Ultra-Fast-Lane-Detection

PyTorch 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.

Caffe model and prototxt can be found here.

Demo

Install

Please see INSTALL.md

Get started

First of all, please modify data_root and log_path in your configs/culane.py or configs/tusimple.py config according to your environment.

• data_root is the path of your CULane dataset or Tusimple dataset.
• log_path is where tensorboard logs, trained models and code backup are stored. It should be placed outside of this project.

---

For gpu training, run

python -train.py --params configs/culane.yaml

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)

自己训练了一个，mobilenetv2骨干网络，输入size 256512，Fmeasure 66.19% 较resnet18 288800分辨率精度降低3个点，速度在板卡上快了3-4倍

For evaluation, run

mkdir tmp
# This a bad example, you should put the temp files outside the project.

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.

Visualization

We provide a script to visualize the detection results. Run the following commands to visualize on the testing set of CULane and Tusimple.

python demo.py configs/culane.py --test_model path_to_culane_18.pth
# or
python demo.py configs/tusimple.py --test_model path_to_tusimple_18.pth

Since the testing set of Tusimple is not ordered, the visualized video might look bad and we do not recommend doing this.

Speed

To test the runtime, please run

python speed_simple.py  
# this will test the speed with a simple protocol and requires no additional dependencies

python speed_real.py
# this will test the speed with real video or camera input

It will loop 100 times and calculate the average runtime and fps in your environment.

Citation

@InProceedings{qin2020ultra,
author = {Qin, Zequn and Wang, Huanyu and Li, Xi},
title = {Ultra Fast Structure-aware Deep Lane Detection},
booktitle = {The European Conference on Computer Vision (ECCV)},
year = {2020}
}

Thanks

Thanks zchrissirhcz for the contribution to the compile tool of CULane, KopiSoftware for contributing to the speed test, and ustclbh for testing on the Windows platform.