SG2Path: a two-branch semantic guided backbone network for semantic segmentation

Shuang Han; Hengyu Li; Jingyi Liu; Hongzhou Wang; Yueying Wang; Shaorong Xie

doi:10.1117/12.2675233

25 May 2023 SG2Path: a two-branch semantic guided backbone network for semantic segmentation

Shuang Han, Hengyu Li, Jingyi Liu, Hongzhou Wang, Yueying Wang, Shaorong Xie

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126362Q (2023) https://doi.org/10.1117/12.2675233
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Semantic segmentation is vital for computers to process image scene parsing. Semantic segmentation requires the output of high-resolution segmentation results that classify each pixel in an image, and high-resolution feature maps consume significant computational costs in deep learning networks. To trade-off the accuracy and speed of semantic segmentation models, researchers have proposed various different approaches to extract features by semantic segmentation backbone networks. In this paper, we propose a novel semantic-guided backbone network (SG2Path) based on a multi-branch backbone architecture, in which we design a semantic-guided upsampling module (SGUM) to better fuse high-resolution spatial features with low-resolution semantic features in different branches, which effectively solves the semantic misalignment problem between feature maps of different resolutions. Experiments on the Cityscapes dataset and the Visualization analysis of the model prove the advantages of our model in the semantic segmentation task and the significant application potential.

Citation Download Citation

Shuang Han, Hengyu Li, Jingyi Liu, Hongzhou Wang, Yueying Wang, and Shaorong Xie "SG2Path: a two-branch semantic guided backbone network for semantic segmentation", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126362Q (25 May 2023); https://doi.org/10.1117/12.2675233

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KEYWORDS

Semantics

Image segmentation

Convolution

Network architectures

Image classification

Spatial resolution

Feature fusion

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