  The recent years have witnessed great advances for semantic segmentation
using deep convolutional neural networks (DCNNs). However, a large number of
convolutional layers and feature channels lead to semantic segmentation as a
computationally heavy task, which is disadvantage to the scenario with limited
resources. In this paper, we design an efficient symmetric network, called
(ESNet), to address this problem. The whole network has nearly symmetric
architecture, which is mainly composed of a series of factorized convolution
unit (FCU) and its parallel counterparts (PFCU). On one hand, the FCU adopts a
widely-used 1D factorized convolution in residual layers. On the other hand,
the parallel version employs a transform-split-transform-merge strategy in the
designment of residual module, where the split branch adopts dilated
convolutions with different rate to enlarge receptive field. Our model has
nearly 1.6M parameters, and is able to be performed over 62 FPS on a single GTX
1080Ti GPU. The experiments demonstrate that our approach achieves
state-of-the-art results in terms of speed and accuracy trade-off for real-time
semantic segmentation on CityScapes dataset.
