Fast real-time semantic segmentation for autonomous driving

Abstract

Semantic segmentation for autonomous driving has high-speed requirements, but the current semantic segmentation model requires a lot of computing resources and is very time-consuming, which makes it less suitable for autonomous driving. To solve this problem, we propose a fast real-time semantic segmentation network, which has a minimal amount of computing and can run in real-time, so it is very suitable for the self-driving field. The framework contains two essential modules, FPN of images and shallow Backbone. It ensures the efficiency of image multi-scale features and feature extraction, respectively. Experimental results on cityscapes show the effectiveness of our method.