Ultra-Fast-Lane-Detection

The implementation of the paper "Ultra Fast Structure-aware Deep Lane Detection".

Updates: Our paper has been accepted by ECCV2020.

The evaluation code is modified from SCNN and Tusimple Benchmark.

Demo

Install

a. Clone the project

git clone https://github.com/cfzd/Ultra-Fast-Lane-Detection
cd Ultra-Fast-Lane-Detection

b. Create a conda virtual environment and activate it

conda create -n lane-det python=3.7 -y
conda activate lane-det

c. Install dependencies

# If you don't have pytorch
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch 

pip install -r requirements.txt

d. Install CULane evaluation tools. This tools requires OpenCV C++. Please follow here to install OpenCV C++. When you build OpenCV, remove the paths of anaconda from PATH or it will be failed.

# First you need to install OpenCV C++. 
# After installation, make a soft link of OpenCV include path.
ln -s /usr/local/include/opencv4/opencv2 /usr/local/include/opencv2

cd evaluation/culane
make

e. Data preparation

Download CULane and Tusimple. Then extract them to $CULANEROOT and $TUSIMPLEROOT. For Tusimple, the segmentation annotation is not provided, hence we need to generate segmentation from the json annotation.

cd $TUSIMPLEROOT
python scripts/convert_tusimple.py --root $TUSIMPLEROOT
# this will generate segmentations and 2 list files train_gt.txt test.txt

Get Started

Copy a config from configs/culane.py or configs/tusimple.py, then modifiy data_root, log_path and other settings in your config.

For single gpu training, run

python train.py configs/path_to_your_config

For multi-gpu training, run

sh launch_training.sh

# If there is no pretrained torchvision model, multi-gpu training may result in multiple downloading. You can first download the corresponding models manually, and then restart the multi-gpu training.

python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py configs/path_to_your_config

Besides config style settings, we also support command line style one. You can override a setting like

python train.py configs/path_to_your_config --batch_size 8

The batch_size will be set to 8 during training.

To visualize the log with tensorboard, run

tensorboard --logdir log_path --bind_all

Trained models

We provide two trained Res-18 models on CULane and Tusimple.

Dataset	Metric paper	Metric This repo	Avg FPS on GTX 1080Ti	Model
Tusimple	95.87	95.82	306	GoogleDrive/BaiduDrive(code:bghd)
CULane	68.4	69.7	324	GoogleDrive/BaiduDrive(code:w9tw)

For evaluation, run

mkdir tmp

python test.py configs/culane.py --test_model path_to_culane_18.pth --test_work_dir ./tmp

python test.py configs/tusimple.py --test_model path_to_tusimple_18.pth --test_work_dir ./tmp

Same as training, multi-gpu evaluation is also supported.

Citation