  Real-time semantic segmentation, which can be visually understood as the
pixel-level classification task on the input image, currently has broad
application prospects, especially in the fast-developing fields of autonomous
driving and drone navigation. However, the huge burden of calculation together
with redundant parameters are still the obstacles to its technological
development. In this paper, we propose a Fast Bilateral Symmetrical Network
(FBSNet) to alleviate the above challenges. Specifically, FBSNet employs a
symmetrical encoder-decoder structure with two branches, semantic information
branch and spatial detail branch. The Semantic Information Branch (SIB) is the
main branch with semantic architecture to acquire the contextual information of
the input image and meanwhile acquire sufficient receptive field. While the
Spatial Detail Branch (SDB) is a shallow and simple network used to establish
local dependencies of each pixel for preserving details, which is essential for
restoring the original resolution during the decoding phase. Meanwhile, a
Feature Aggregation Module (FAM) is designed to effectively combine the output
of these two branches. Experimental results of Cityscapes and CamVid show that
the proposed FBSNet can strike a good balance between accuracy and efficiency.
Specifically, it obtains 70.9\% and 68.9\% mIoU along with the inference speed
of 90 fps and 120 fps on these two test datasets, respectively, with only 0.62
million parameters on a single RTX 2080Ti GPU. The code is available at
https://github.com/IVIPLab/FBSNet.
