DHPM

Paper: Dense Hybrid Proposal Modulation for Lane Detection

*This repository is based on BezierLaneNet, we have improved it by applying availability constraint, diversity constraint and discrimination constraint, so users only need to download the code of BezierLaneNet, and then download "hungarian_bezier_loss.py" in this repository to replace it. For different datasets, please modify the values in lines 12 and 13.

*To experiment on CurveLanes, you need to first use convert_curvelanes.py and convert_curvelanes.py under https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2 to generate segmentation and labels. Thanks a lot for their help.

*BezierLaneNet's address is https://github.com/voldemortX/pytorch-auto-drive

Performance

TuSimple performance (best):

method	backbone	accuracy	FP	FN
BezierLaneNet	resnet18	95.41	5.30	4.60
BezierLaneNet	resnet34	95.65	5.10	3.90
Ours	resnet18	95.61	5.30	3.50
Ours	resnet34	95.87	5.00	3.40

CULane performance (best):

method	backbone	F1
BezierLaneNet	resnet18	73.67
BezierLaneNet	resnet34	75.57
Ours	resnet18	74.59
Ours	resnet34	76.21

LLAMAS performance (best):

method	backbone	F1
BezierLaneNet	resnet18	94.91
BezierLaneNet	resnet34	95.17
Ours	resnet18	95.15
Ours	resnet34	95.30

CurveLanes performance (best):

method	backbone	F1
BezierLaneNet	resnet	74.56
Ours	resnet	75.03

Citation

@inproceedings{BezierLaneNet,
  title={Rethinking efficient lane detection via curve modeling},
  author={Feng, Zhengyang and Guo, Shaohua and Tan, Xin and Xu, Ke and Wang, Min and Ma, Lizhuang},
  booktitle={Computer Vision and Pattern Recognition},
  year={2022}
}
@inproceedings{culane,
  author    = {Xingang Pan and
               Jianping Shi and
               Ping Luo and
               Xiaogang Wang and
               Xiaoou Tang},
  title     = {Spatial as Deep: Spatial {CNN} for Traffic Scene Understanding},
  booktitle = {{AAAI}},
  pages     = {7276--7283},
  year      = {2018}
}
@misc{tusimple,
   author = {TuSimple},
   title = {Tusimple benchmark.},
   howpublished = {\url{https://github.com/TuSimple/tusimple-benchmark/}},
   year={Accessed September, 2020.}
}
@inproceedings{llamas,
  title={Unsupervised Labeled Lane Markers Using Maps},
  author={Behrendt Karsten and Soussan Ryan},
  booktitle={ICCVW},
  pages={832--839},
  year={2019}
}
@inproceedings{CurveLanes,
  author    = {Hang Xu and
               Shaoju Wang and
               Xinyue Cai and
               Wei Zhang and
               Xiaodan Liang and
               Zhenguo Li},
  title     = {CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending},
  booktitle = {ECCV},
  pages     = {689--704},
  year      = {2020}
}