Fast-DecoupledNet: An Improved Multi-branch Edge Enhanced Semantic Segmentation Network

Abstract

There are existing semantic segmentation methods that incorporate the idea of edge detection by using multi-branch networks to focus on edges and subjects separately, but there are a large number of unfocused aspects and limited improvement. In this paper, we suggest the Fast-DecoupledNet, a semantic segmentation network. We design Edge Feature Extractor to extract the target’s edge features more accurately, and the global features obtained by joint downsampling are computed to obtain the subject features and the final features. In addition, we employ a shallower ResNet as the backbone network to reduce computational complexity while ensuring computational accuracy. Our proposed methods achieve the state-of-the-art 72.59 F-score and 77.64% mIoU on the Deepglobe Land Cover Classification dataset.