Scene Semantic Segmentation Based on Regional Self-attention

doi:10.3969/j.issn.1006-2475.2025.05.017

Abstract

Abstract: The ability of computers to understand complex road environments is greatly enhanced through semantic segmentation based on scene images. In this paper， we propose a novel segmentation method that leverages local self-attention to model the long-range dependencies of different semantic objects， thereby improving the feature representation of semantic objects. The proposed approach also employs an extended convolutional strategy to mitigate the grid effect and adapt to variations in the size of segmented objects. Additionally， we utilize channel attention to determine the relative importance of each feature channel. The efficacy of proposed method is validated on the CamSeq01 and CamVid dataset， and experimental results demonstrate that our approach significantly outperforms general models in terms of segmentation performance.

Key words: , scene semantic segmentation, regional self-correlation, dilate convolution, channel attention

CLC Number:

TP301.6

SHI Xianwei1, FAN Xin2. Scene Semantic Segmentation Based on Regional Self-attention[J]. Computer and Modernization, 2025, 0(05): 122-126.

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