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CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution

This is the official implementation code of the paper "CondLaneNet: a Top-to-down Lane Detection Framework Based on ConditionalConvolution". (Link: https://arxiv.org/abs/2105.05003) We achieve state-of-the-art performance on multiple lane detection benchmarks. Our paper has been accepted by ICCV2021.

Architecture,

Installation

This implementation is based on mmdetection(v2.0.0). Please refer to install.md for installation.

Datasets

We conducted experiments on CurveLanes, CULane and TuSimple. Please refer to dataset.md for installation.

Models

For your convenience, we provide the following trained models on Curvelanes, CULane, and TuSimple datasets

ModelSpeedF1Link
curvelanes_small154FPS85.09download
curvelanes_medium109FPS85.92download
curvelanes_large48FPS86.10download
culane_small220FPS78.14download
culane_medium152FPS78.74download
culane_large58FPS79.48download
tusimple_small220FPS97.01download
tusimple_medium152FPS96.98download
tusimple_large58FPS97.24download

Testing

CurveLanes 1 Edit the "data_root" in the config file to your Curvelanes dataset path. For example, for the small version, open "configs/curvelanes/curvelanes_small_test.py" and set "data_root" to "[your-data-path]/curvelanes".

2 run the test script

cd [project-root]
python tools/condlanenet/curvelanes/test_curvelanes.py configs/condlanenet/curvelanes/curvelanes_small_test.py [model-path] --evaluate

If "--evaluate" is added, the evaluation results will be printed. If you want to save the visualization results, you can add "--show" and add "--show_dst" to specify the save path.

CULane

1 Edit the "data_root" in the config file to your CULane dataset path. For example,for the small version, you should open "configs/culane/culane_small_test.py" and set the "data_root" to "[your-data-path]/culane".

2 run the test script

cd [project-root]
python tools/condlanenet/culane/test_culane.py configs/condlanenet/culane/culane_small_test.py [model-path]

3 We use the official evaluation tools of SCNN to evaluate the results.

TuSimple

1 Edit the "data_root" in the config file to your TuSimple dataset path. For example,for the small version, you should open "configs/tusimple/tusimple_small_test.py" and set the "data_root" to "[your-data-path]/tuSimple".

2 run the test script

cd [project-root]
python tools/condlanenet/tusimple/test_tusimple.py configs/condlanenet/tusimple/tusimple_small_test.py [model-path]

3 We use the official evaluation tools of TuSimple to evaluate the results.

Speed Test

cd [project-root]
python tools/condlanenet/speed_test.py configs/condlanenet/culane/culane_small_test.py [model-path]

Training

For example, train CULane using 4 gpus:

cd [project-root]
CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29001 tools/dist_train.sh configs/condlanenet/culane/culane_small_train.py 4 --no-validate 

Results

CurveLanes

ModelF1SpeedGFLOPS
Small(ResNet-18)85.09154FPS10.3
Medium(ResNet-34)85.92109FPS19.7
Large(ResNet-101)86.1048FPS44.9

CULane

ModelF1SpeedGFLOPS
Small(ResNet-18)78.14220FPS10.2
Medium(ResNet-34)78.74152FPS19.6
Large(ResNet-101)79.4858FPS44.8

TuSimple

ModelF1SpeedGFLOPS
Small(ResNet-18)97.01220FPS10.2
Medium(ResNet-34)96.98152FPS19.6
Large(ResNet-101)97.2458FPS44.8

Visualization results

Results