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一个基于模糊C-Means集群和特征融合的桥梁裂细分算法

Yadong Yao^1,2, Yurui Zhang^1,2, Zai Liu^1,2

¹Institute of Transportation, Inner Mongolia University, Hohhot 010070, China.

Sensors (Basel, Switzerland)

|July 30, 2025

概括

本研究引入了一种新的无监督算法,用于使用模糊的C-means集群和特征融合进行桥梁裂细分. 它准确地检测到图像中的裂,克服了传统和深度学习方法的局限性.

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
图像处理图像处理

背景情况:

传统的用于裂检测的图像处理受噪声敏感性和值依赖性影响.
深度学习方法需要大量的标记数据,这构成了重大挑战.
现有的方法在杂的环境中扎着微小的裂和错误判断.

研究的目的:

开发一种新的,高效的,无监督的裂细分算法,用于检测桥梁损坏.
克服传统和深度学习方法在裂检测方面的局限性.
在结构健康监测中提高裂纹细分的准确性和实时效率.

主要方法:

利用模糊的C-means (FCM) 集群,在3D特征空间 (B频道像素) 中使用c=3进行初步细分.
采用连接域标签和循环值来区分线性裂和噪声.
实施了基于裂像素振幅的5x5社区搜索策略,以恢复碎片化裂连续性.

主要成果:

在Concrete Crack和SDNET2018数据集上实现了0.885的准确率和0.891的回忆率.
在性能方面超过了DeepLabv3+算法4.2%.
证明了实时效率,每张图像的处理时间为0.8秒.

结论:

拟议的算法为桥梁裂检测提供了一个高效的无监督解决方案.
它有效地解决了在噪音条件下错过的细裂和错误判断的破裂裂等挑战.
几何特征和像素分布特征的整合提高了检测准确性和稳定性.

关键词:

桥梁裂检测检测桥梁裂检测循环性值是指循环性的值.连接的域名标签连接的域名标签模糊的C-意味着集群.多功能的聚变聚变.没有监督的检测检测.

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