DSNET: Accelerate Indoor Scene Semantic Segmentation

Abstract

In this paper, we address the problem of real-time image semantic segmentation for indoor scene. As for scene parsing, both accuracy and speed are equally important. However, most of existing methods mainly focus on improving accuracy rather than speed. How to find a balance between accuracy and speed is crucial for real-time semantic segmentation tasks. To tackle this problem, we propose a lightweight framework with depthwise dilation residual module and multi-scale information integration module. A single DSNet yields the performance of mIoU accuracy 32.12% on SUN RGB-D dataset and accuracy 26.32% on ADE20K dataset, which is the most challenge scene parsing dataset. Besides, our system yields real-time inference on a single NVIDIA GPU.