多重注意力交叉扫描VM-UNet用于钢管道X射线接缺陷检测
Ting Zhang1, Dengwu Wang1, Shanwen Zhang1
1School of Computer Science, Xijing University, Xi'an, China.
PloS one
|February 6, 2026
概括
一个新的多重注意力交叉扫描VM-UNet (MACVM-UNet) 改进了X射线图像中的钢管接缺陷检测 (SPWDD). 这种方法增强了特征提取,用于精确的石油/天然气管道的非破坏性测试.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算机视觉和图像处理
- 非破坏性测试 不破坏性测试
背景情况:
- 钢管道接缺陷检测 (SPWDD) 对石油/天然气基础设施安全至关重要.
- 接缺陷的X射线图像 (WDXI) 由于复杂的形态和低对比度而存在挑战.
- 准确的SPWDD对于防止故障和确保运营完整性至关重要.
研究的目的:
- 为准确和高效的SPWDD开发一个先进的深度学习模型.
- 解决WDXI分析的挑战,包括特征提取和融合.
- 为钢管道的非破坏性测试提供强大的解决方案.
主要方法:
- 介绍了一种新的多重注意力交叉扫描VM-UNet (MACVM-UNet) 架构.
- 集成交叉扫描视觉状态空间模型 (CVSS) 进行本地和远程特征捕获.
- 利用频道注意力跳过连接 (CASC) 和多尺度注意力特征聚合 (MSAFA) 进行增强的特征融合.
主要成果:
- 与最先进的方法相比,MACVM-UNet模型在SPWDD中表现出卓越的性能.
- 实现了较高的指标:mAcc为86.03%,mPre为86.14%,mF1为84.6%.
- 保持低计算复杂度,训练时间为1.61小时.
结论:
- 拟议的MACVM-UNet为非破坏性的SPWDD提供了一个高效和可行的解决方案.
- 该模型有效地从不同尺寸的WDXI中提取全球-本地特征.
- 这一进步有助于提高油气管道基础设施的安全性和可靠性.
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