CLRNet is a lane detection model. The CLRNet model is designed with line prior for lane detection, line iou loss as well as nms method, fused to extract contextual high-level features of lane line with low-level features, and refined by FPN multi-scale. Finally, the model achieved SOTA performance in lane detection datasets.

Model Zoo

CLRNet Results on CULane dataset

backbone	mF1	F1@50	F1@75	download	config
ResNet-18	54.98	79.46	62.10	model	config

Download

Download CULane. Then extract them to dataset/culane.

For CULane, you should have structure like this:

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

If you use Baidu Cloud, make sure that images in driver_23_30frame_part1.tar.gz and driver_23_30frame_part2.tar.gz are located in one folder driver_23_30frame instead of two seperate folders after you decompress them.

Now we have uploaded a small subset of CULane dataset to PaddleDetection for code checking. You can simply run the training script below to download it automatically. If you want to implement the results, you need to download the full dataset at th link for training.

Training

single GPU

python tools/train.py -c configs/clrnet/clr_resnet18_culane.yml

multi GPU

export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/clrnet/clr_resnet18_culane.yml

Evaluation

python tools/eval.py -c configs/clrnet/clr_resnet18_culane.yml -o weights=output/clr_resnet18_culane/model_final.pdparams

Inference

python tools/infer_culane.py -c configs/clrnet/clr_resnet18_culane.yml -o weights=output/clr_resnet18_culane/model_final.pdparams --infer_img=demo/lane00000.jpg

Notice: The inference phase does not support static model graph deploy at present.

Citations

@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}
}

README.md

CLRNet (CLRNet: Cross Layer Refinement Network for Lane Detection)

Table of Contents

Introduction