Precise Detection of Surface Defects in Bridge Cables Using Combined Point Cloud Distance and Dual-Threshold Angle Analysis

XIA Xiaohua; ZHU Yingshuo; QIU Fabo; CHEN Jian

doi:10.7652/xjtuxb202506016

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Precise Detection of Surface Defects in Bridge Cables Using Combined Point Cloud Distance and Dual-Threshold Angle Analysis

更新时间：2025-08-26

- Precise Detection of Surface Defects in Bridge Cables Using Combined Point Cloud Distance and Dual-Threshold Angle Analysis
- Journal of Xi’an Jiaotong University Vol. 59, Issue 6, Pages: 155-166(2025)
- 作者机构：
  
  长安大学道路施工技术与装备教育部重点实验室， 710064，西安
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202506016
  CLC： TP391
- Received：14 November 2024，
  
  Online First：24 February 2025，
  
  Published：10 June 2025
- 稿件说明：
移动端阅览
XIA Xiaohua, ZHU Yingshuo, QIU Fabo, et al. Precise Detection of Surface Defects in Bridge Cables Using Combined Point Cloud Distance and Dual-Threshold Angle Analysis[J]. Journal of Xi’an Jiaotong University, 2025, 59(6): 155-166.
DOI：

XIA Xiaohua, ZHU Yingshuo, QIU Fabo, et al. Precise Detection of Surface Defects in Bridge Cables Using Combined Point Cloud Distance and Dual-Threshold Angle Analysis[J]. Journal of Xi’an Jiaotong University, 2025, 59(6): 155-166. DOI： 10.7652/xjtuxb202506016.

摘要

针对点云分割算法应用于桥梁拉索表面缺陷检测时容易出现错分割和运算量大的问题，提出了一种结合点云距离和角度双阈值的桥梁拉索表面缺陷精确检测方法。首先，通过设置拉索表面点到拟合圆柱面的距离阈值，实现缺陷初步检测；然后，设计结合点云聚类和方框滤波的拉索表面缺陷感兴趣（ROI）区域提取方法，基于初步检测结果，通过改进欧式聚类算法和方框滤波，分割出ROI区域；最后，构建一种基于点云法向量和拟合圆柱径向向量夹角的缺陷边界检测方法，通过设置角度阈值，分割出ROI区域中的缺陷点，实现了拉索缺陷区域边界的精确检测。研究结果表明：相较于不进行ROI区域提取和不进行点云聚类的ROI区域提取方法，所提方法能够减少40%以上的点云数据量，缩短超过15%的检测时间；拉索表面缺陷检测的平均精确率、召回率分别为96.83%和94.42%，综合评价指标

分数达到95.58%，均优于对比的随机抽样一致算法和区域生长算法，验证了所提方法的有效性和先进性。

Abstract

To address the issues of mis-segmentation and high computational complexity in the application of point cloud segmentation algorithms to bridge cable surface defect detection

a precise detection method for bridge cable surface defects based on dual thresholds of point cloud distance and angle is proposed in this paper. First

a distance threshold was established between the cable surface points and the fitted cylindrical surface to facilitate preliminary defect detection. Then

a region of interest (ROI) extraction method for cable surface defects was designed

combining point cloud clustering and box filtering. Based on the preliminary detection results

the ROI was segmented using an improved Euclidean clustering algorithm coupled with box filtering. Finally

a defect boundary detection method was constructed based on the angle between the point cloud normal vector and the radial vector of the fitted cylinder. The defect points within the ROI were segmented by setting an angle threshold

enabling precise detection of the defect boundaries on the cable surface. Research results demonstrated that

compared with methods that do not perform ROI extraction or those that extract ROI without point cloud clustering

the proposed method reduced the point cloud data volume by more than 40%

thereby shortening the detection time by over 15%. The average precision and recall rates for cable surface defect detection reached 96.83% and 94.42%

respectively

with a

-score of 95.58%

outperforming the comparative random sample consensus algorithm and the region growing algorithm. This validates the effectiveness and advancement of the proposed method.

关键词

Keywords

references

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