Light Conditions Style Transfer

Paper

Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style Transfer

Accepted by 2020 IEEE Intelligent Vehicles Symposium (IV 2020).

The main framework is as follows:

Empirically, lane detection model trained using our method demonstrated adaptability in low-light conditions and robustness in complex scenarios. (It can achieve 73.9 F1-measure in CULane testing set)

Datasets

CULane

The whole dataset is available at CULane.

CULane
├── driver_23_30frame       # training&validation
├── driver_161_90frame      # training&validation
├── driver_182_30frame      # training&validation
├── driver_193_90frame      # testing
├── driver_100_30frame      # testing
├── driver_37_30frame       # testing
├── laneseg_label_w16       # labels
└── list                    # list

Generated Images

The images in low-light conditions are generated by the proposed SIM-CycleGAN.

Requirements

• PyTorch 1.3.0.

• Matlab (for tools/prob2lines), version R2017a or later.

• Opencv (for tools/lane_evaluation).

Before start

conda create -n  your_env_name python=3.6
conda activate your_env_name
conda install pytorch==1.3.0 torchvision==0.4.1 cudatoolkit=10.0 -c pytorch
pip install -r requirements.txt 

SIM-CycleGAN

The source code for SIM-CycleGAN has been released. (11/03)

train

Train your own SIM-CycleGAN model as follow.

python train.py  --name repo_name \
                 --dataset_loadtxt_A /path/to/domain_A_txt \
                 --dataset_loadtxt_B /path/to/domain_B_txt \
                 --gpu_ids 6 \

test

Use your trained model to generate images.

python test.py   --name repo_name \
                 --model simcycle_gan \
                 --dataset_loadtxt_A /path/to/domain_A_txt \
                 --dataset_loadtxt_B /path/to/domain_B_txt \
                 --gpu_ids 6 \

Lane Detetcion

The source code used for the lane detction is made publicly available by HOU Yuenan.

Test for Demo

We provide demo for testing a single image or a video.

sh ./demo.sh

You can get the results as follow.

Result for probability map

Result for points

If you want to test the model for video, you can set mode=0 in demo.sh.

Evaluate the Model

The trained model used in this paper is available in ./trained.

1. Run test script

sh ./test_erfnet.sh

2. Get lines from probability maps

cd tools/prob2lines
matlab -nodisplay -r "main;exit"

Please check the file path in Matlab code before.

3. Evaluation

cd /tools/lane_evaluation
make
# You may also use cmake instead of make, via:
# mkdir build && cd build && cmake ..
sh eval_all.sh    # evaluate the whole test set
sh eval_split.sh  # evaluate each scenario separately

The evaluation results are saved in /tools/lane_evaluation/output.

Performance

Light Conditions Stlye Transfer

Some examples of real images in normal light conditions and their corresponding translations images in low-light conditions.

Lane Detetcion

Performance ( (F₁-measure) ) of different methods on CULane testing set. For crossroad, only FP is shown.

The probability maps output by the three methods above are shown as following

To do

• Add attenction on ERFNet

• Open the source code for SIM-CycleGAN

• Upgade pytorch (from 0.3.0 to 1.3.0)

• Upload demo for test

Citation

Please cite this in your publication if our work helps your research.

@inproceedings{Liu2020Lane,
  title={Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style Transfer},
  author={Liu, Tong and Chen, Zhaowei and Yang, Yi and Wu, Zehao and Li, Haowei},
  booktitle={2020 IEEE intelligent vehicles symposium (IV)},
  year={2020},
}

Acknowledgement

This project refers to the following projects:

• Codes-for-Lane-Detection
• SCNN
• CycleGAN