MFENet: Multi-level feature enhancement network for real-time semantic segmentation

Abstract

Accuracy is of vital importance for real-time semantic segmentation. However, modern methods often weaken high-level or low-level feature extraction to promote inference speed, thereby resulting in poor accuracy. In this paper, we present a Multi-level Feature Enhancement Network (MFENet) to enhance the feature extraction of each level in backbone. This approach can achieve high performance while maintaining high inference speed. We first rely on a Spatial and Edge Extraction Module with the Laplace Operator to improve the edge information extraction of low-level features. Next, we design a Context Boost Module to increase the context information inside each object of high-level features. Finally, we introduce the Selective Refinement Module to selectively combine the information from these two modules. Our network attained precise real-time segmentation results on Cityscapes, CamVid and COCO-Stuff datasets. More specifically, the architecture achieved 76.7% Mean IoU on the Cityscapes test dataset with 12.5 GFLOPS and a speed of 47 FPS on one NVIDIA Titan Xp card, which is more accurate than existing real-time methods.