_dir data/<DATA_FOLDER_NAME> \
        --log_dir checkpoints/

Inference

python tools/test.py --config configs/<CONFIG_FILE> \
        --data_dir data/<DATA_FOLDER_NAME> \
        --checkpoint <CHECKPOINT_PATH> \
        --res_dir results/