  The rapid development of autonomous driving in recent years presents lots of
challenges for scene understanding. As an essential step towards scene
understanding, semantic segmentation thus received lots of attention in past
few years. Although deep learning based state-of-the-arts have achieved great
success in improving the segmentation accuracy, most of them suffer from an
inefficiency problem and can hardly applied to practical applications. In this
paper, we systematically analyze the computation cost of Convolutional Neural
Network(CNN) and found that the inefficiency of CNN is mainly caused by its
wide structure rather than the deep structure. In addition, the success of
pruning based model compression methods proved that there are many redundant
channels in CNN. Thus, we designed a very narrow while deep backbone network to
improve the efficiency of semantic segmentation. By casting our network to
FCN32 segmentation architecture, the basic structure of most segmentation
methods, we achieved 60.6\% mIoU on Cityscape val dataset with 54 frame per
seconds(FPS) on $1024\times2048$ inputs, which already outperforms one of the
earliest real time deep learning based segmentation methods: ENet.

Computer Vision and Pattern Recognition