  Road scene understanding is a critical component in an autonomous driving
system. Although the deep learning-based road scene segmentation can achieve
very high accuracy, its complexity is also very high for developing real-time
applications. It is challenging to design a neural net with high accuracy and
low computational complexity. To address this issue, we investigate the
advantages and disadvantages of several popular CNN architectures in terms of
speed, storage and segmentation accuracy. We start from the Fully Convolutional
Network (FCN) with VGG, and then we study ResNet and DenseNet. Through detailed
experiments, we pick up the favorable components from the existing
architectures and at the end, we construct a light-weight network architecture
based on the DenseNet. Our proposed network, called DSNet, demonstrates a
real-time testing (inferencing) ability (on the popular GPU platform) and it
maintains an accuracy comparable with most previous systems. We test our system
on several datasets including the challenging Cityscapes dataset (resolution of
1024x512) with an mIoU of about 69.1 % and runtime of 0.0147 second per image
on a single GTX 1080Ti. We also design a more accurate model but at the price
of a slower speed, which has an mIoU of about 72.6 % on the CamVid dataset.
