RODNet: Radar Object Detection using Cross-Modal Supervision

This is the official implementation of our RODNet paper at WACV 2021.

[Paper] [Dataset]

Please cite our WACV 2021 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}
}

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.

conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

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 .

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/