MMLaneDet is an open source lane detection toolbox based on Pytorch. It contains many excellent lane detection models and our DALNet (The code will be made available after acceptance of the paper).
Python == 3.8
CUDA == 11.1
pytorch == 1.9.1
mmcv-full == 1.5.1
mmdet == 2.25.0
python setup.py developDownload Tusimple. Then extract them to $TUSIMPLEROOT. Create link to data directory.
cd $LANEDET_ROOT
mkdir -p data
ln -s $TUSIMPLEROOT data/tusimpleFor Tusimple, the segmentation annotation is not provided, hence we need to generate segmentation from the json annotation.
python tools/dataset_converts/generate_seg_tusimple.py --root $TUSIMPLEROOTDownload CULane. Then extract them to $CULANEROOT. Create link to data directory.
cd $LANEDET_ROOT
mkdir -p data
ln -s $CULANEROOT data/CULaneFor 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
Download LLAMAS. Then extract them to $LLAMASROOT. Create link to data directory.
cd $LANEDET_ROOT
mkdir -p data
ln -s $LLAMASROOT data/llamas# train
bash tools/dist_train.sh /path_to_your_config 8
# inference
bash tools/dist_test.sh /path_to_your_config /path_to_your_pth 8 --eval mAP| Model | Setting | BatchSize | Lr Schd | Acc | F1 | Config | Download |
|---|---|---|---|---|---|---|---|
| LaneATT | r18 | 4(gpus) * 8 | 100 epochs | 95.85 | 96.69 | config | model/log |
| CLRNet | r18 | 4(gpus) * 8 | 70 epochs | 96.81 | 97.63 | config | model/log |
| BezierLaneNet | r18 | 4(gpus) * 8 | 400 epochs | 95.79 | 96.24 | config | model/log |
| GANet | r18 | 4(gpus) * 8 | 70 epochs | 95.99 | 97.23 | config | model/log |
| Model | Setting | BatchSize | Lr Schd | F1@50 | F1@75 | mF1 | Config | Download |
|---|---|---|---|---|---|---|---|---|
| CLRNet | r18 | 4(gpus) * 8 | 15 epochs | 79.32 | 62.04 | 55.02 | config | model/log |
| CondLane | r18 | 2(gpus) * 4 | 16 epochs | 77.99 | 57.48 | 51.42 | config | model/log |
| BezierLaneNet | r18 | 4(gpus) * 8 | 36 epochs | 73.11 | 44.43 | 42.41 | config | model/log |
| LaneATT | r18 | 4(gpus) * 8 | 15 epochs | 76.31 | 53.01 | 48.19 | config | model/log |
| Model | Setting | BatchSize | Lr Schd | F1@50 | F1@75 | mF1 | Config | Download |
|---|---|---|---|---|---|---|---|---|
| CLRNet | r18 | 4(gpus) * 8 | 20 epochs | 96.68 | 85.63 | 71.51 | config | model/log |
I don't have enough time to do all the experiments and optimize the parameters, so some of the results are not fully aligned. I would like to have partners on board to help optimize this project.
| Model | Setting | BatchSize | Lr Schd | F1@50 | F1@75 | mF1 |
|---|---|---|---|---|---|---|
| BezierLaneNet | r18 | 4(gpus) * 8 | 400 epochs | 85.13 | 38.62 | 42.81 |
| GANet | r18 | 4(gpus) * 8 | 70 epochs | 95.68 | 62.01 | 57.64 |
| CondaLaneNet | r18 | 4(gpus) * 8 | 70 epochs | 95.10 | 53.10 | 52.37 |
| UFLD | r18 | 4(gpus) * 8 | 70 epochs | 93.67 | 57.74 | 53.50 |
| LaneATT | r18 | 4(gpus) * 8 | 70 epochs | 93.82 | 58.97 | 55.57 |
| DALNet | r18 | 4(gpus) * 8 | 70 epochs | 96.43 | 65.48 | 59.79 |
Aliyundirve: https://www.alipan.com/s/n1HV3tFpWCF
Many thanks to the authors of mmdetection, lanedet and pytorch-auto-drive.
If you find mmLaneNet or DALNet is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.
@article{yu2023dalnet,
title={DALNet: A Rail Detection Network Based on Dynamic Anchor Line},
author={Zichen Yu and Quanli Liu and Wei Wang and Liyong Zhang and Xiaoguang Zhao},
journal={arXiv preprint arXiv:2308.11381},
year={2023}
}