  Road detection from the perspective of moving vehicles is a challenging issue
in autonomous driving. Recently, many deep learning methods spring up for this
task because they can extract high-level local features to find road regions
from raw RGB data, such as Convolutional Neural Networks (CNN) and Fully
Convolutional Networks (FCN). However, how to detect the boundary of road
accurately is still an intractable problem. In this paper, we propose a
siamesed fully convolutional networks (named as ``s-FCN-loc''), which is able
to consider RGB-channel images, semantic contours and location priors
simultaneously to segment road region elaborately. To be specific, the
s-FCN-loc has two streams to process the original RGB images and contour maps
respectively. At the same time, the location prior is directly appended to the
siamesed FCN to promote the final detection performance. Our contributions are
threefold: (1) An s-FCN-loc is proposed that learns more discriminative
features of road boundaries than the original FCN to detect more accurate road
regions; (2) Location prior is viewed as a type of feature map and directly
appended to the final feature map in s-FCN-loc to promote the detection
performance effectively, which is easier than other traditional methods, namely
different priors for different inputs (image patches); (3) The convergent speed
of training s-FCN-loc model is 30\% faster than the original FCN, because of
the guidance of highly structured contours. The proposed approach is evaluated
on KITTI Road Detection Benchmark and One-Class Road Detection Dataset, and
achieves a competitive result with state of the arts.
