海洋测绘2026，Vol.46Issue(3)：25-29,5.DOI:10.3969/j.issn.1671-3044.2026.03.006
基于注意力特征融合的海底管线语义分割方法
Semantic segmentation method for subsea pipelines based on attention feature fusion
陈超 1王豪巍 1王炎青 2詹燕红 1莫振铎1
作者信息
- 1. 中海油(天津)管道工程技术有限公司,天津 300452||天津市海底管道重点实验室,天津 300452||中海油能源发展股份有限公司海洋装备智造和运维重点实验室 天津 300452
- 2. 天津市海底管道重点实验室,天津 300452
- 折叠
摘要
Abstract
As a critical component of marine infrastructure,the high-precision automatic segmentation of subsea pipelines is essential for safety monitoring and intelligent inspection.However,challenges such as complex seabed backgrounds,slender and faint pipeline targets,and extreme class imbalance severely limit the performance of existing segmentation methods.To address these challenges,this paper proposes an enhanced U-Net architecture.By introducing the Spatial-Channel Fusion Module(SDFM)and Context-Guided Attention Fusion Module(CGAF),it synergistically enhances the perception and recovery capabilities of pipeline structures.SDFM jointly models spatial and channel dependencies during early feature extraction,strengthening responses in critical regions.CGAF dynamically integrates jump-connected features through a local-global dual-path attention mechanism,precisely suppressing background interference and improving boundary accuracy.Experimental results demonstrate that the proposed method significantly outperforms mainstream models such as DeepLabv3+,U-Net,and the YOLO series,fully validating its superiority and robustness in segmenting slender pipeline objects within complex underwater scenarios..关键词
海底管线分割/U型网络/特征融合/语义分割/侧扫声纳Key words
subsea pipeline segmentation/U-Net/feature fusion/semantic segmentation/side-scan sonar分类
天文与地球科学引用本文复制引用
陈超,王豪巍,王炎青,詹燕红,莫振铎..基于注意力特征融合的海底管线语义分割方法[J].海洋测绘,2026,46(3):25-29,5.
