wind222 / Codes-for-Lane-Detection

Codes for the CNN-based lane detection models

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Codes-for-Lane-Detection

Codes for the CNN-based lane detection models. (expected release date: 2018.11.20)

Timeline

I will release the torch code in 2018.11.20. Tensorflow and pytorch version of the model will be released thereafter.

Content

Prerequisites

Models

Preliminary results on CULane testing set (F1-measure):

Category SCNN-Torch SCNN-Tensorflow SCNN-Pytorch
Normal 90.6 80.0 --
Crowded 69.7 57.1 --
Night 66.1 50.8 --
No line 43.4 35.0 --
Shadow 66.9 45.6 --
Arrow 84.1 68.3 --
Dazzle light 58.5 48.7 --
Curve 64.4 51.6 --
Crossroad 1990 4480 --
Total 71.6 58.7 --

SCNN-Tensorflow

Progress (on-going, debug):

  • Define network architecture
  • Load pre-trained weights
  • Define dataloader (data augmentation [on-going])
  • Testing
  • Training
  • validation
  • using multiple GPUs
  • clean the codes and make them reproducible

Observations:

FP is big. The output of the lane existence prediction branch is always (1, 1, 1, 1).

Notes:

Please go to the Tensorflow-SCNN repo to see detailed instructions.

SCNN-Pytorch

Progress (on-going):

  • Define network architecture (VGG-16 + message passing)
  • Load pre-trained weights
  • Define dataloader (load images and labels + data augmentation)
  • Testing (generate probability maps + smoothing)
  • Training
  • validation
  • using multiple GPUs
  • clean the codes and make them reproducible

Datasets

TuSimple

The ground-truth labels of TuSimple testing set is now available at TuSimple. Please evaluate your pred.json using the labels and this script.

CULane

The whole dataset is available at CULane.

Others

Citation

If you use the codes, please cite the following publications:

@inproceedings{pan2018SCNN,  
  author = {Xingang Pan, Jianping Shi, Ping Luo, Xiaogang Wang, and Xiaoou Tang},  
  title = {Spatial As Deep: Spatial CNN for Traffic Scene Understanding},  
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},  
  month = {February},  
  year = {2018}  
}

Our paper working on lane detection will be available soon!

Acknowledgement

This repo is built upon SCNN and LaneNet

Contact

If you have any problems in reproducing the results, just raise an issue in this repo.

About

Codes for the CNN-based lane detection models

License:MIT License


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Language:Python 100.0%