一个多阶段的特征聚合和结构意识网络,用于混凝土桥梁裂检测
Erhu Zhang1, Tao Jiang1, Jinghong Duan2
1Department of Information Science, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
一个新的网络,MFSA-Net,通过结合方形和条形卷积来有效检测混凝土桥梁裂,以捕捉线性结构和远程依赖. 这种方法提高了关键基础设施安全的精度和回忆.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 混凝土桥上的裂构成重大安全风险.
- 现有的检测方法与裂纹特征 (如细度,低对比度和背景噪音) 斗争.
- 传统的卷积方法缺乏捕获远程依赖性和有效抑制背景干扰的能力.
研究的目的:
- 提出一个新的网络,MFSA-Net,用于精确的像素级混凝土桥梁裂检测.
- 增强对线性裂结构和远程依赖性的感知.
- 提高裂纹检测系统的精度和稳定性.
主要方法:
- 开发了一个多阶段的特征聚合和结构意识网络 (MFSA-Net).
- 引入了一个结构意识的卷积块,结合了方形和条形卷积.
- 实现了一个特征注意力融合块,用于边缘利和特征融合.
- 来自细粒度细分的不同阶段的聚合特征.
主要成果:
- 在混凝土桥梁裂数据集上,MFSA-Net的平均精度为73.74%,回忆率为77.04%,F1得分为75.30%,IOU为60.48%.
- 与现有方法相比,表现出优越的性能.
- 在混凝土路面裂数据集上展示了适应性和最佳性能.
结论:
- 据了解,MFSA-Net有效地解决了混凝土桥梁裂检测方面的挑战.
- 拟议的网络架构增强了感知线性结构和远程依赖性的能力.
- 在各种基础设施场景中,MFSA-Net 显示出在裂检测方面具有显著的潜力.
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