公路工程2024,Vol.49Issue(6) :49-56.DOI:10.19782/j.cnki.1674-0610.2024.06.008

基于无人机图像技术和支持向量机(SVM)的桥梁裂缝自动识别系统

Automatic Bridge Crack Detection System Based on UAV Image Technology and Support Vector Machine(SVM)

唐永 么学春 王晨 高凤宇
公路工程2024,Vol.49Issue(6) :49-56.DOI:10.19782/j.cnki.1674-0610.2024.06.008

基于无人机图像技术和支持向量机(SVM)的桥梁裂缝自动识别系统

Automatic Bridge Crack Detection System Based on UAV Image Technology and Support Vector Machine(SVM)

唐永 1么学春 1王晨 2高凤宇2
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作者信息

  • 1. 中交建筑集团有限公司,北京 100022
  • 2. 华侨大学,福建厦门 362021
  • 折叠

摘要

为了及时有效地自动检测出有裂缝病害的桥梁部位并给出维护建议,提出了一个功能更完整的全过程桥梁裂缝检测系统.此系统使用LOG滤波器对无人机采集的图像预处理,基于SVM识别裂缝类型,根据像素计算裂缝长度和面积占比,通过形态学腐蚀计算裂缝的最大宽度,将裂缝分为横向裂缝、纵向裂缝和其他裂缝3类,并获取裂缝长度、裂缝最大宽度和裂缝面积占比3个重要参数,然后对桥梁健康状态进行自动评估,给出维护建议.使用无人机采集河南省焦平高速互通式立交桥段的裂缝图片对设计的系统进行验证,结果表明可以实现裂缝参数的自动计算,得到了 96%的总体分类精度,并能够将检测结果导出为.xls类型文件,作为历史数据集供后续使用.

Abstract

In order to automatically detect the bridge parts with cracks in a timely and effective manner and give maintenance suggestions,a more complete whole-process bridge crack detection system is proposed.This system uses LOG filter to preprocess the image collected by UAV,identifies the crack type based on SVM,calculates the crack length and area ratio according to the pixel,and calculates the maximum width of the crack by morphological corrosion.Finally,the cracks are divided into three categories:transverse cracks,longitudinal cracks and other cracks,and the three important parameters of crack length,crack maximum width and crack area ratio are obtained.Then the health status of the bridge is automatically evaluated and maintenance suggestions are given.The designed system is verified by using the crack pictures of Jiaoping high-speed interchange bridge section in Henan Province collected by UAV.The results show that the developed detection system can realize the automatic calculation of crack parameters,and the overall classification accuracy of 96%is obtained.The detection results can be exported as.xls type files,which can be used as historical data sets for subsequent use.

关键词

桥梁工程/裂缝检测/无人机图像处理/支持向量机

Key words

bridge engineering/crack detection/UAV image processing/support vector machine

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出版年

2024
公路工程
湖南省交通科学研究院

公路工程

CSTPCD
影响因子:0.942
ISSN:1674-0610
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