Road Detection Evaluation
Jae Shin Yoon (KAIST), Kibeak Park (KAIST), Soonmin Hwang (KAIST), Namil Kim (KAIST), Yukyung Choi (KAIST), Francois Rameau (KAIST), In So Kweon (KAIST)
Road Detection Dataset
Road Detection Dataset
Input Classifier GMM VP GrowCut AlexNet CN24 FCN Ours GT
You can download all the databases including ground-truth in below. Each frame is composed with three channels. First channel is ground-truth. Second or third channels are original thermal image.
- download thermal images (Campus-#3547, City-#1307, Suburb-#1087)
- download baseline code
Evaluation Results
Evaluation Results
Related Datasets
Related Datasets
Related Works
Related Works
- [1] Recovering surface layout from an image, IJCV 2007.
- [2] Bradski. Self-supervised monocular road detection in desert terrain, RSS 2006.
- [3] Vanishing point detection for road detection, CVPR 2009.
- [4] A hierarchical approach for road detection, ICRA 2014.
- [5] Imagenet classification with deep convolutional neural networks, NIPS 2012.
- [6] Convolutional patch networks with spatial prior for road detection and urban scene understanding, VISAPP 2015.
- [7] Fully convolutional networks for semantic segmentation, CVPR 2015.
Citation
Citation
- When using our baseline algorithm in your research, we will be happy if you cite us:
@INPROCEEDINGS{7535507, author={J. S. Yoon and K. Park and S. Hwang and N. Kim and Y. Choi and F. Rameau and I. s. Kweon}, booktitle={2016 IEEE Intelligent Vehicles Symposium (IV)}, title={Thermal-infrared based drivable region detection}, year={2016}, pages={978-985}, month={June},}