/CLRNet

Pytorch implementation of our paper "CLRNet: Cross Layer Refinement Network for Lane Detection" (CVPR2022 Acceptance).

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CLRNet: Cross Layer Refinement Network for Lane Detection

Pytorch implementation of the paper "CLRNet: Cross Layer Refinement Network for Lane Detection" (CVPR2022 Acceptance).

Introduction

Arch

  • CLRNet exploits more contextual information to detect lanes while leveraging local detailed lane features to improve localization accuracy.
  • CLRNet achieves SOTA result on CULane, Tusimple, and LLAMAS datasets.

Installation

Prerequisites

Only test on Ubuntu18.04 and 20.04 with:

  • Python >= 3.8 (tested with Python3.8)
  • PyTorch >= 1.6 (tested with Pytorch1.6)
  • CUDA (tested with cuda10.2)
  • Other dependencies described in requirements.txt

Clone this repository

Clone this code to your workspace. We call this directory as $CLRNET_ROOT

git clone https://github.com/Turoad/clrnet

Create a conda virtual environment and activate it (conda is optional)

conda create -n clrnet python=3.8 -y
conda activate clrnet

Install dependencies

# Install pytorch firstly, the cudatoolkit version should be same in your system.

conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

# Or you can install via pip
pip install torch==1.8.0 torchvision==0.9.0

# Install python packages
python setup.py build develop

Data preparation

CULane

Download CULane. Then extract them to $CULANEROOT. Create link to data directory.

cd $CLRNET_ROOT
mkdir -p data
ln -s $CULANEROOT data/CULane

For CULane, you should have structure like this:

$CULANEROOT/driver_xx_xxframe    # data folders x6
$CULANEROOT/laneseg_label_w16    # lane segmentation labels
$CULANEROOT/list                 # data lists

Tusimple

Download Tusimple. Then extract them to $TUSIMPLEROOT. Create link to data directory.

cd $CLRNET_ROOT
mkdir -p data
ln -s $TUSIMPLEROOT data/tusimple

For Tusimple, you should have structure like this:

$TUSIMPLEROOT/clips # data folders
$TUSIMPLEROOT/lable_data_xxxx.json # label json file x4
$TUSIMPLEROOT/test_tasks_0627.json # test tasks json file
$TUSIMPLEROOT/test_label.json # test label json file

For Tusimple, the segmentation annotation is not provided, hence we need to generate segmentation from the json annotation.

python tools/generate_seg_tusimple.py --root $TUSIMPLEROOT
# this will generate seg_label directory

LLAMAS

Dowload LLAMAS. Then extract them to $LLAMASROOT. Create link to data directory.

cd $CLRNET_ROOT
mkdir -p data
ln -s $LLAMASROOT data/llamas

Unzip both files (color_images.zip and labels.zip) into the same directory (e.g., data/llamas/), which will be the dataset's root. For LLAMAS, you should have structure like this:

$LLAMASROOT/color_images/train # data folders
$LLAMASROOT/color_images/test # data folders
$LLAMASROOT/color_images/valid # data folders
$LLAMASROOT/labels/train # labels folders
$LLAMASROOT/labels/valid # labels folders

Getting Started

Training

For training, run

python main.py [configs/path_to_your_config] --gpus [gpu_num]

For example, run

python main.py configs/clrnet/clr_resnet18_culane.py --gpus 0

Validation

For testing, run

python main.py [configs/path_to_your_config] --[test|validate] --load_from [path_to_your_model] --gpus [gpu_num]

For example, run

python main.py configs/clrnet/clr_dla34_culane.py --validate --load_from culane_dla34.pth --gpus 0

Currently, this code can output the visualization result when testing, just add --view. We will get the visualization result in work_dirs/xxx/xxx/visualization.

Results

F1 vs. Latency for SOTA methods on the lane detection

CULane

Backbone mF1 F1@50 F1@75
ResNet-18 55.23 79.58 62.21
ResNet-34 55.14 79.73 62.11
ResNet-101 55.55 80.13 62.96
DLA-34 55.64 80.47 62.78

TuSimple

Backbone F1 Acc FDR FNR
ResNet-18 97.89 96.84 2.28 1.92
ResNet-34 97.82 96.87 2.27 2.08
ResNet-101 97.62 96.83 2.37 2.38

LLAMAS

Backbone valid
  mF1      F1@50   F1@75		 test
F1@50
ResNet-18 70.83     96.93     85.23 96.00
DLA-34 71.57     97.06     85.43 96.12

“F1@50” refers to the official metric, i.e., F1 score when IoU threshold is 0.5 between the gt and prediction. "F1@75" is the F1 score when IoU threshold is 0.75.

Citation

If our paper and code are beneficial to your work, please consider citing:

@InProceedings{Zheng_2022_CVPR,
    author    = {Zheng, Tu and Huang, Yifei and Liu, Yang and Tang, Wenjian and Yang, Zheng and Cai, Deng and He, Xiaofei},
    title     = {CLRNet: Cross Layer Refinement Network for Lane Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {898-907}
}

Acknowledgement