有监督学习下的市域铁路隧道结构裂缝边缘识别方法

Kun Zhang; Dingfang Chen; Mingxin Gao; Yongliang Huang; Yang Liu; Yanqing Men

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

有监督学习下的市域铁路隧道结构裂缝边缘识别方法

Translated title of the contribution: A Detecting Method for Structure Crack Edges of Municipal Railway Tunnels under Supervised Learning

Kun Zhang
, Dingfang Chen
, Mingxin Gao
, Yongliang Huang
, Yang Liu
, Yanqing Men

Wuhan University of Technology
LTD.
School of Transportation Science and Engineering, Harbin Institute of Technology
Jinan Rail Transit Group Co., Ltd.

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

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.

Translated title of the contribution	A Detecting Method for Structure Crack Edges of Municipal Railway Tunnels under Supervised Learning
Original language	Chinese (Traditional)
Pages (from-to)	446-453
Number of pages	8
Journal	Zhongguo Jixie Gongcheng/China Mechanical Engineering
Volume	32
Issue number	4
DOIs	https://doi.org/10.3969/j.issn.1004-132X.2021.04.009
State	Published - 25 Feb 2021
Externally published	Yes

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Cite this

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title = "有监督学习下的市域铁路隧道结构裂缝边缘识别方法",

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.",

keywords = "Crack detection, Image processing, Municipal railway tunnel structure, Principal component analysis, Supervised learning",

author = "Kun Zhang and Dingfang Chen and Mingxin Gao and Yongliang Huang and Yang Liu and Yanqing Men",

year = "2021",

month = feb,

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doi = "10.3969/j.issn.1004-132X.2021.04.009",

language = "繁体中文",

volume = "32",

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journal = "Zhongguo Jixie Gongcheng/China Mechanical Engineering",

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T1 - 有监督学习下的市域铁路隧道结构裂缝边缘识别方法

AU - Zhang, Kun

AU - Chen, Dingfang

AU - Gao, Mingxin

AU - Huang, Yongliang

AU - Liu, Yang

AU - Men, Yanqing

PY - 2021/2/25

Y1 - 2021/2/25

N2 - 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.

AB - 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.

KW - Crack detection

KW - Image processing

KW - Municipal railway tunnel structure

KW - Principal component analysis

KW - Supervised learning

UR - https://www.scopus.com/pages/publications/85102703449

U2 - 10.3969/j.issn.1004-132X.2021.04.009

DO - 10.3969/j.issn.1004-132X.2021.04.009

M3 - 文章

AN - SCOPUS:85102703449

SN - 1004-132X

VL - 32

SP - 446

EP - 453

JO - Zhongguo Jixie Gongcheng/China Mechanical Engineering

JF - Zhongguo Jixie Gongcheng/China Mechanical Engineering

IS - 4

ER -

有监督学习下的市域铁路隧道结构裂缝边缘识别方法

Abstract

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