DAROD - A Deep Automotive Radar Object Detector on Range-Doppler maps

This is the official implementation of DAROD paper (2022 IEEE Intelligent Vehicle Symposium).
Colin Decourt, Rufin VanRullen, Thomas Oberlin, Dider Salle

Citation:

Please cite this paper as follows:

@INPROCEEDINGS{9827281,
  author={Decourt, Colin and VanRullen, Rufin and Salle, Didier and Oberlin, Thomas},
  booktitle={2022 IEEE Intelligent Vehicles Symposium (IV)},
  title={DAROD: A Deep Automotive Radar Object Detector on Range-Doppler maps},
  year={2022},
  volume={},
  number={},
  pages={112-118},
  doi={10.1109/IV51971.2022.9827281}}

Contents

In this repository we provide an adaption of the Faster R-CNN architecture for object detection on range-Doppler (RD) maps.
We provide training and evaluation scripts for Carrada and RADDet datasets.

Installation

This project requires tensorflow 2.4.1 and tensorflow-datasets 4.4.0 with CUDA 11.2. Clone the project:

git clone ...
cd darod

Install required packages

Create conda environment
conda create -n darod python=3.7 tensorflow-gpu=2.4.1 scikit-learn scikit-image matplotlib numpy scipy tqdm tabulate imageio

Install missing packages
pip3 install tensorflow-datasets==4.4.0 tensorflow-addons==0.13.0

Create tensorflow datasets records

For efficiency, we use tensorflow datasets to load and process data. Before training the model, create tensorflow datasets for Carrada and RADDet.

CARRADA dataset

Update path to Carrada dataset in datasets/carrada_builder/carrada.py (l 87):

def _split_generators(self, dl_manager: tfds.download.DownloadManager):
    """Returns SplitGenerators."""
    # TODO(carrada): Downloads the data and defines the splits
    path = "<path_to_carrada>"

    # TODO(carrada): Returns the Dict[split names, Iterator[Key, Example]]
    return {
        'train': self._generate_examples(path, 'train'),
        'test': self._generate_examples(path, 'test'),
        'val': self._generate_examples(path, 'val'),
    }

To create tf-records file for Carrada, use the following commands:

cd datasets/carrada_builder/
tfds build carrada --data_dir <path_to_tensorflow_datasets>
cd ..

RADDet dataset

Update path to RADDet dataset in datasets/raddet_builder/raddet.py (l 61):

def _split_generators(self, dl_manager: tfds.download.DownloadManager):
    """Returns SplitGenerators."""
    # TODO(carrada): Downloads the data and defines the splits
    train_path = "<path_to_raddet>/train/"
    test_path = "<path_to_raddet>/test/"
    # TODO(carrada): Returns the Dict[split names, Iterator[Key, Example]]
    return {
        'train': self._generate_examples(train_path, 'train'),
        'test': self._generate_examples(test_path, 'test'),
    }

Build the dataset:

cd raddet_builder/
tfds build raddet
cd ../../

Train the model

Update configuration files

Once the datasets are generated, update configuration files for Carrada and RADDet. Open config_carrada.json and config_raddet.json and change the following lines:

"dataset_version": "1.0.0", # 1.0.0 by defaults
"tfds_path": "<path_to_tfds_folder>", # <path_to_datasets>/tensorflow_datasets/

Train on Carrada

python train.py --config ./config/config_carrada.json --backup-dir ./logs/ --exp darod_carrada

Train on RADDet

python train.py --config ./config/config_raddet.json --backup-dir ./logs/ --exp darod_carrada

Eval the model

We provide DAROD pretrained weights. You can use them to evaluate the model.

We store the results in a .json file such that:

"class_id": { # metrics for class class_id 
  "AP": [
    AP@0.1,
    AP@0.3,
    AP@0.5,
    AP@0.7,
  ],
  "precision": [
    precision@0.1,
    precision@0.3,
    precision@0.5,
    precision@0.7,
  ],
  "recall": [
    recall@0.1,
    recall@0.3,
    recall@0.5,
    recall@0.7,
  ],
  "F1": [
    F1@0.1,
    F1@0.3,
    F1@0.5,
    F1@0.7,
  ],

...

"mean": { # mean of metrics over all classes
  "AP": [
      mAP@0.1,
      mAP@0.3,
      mAP@0.5,
      mAP@0.7,
  ],
  "precision": [
    mPrecision@0.1,
    mPrecision@0.3,
    mPrecision@0.5,
    mPrecision@0.7,
  ],
  "recall": [
    mRecall@0.1,
    mRecall@0.3,
    mRecall@0.5,
    mRecall@0.7,
  ],
  "F1": [
    mF1@0.1,
    mF1@0.3,
    mF1@0.5,
    mF1@0.7,
  ],
}

Eval on Carrada

Supposing, logs are in ./logs/darod_carrada:

python eval.py --path ./logs/darod_carrada

Eval on RADDet

Supposing, logs are in ./logs/darod_raddet:

python eval.py --path ./logs/darod_raddet

Acknowledgement

This implementation repository is based on the following Faster R-CNN implementation: https://github.com/FurkanOM/tf-faster-rcnn. Thanks to the authors for the work.