LaneNet lane detection in Pytorch

LaneNet is a segmentation-tasked lane detection algorithm, described in [1] "Towards end-to-end lane detection: an instance segmentation approach" . The key idea of instance segmentation should be referred to [2] "Semantic instance segmentation with a discriminative loss function". This repository contains a re-implementation in Pytorch.

News

• Codebase would be updated these days. There are many bugs currently in this repo. Sorry for the late response.

Data preparation

CULane

The dataset is available in CULane. Please download and unzip the files in one folder, which later is represented as CULane_path. Then modify the path of CULane_path in config.py.

CULane_path
├── driver_100_30frame
├── driver_161_90frame
├── driver_182_30frame
├── driver_193_90frame
├── driver_23_30frame
├── driver_37_30frame
├── laneseg_label_w16
├── laneseg_label_w16_test
└── list

Note: absolute path is encouraged.

Tusimple

The dataset is available in here. Please download and unzip the files in one folder, which later is represented as Tusimple_path. Then modify the path of Tusimple_path in config.py.

Tusimple_path
├── clips
├── label_data_0313.json
├── label_data_0531.json
├── label_data_0601.json
└── test_label.json

Note: seg_label images and gt.txt, as in CULane dataset format, will be generated the first time Tusimple object is instantiated. It may take time.

Demo Test

For single image demo test:

python demo_test.py -i demo/demo.jpg 
                    -w path/to/weight
                    -b 1.5
                    [--visualize / -v]

An untested model can be downloaded [here]. (It will be uploaded soon.)

Train

1. Specify an experiment directory, e.g. experiments/exp0. Assign the path to variable exp_dir in train.py.

2. Modify the hyperparameters in experiments/exp0/cfg.json.

3. Start training:

   python train.py [-r]

4. Monitor on tensorboard:

   tensorboard --logdir='experiments/exp0' > experiments/exp0/board.log 2>&1 &
   

Note

• My model is trained with torch.nn.DataParallel. Modify it according to your hardware configuration.

Reference

[1]. Neven, Davy, et al. "Towards end-to-end lane detection: an instance segmentation approach." 2018 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2018.

[2]. De Brabandere, Bert, Davy Neven, and Luc Van Gool. "Semantic instance segmentation with a discriminative loss function." arXiv preprint arXiv:1708.02551 (2017).