Efficient Resolution-preserving Network for Real-time Semantic Segmentation

Juan Zhao, Fuhao Zou, Ruixuan Li, Yufei Li, Kai Li

Abstract: Inference process of the semantic segmentation is a dense predictive task who requires heavy computational cost. However, the available computational resource is quite limited in many practical scenarios. Therefore, real-time semantic segmentation balancing both speed and accuracy is becoming particularly significant. In this paper, we propose a resolution-preserving framework for real-time semantic segmentation. A lightweight backbone network is employed to obtain high-level semantic features, which will be fully reused. A resolution-preserving module is designed to extract low-level spatial details while retaining high-resolution. We also introduce a feature refinement module to improve the performance. Experiments on Cityscapes dataset demonstrate that the proposed ERPNet achieves a balance between speed and accuracy. Especially, we obtain the results of 73.6% mIoU on the Cityscapes test set with the speed of 109.5 FPS on single GeForce GTX 2080 card.