A Detecting Method for Structure Crack Edges of Municipal Railway  Tunnels under Supervised Learning

doi:10.3969/j.issn.1004-132X.2021.04.009

Abstract

Abstract: Regular inspection of crack diseases was the key to ensure the normal operation of metro tunnel structures. To address this issue, a method for crack detection of metro tunnel structures under supervised learning was proposed herein. Firstly, the images of the metro tunnel structures were obtained by a consumer digital camera. Secondly, the geometric feature vectors of the edge targets were constructed based on the preliminary detection results by Canny operator, and then the crack edges of metro tunnel structures were detected by using the geometric feature vectors of edge targets. Finally, the effectiveness of the proposed method was verified by the image data of the metro tunnel structures.

Key words: municipal railway tunnel structure, crack detection, supervised learning, image processing, principal component analysis

摘要： 裂缝病害的定期巡检工作是保证市域铁路隧道结构正常运营的关键。针对市域铁路隧道结构表观裂缝的有效识别问题，提出了有监督学习下的市域铁路隧道结构裂缝边缘识别方法。首先，利用消费级数码相机获取了市域铁路隧道结构表观状态的图像数据；然后，基于Canny算子的初步边缘目标检测结果，构造了边缘目标的几何特征矢量，并在此基础上提出了基于几何特征数据的裂缝边缘识别方法；最后，采用实际市域铁路隧道结构的表观图像数据验证了所提出方法的有效性。

关键词: 市域铁路隧道结构, 裂缝识别, 监督学习, 图像处理, 主成分分析

CLC Number:

U231.92

ZHANG Kun, CHEN Dingfang, GAO Mingxin, HUANG Yongliang, LIU Yang, MEN Yanqing.

A Detecting Method for Structure Crack Edges of Municipal Railway Tunnels under Supervised Learning

[J]. China Mechanical Engineering, DOI: 10.3969/j.issn.1004-132X.2021.04.009.

张琨, 陈定方, 高铭鑫, 黄永亮, 刘洋, 门燕青. [车辆工程与测控]有监督学习下的市域铁路隧道结构裂缝边缘识别方法[J]. 中国机械工程, DOI: 10.3969/j.issn.1004-132X.2021.04.009.

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