Supervised Generative Segmentation Network for road segmentation for all-day outdoor robot navigation.
Python 2.7.12
Numpy 1.13.1
Tensorflow 1.2.0
cuda 8.0
cudnn 5.1.10
OpenCV 2.4.13
(YOUR_PATH)/SGSN/datasets/train/X/images/
(YOUR_PATH)/SGSN/datasets/train/X/labels/
(YOUR_PATH)/SGSN/datasets/train/Y/images/
(YOUR_PATH)/SGSN/datasets/train/Y/labels/
(YOUR_PATH)/SGSN/datasets/test/X/images/
(YOUR_PATH)/SGSN/datasets/test/X/labels/
Please follow the links in the Download links.
To build training set, copy the training images and the labels (not label graphs) of dusk_sight, night_sight, rain_sight and sun_sight to X domain (./datasets/train/X/images/, ./datasets/train/X/labels), and copy the images and the labels of dusk_sight to Y domain (./datasets/train/Y/images/, ./datasets/train/Y/labels).
To build test set, copy the test images and the labels (not label graphs) of dusk_sight, night_sight, rain_sight and sun_sight to X domian (./datasets/test/X/images/, ./datasets/test/X/labels).
cd (YOUR_PATH)/SGSN/datasets
python2 ./copy_night.py
cd (YOUR_PATH)/SGSN
python2 ./train.py
cd (YOUR_PATH)/SGSN
python2 ./evaluate.py
The dataset for road segmentation for the outdoor images in any time.
A total of 6380 images of different time and weather conditions are contained in UAS, including 1399 samples captured at dusk, 2167 samples captured at night, 819 samples captured in the rain and 1995 samples captured in the sunshine. For each image, a precise binary semantic label is made by manual annotation.
https://pan.baidu.com/s/1IWSVKYBrYwxaRThPfDsDGg
@article{zhang2018road, title={Road segmentation for all-day outdoor robot navigation}, author={Zhang, Yuxiao and Chen, Haiqiang and He, Yiran and Ye, Mao and Cai, Xi and Zhang, Dan}, journal={Neurocomputing}, volume={314}, pages={316--325}, year={2018}, publisher={Elsevier} }