SGNet: a fast and accurate semantic segmentation network based on semantic guidance

Abstract

ABSTRACT In this paper, we design a fast and accurate lightweight semantic segmentation method for mobile robots. Accurate semantic segmentation usually requires obtaining high-resolution feature maps with strong semantic representation. Among the widely utilized methods, dilated convolution is computationally intensive and the feature pyramid accuracy is not as accurate. Inspired by the Flow Alignment Module (FAM) proposed by SFNet, we propose a Semantic Guided Upsampling Module (SGUM) for learning semantic offsets between neighboring level feature maps to solve the semantic misalignment problem and effectively fuse high level semantic features into low-level high-resolution feature maps. The integration of the SGUM into the semantic segmentation network with different backbones shows better performance than other methods on various datasets, showing the effectiveness of the SGUM on semantic feature fusion as well as its generalizability.