DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation

Gen Li (Sungkyunkwan University), Joongkyu Kim (Sungkyunkwan University)

Abstract
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 trade-off between accuracy and inference speed. In this paper, we propose a novel Depth-wise 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 post-processing, 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.

DOI
10.5244/C.33.186
https://dx.doi.org/10.5244/C.33.186

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