Automatic Identification and Location of Tunnel Lining Cracks

The lining crack is common for the tunnel in the stage of operation, which has seriously influenced the service life and safety of tunnel engineering. It is a new trend to use computer vision to detect tunnel cracks over the past few years in China and foreign countries. By image processing technolo...

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Main Authors: Pengyu Wang, Shuhong Wang, Alipujiang Jierula
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Advances in Civil Engineering
Online Access:http://dx.doi.org/10.1155/2021/8846442
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spelling doaj-efe8d7aad6934f60866d31a18d90ace02021-04-05T00:01:50ZengHindawi LimitedAdvances in Civil Engineering1687-80942021-01-01202110.1155/2021/8846442Automatic Identification and Location of Tunnel Lining CracksPengyu Wang0Shuhong Wang1Alipujiang Jierula2School of Resources and Civil EngineeringSchool of Resources and Civil EngineeringSchool of Resources and Civil EngineeringThe lining crack is common for the tunnel in the stage of operation, which has seriously influenced the service life and safety of tunnel engineering. It is a new trend to use computer vision to detect tunnel cracks over the past few years in China and foreign countries. By image processing technology and intelligent algorithm, the computer has a hominine visual perception system which understands, analyzes, and determines input image information, thus recognizing and detecting specific objectives. However, the effect of image recognition for tunnel crack now cannot satisfy the demands of practical engineering. SSD algorithm has been used when analyzing features of lining surface image, while comparison analysis has been made from image recognition results, error rate, and running time. The results indicate that the SSD algorithm can accurately and rapidly detect and mark the position of the tunnel crack. The tunnel information obtained from image recognition is subsequently imported into the team independently developed software GeoSMA-3D, which is useful for determining tunnel grade.http://dx.doi.org/10.1155/2021/8846442
collection DOAJ
language English
format Article
sources DOAJ
author Pengyu Wang
Shuhong Wang
Alipujiang Jierula
spellingShingle Pengyu Wang
Shuhong Wang
Alipujiang Jierula
Automatic Identification and Location of Tunnel Lining Cracks
Advances in Civil Engineering
author_facet Pengyu Wang
Shuhong Wang
Alipujiang Jierula
author_sort Pengyu Wang
title Automatic Identification and Location of Tunnel Lining Cracks
title_short Automatic Identification and Location of Tunnel Lining Cracks
title_full Automatic Identification and Location of Tunnel Lining Cracks
title_fullStr Automatic Identification and Location of Tunnel Lining Cracks
title_full_unstemmed Automatic Identification and Location of Tunnel Lining Cracks
title_sort automatic identification and location of tunnel lining cracks
publisher Hindawi Limited
series Advances in Civil Engineering
issn 1687-8094
publishDate 2021-01-01
description The lining crack is common for the tunnel in the stage of operation, which has seriously influenced the service life and safety of tunnel engineering. It is a new trend to use computer vision to detect tunnel cracks over the past few years in China and foreign countries. By image processing technology and intelligent algorithm, the computer has a hominine visual perception system which understands, analyzes, and determines input image information, thus recognizing and detecting specific objectives. However, the effect of image recognition for tunnel crack now cannot satisfy the demands of practical engineering. SSD algorithm has been used when analyzing features of lining surface image, while comparison analysis has been made from image recognition results, error rate, and running time. The results indicate that the SSD algorithm can accurately and rapidly detect and mark the position of the tunnel crack. The tunnel information obtained from image recognition is subsequently imported into the team independently developed software GeoSMA-3D, which is useful for determining tunnel grade.
url http://dx.doi.org/10.1155/2021/8846442
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AT shuhongwang automaticidentificationandlocationoftunnelliningcracks
AT alipujiangjierula automaticidentificationandlocationoftunnelliningcracks
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