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https://github.com/yizhou-wang/RODNet
RODNet: Radar object detection network
https://github.com/yizhou-wang/RODNet
autonomous-driving detection object-detection paper pytorch radar rodnet
Last synced: about 2 months ago
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RODNet: Radar object detection network
- Host: GitHub
- URL: https://github.com/yizhou-wang/RODNet
- Owner: yizhou-wang
- License: mit
- Created: 2020-11-03T07:30:30.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2022-08-11T18:25:00.000Z (about 2 years ago)
- Last Synced: 2024-07-02T02:57:12.235Z (3 months ago)
- Topics: autonomous-driving, detection, object-detection, paper, pytorch, radar, rodnet
- Language: Python
- Homepage:
- Size: 2.37 MB
- Stars: 225
- Watchers: 5
- Forks: 72
- Open Issues: 24
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# RODNet: Radar Object Detection Network
This is the official implementation of our RODNet papers
at [WACV 2021](https://openaccess.thecvf.com/content/WACV2021/html/Wang_RODNet_Radar_Object_Detection_Using_Cross-Modal_Supervision_WACV_2021_paper.html)
and [IEEE J-STSP 2021](https://ieeexplore.ieee.org/abstract/document/9353210).
[[Arxiv]](https://arxiv.org/abs/2102.05150)
[[Dataset]](https://www.cruwdataset.org)
[[ROD2021 Challenge]](https://codalab.lisn.upsaclay.fr/competitions/1063)
[[Presentation]](https://youtu.be/UZbxI4o2-7g)
[[Demo]](https://youtu.be/09HaDySa29I)
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
Clone RODNet code.
```commandline
cd $RODNET_ROOT
git clone https://github.com/yizhou-wang/RODNet.git
```
Create a conda environment for RODNet. Tested under Python 3.6, 3.7, 3.8.
```commandline
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.
```commandline
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`](https://github.com/yizhou-wang/cruw-devkit) repository for detailed instructions.
```commandline
git clone https://github.com/yizhou-wang/cruw-devkit.git
cd cruw-devkit
pip install .
cd ..
```
Setup RODNet package.
```commandline
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
Download [ROD2021 dataset](https://www.cruwdataset.org/download#h.mxc4upuvacso).
Follow [this script](https://github.com/yizhou-wang/RODNet/blob/master/tools/prepare_dataset/reorganize_rod2021.sh) to reorganize files as below.
```
data_root
- sequences
| - train
| | -
| | | - IMAGES_0
| | | | - .jpg
| | | | - ***.jpg
| | | - RADAR_RA_H
| | | - _.npy
| | | - ***.npy
| | - ***
| |
| - test
| -
| | - RADAR_RA_H
| | - _.npy
| | - ***.npy
| - ***
|
- annotations
| - train
| | - .txt
| | - ***.txt
| - test
| - .txt
| - ***.txt
- calib
```
Convert data and annotations to `.pkl` files.
```commandline
python tools/prepare_dataset/prepare_data.py \
--config configs/ \
--data_root \
--split train,test \
--out_data_dir data/
```
## Train models
```commandline
python tools/train.py --config configs/ \
--data_dir data/ \
--log_dir checkpoints/
```
## Inference
```commandline
python tools/test.py --config configs/ \
--data_dir data/ \
--checkpoint \
--res_dir results/
```