  As a pixel-level prediction task, semantic segmentation needs large
computational cost with enormous parameters to obtain high performance.
Recently, due to the increasing demand for autonomous systems and robots, it is
significant to make a tradeoff between accuracy and inference speed. In this
paper, we propose a novel Depthwise Asymmetric Bottleneck (DAB) module to
address this dilemma, which efficiently adopts depth-wise asymmetric
convolution and dilated convolution to build a bottleneck structure. Based on
the DAB module, we design a Depth-wise Asymmetric Bottleneck Network (DABNet)
especially for real-time semantic segmentation, which creates sufficient
receptive field and densely utilizes the contextual information. Experiments on
Cityscapes and CamVid datasets demonstrate that the proposed DABNet achieves a
balance between speed and precision. Specifically, without any pretrained model
and postprocessing, it achieves 70.1% Mean IoU on the Cityscapes test dataset
with only 0.76 million parameters and a speed of 104 FPS on a single GTX 1080Ti
card.
