Automatic Crack Detection using Mask R-CNN

In order to avoid possible failures and prevent damage in civil infrastructures, such as tunnels and bridges, inspection should be done on a regular basis. Cracks are one of the earliest indications of degradation, hence, their detection allows preventive measures to be taken to avoid further damage...

Full description

Saved in:
Bibliographic Details
Published in:2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) pp. 152 - 157
Main Authors: Attard, Leanne, Debono, Carl James, Valentino, Gianluca, Di Castro, Mario, Masi, Alessandro, Scibile, Luigi
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2019
Subjects:
crack detection
Feature extraction
Inspection
mask r-cnn
object detection
Pipelines
Signal processing
Surface cracks
Surface morphology
Training
vision-based inspection
ISSN:1849-2266
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract In order to avoid possible failures and prevent damage in civil infrastructures, such as tunnels and bridges, inspection should be done on a regular basis. Cracks are one of the earliest indications of degradation, hence, their detection allows preventive measures to be taken to avoid further damage. In this paper, we demonstrate that Mask R-CNN can be used to localize cracks on concrete surfaces and obtain their corresponding masks to aid extract other properties that are useful for inspection. Such a tool can help mitigate the drawbacks of manual inspection by automating crack detection, lowering time consumption in executing this task, reducing costs and increasing the safety of the personnel. To train Mask R-CNN for crack detection we built a groundtruth database of masks on images from a subset of a standard crack dataset. Tests on the trained model achieved a precision value of 93.94 % and a recall of 77.5 %.
AbstractList In order to avoid possible failures and prevent damage in civil infrastructures, such as tunnels and bridges, inspection should be done on a regular basis. Cracks are one of the earliest indications of degradation, hence, their detection allows preventive measures to be taken to avoid further damage. In this paper, we demonstrate that Mask R-CNN can be used to localize cracks on concrete surfaces and obtain their corresponding masks to aid extract other properties that are useful for inspection. Such a tool can help mitigate the drawbacks of manual inspection by automating crack detection, lowering time consumption in executing this task, reducing costs and increasing the safety of the personnel. To train Mask R-CNN for crack detection we built a groundtruth database of masks on images from a subset of a standard crack dataset. Tests on the trained model achieved a precision value of 93.94 % and a recall of 77.5 %.
Author Scibile, Luigi
Masi, Alessandro
Di Castro, Mario
Valentino, Gianluca
Debono, Carl James
Attard, Leanne
Author_xml – sequence: 1
  givenname: Leanne
  surname: Attard
  fullname: Attard, Leanne
  email: leanne.attard@um.edu.mt
  organization: University of Malta, Msida, Malta
– sequence: 2
  givenname: Carl James
  surname: Debono
  fullname: Debono, Carl James
  email: c.debono@ieee.org
  organization: University of Malta, Msida, Malta
– sequence: 3
  givenname: Gianluca
  surname: Valentino
  fullname: Valentino, Gianluca
  email: gianluca.valentino@um.edu.mt
  organization: University of Malta, Msida, Malta
– sequence: 4
  givenname: Mario
  surname: Di Castro
  fullname: Di Castro, Mario
  email: mario.di.castro@cern.ch
  organization: Survey, Mechatronics and Measurements group, CERN, Meyrin, Switzerland
– sequence: 5
  givenname: Alessandro
  surname: Masi
  fullname: Masi, Alessandro
  email: alessandro.masi@cern.ch
  organization: Survey, Mechatronics and Measurements group, CERN, Meyrin, Switzerland
– sequence: 6
  givenname: Luigi
  surname: Scibile
  fullname: Scibile, Luigi
  email: luigi.scibile@cern.ch
  organization: CERN, Meyrin, Switzerland
BookMark eNotz7tOwzAUgGGDQKIteQDEkhkp6Tm248sYhQKVSkFc5sqOj5FVmqAkHXh7Bjr92yf9c3bR9R0xdoNQIoJdrt9f65ID2tIYZRTaM5ZZbVBzgwIlVOdshkbagnOlrlg2jsmDNBVIbc2M3dXHqT-4KbV5M7h2n9_TRO2U-i4_jqn7yp_duM_fima7vWaX0X2PlJ26YJ8Pq4_mqdi8PK6belMkDmIqqIraYishOBUjEQbhAlEgbUI03ukIQQuvYlBeCa-NBeUh8KC1FzJUYsFu_91ERLufIR3c8Ls73Yk_K8ZEgQ
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ISPA.2019.8868619
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Xplore
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE/IET Electronic Library
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9781728131405
1728131405
EISSN 1849-2266
EndPage 157
ExternalDocumentID 8868619
Genre orig-research
GroupedDBID 6IE
6IL
ABLEC
ALMA_UNASSIGNED_HOLDINGS
CBEJK
IEGSK
RIE
RIL
ID FETCH-LOGICAL-i203t-e5f791c40da6ffee1d3adeede78df8ba7f0d73b6fd6b63b78906b0d2d77b34d53
IEDL.DBID RIE
ISICitedReferencesCount 58
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000865974800027&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Wed Jun 26 19:27:11 EDT 2024
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i203t-e5f791c40da6ffee1d3adeede78df8ba7f0d73b6fd6b63b78906b0d2d77b34d53
PageCount 6
ParticipantIDs ieee_primary_8868619
PublicationCentury 2000
PublicationDate 2019-Sept.
PublicationDateYYYYMMDD 2019-09-01
PublicationDate_xml – month: 09
  year: 2019
  text: 2019-Sept.
PublicationDecade 2010
PublicationTitle 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA)
PublicationTitleAbbrev ISPA
PublicationYear 2019
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssib048504798
ssib042470063
Score 1.9727635
Snippet In order to avoid possible failures and prevent damage in civil infrastructures, such as tunnels and bridges, inspection should be done on a regular basis....
SourceID ieee
SourceType Publisher
StartPage 152
SubjectTerms crack detection
Feature extraction
Inspection
mask r-cnn
object detection
Pipelines
Signal processing
Surface cracks
Surface morphology
Training
vision-based inspection
Title Automatic Crack Detection using Mask R-CNN
URI https://ieeexplore.ieee.org/document/8868619
WOSCitedRecordID wos000865974800027&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA61ePCk0opvcvAkxmY3zz2WalHQpfiA3koeEynCVtqtv99NWiuCF29JDknmARMy33yD0IUIhdPaApGcBsKbJzjRzBSERq4ty20wPrHrP6iy1ONxMWqhq00tDAAk8Blcx2HK5fuZW8avsp7WUsvI8bmllFzVan37Ds-5iuF2M9cikqfrdSIzo0Xv_nnUj1iu6Bxpn18NVVI8Ge7-7yZ7qPtTmIdHm5Czj1pQddBlf1nPEvMqHsyNe8c3UCeAVYUjqv0NP5rFO34ig7Lsotfh7cvgjqxbIJBpTllNQARVZI5Tb2QIAJlnxjdngNI-aGtUoF4xK4OXVjIbq1qlpT73SlnGvWAHqF3NKjhEOOTCZI36gxONSbixkoJwka8PrAsqP0KdKOfkY8VyMVmLePz38gnaiapcoa1OUbueL-EMbbvPerqYnyfTfAHm0Y5r
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA5FBT2ptOLbPXgSY7ObbJI9lmppsV2KVuit5DGRUthKu_X3u0lrRfDiLckhyTxgQuabbxC6TV1mpNSAOSMOs-oJjiVVGSaea0sz7ZQN7Pp9kedyPM6GNXS_rYUBgAA-gwc_DLl8Ozcr_1XWlJJL7jk-d1PGErKu1vr2HpYw4QPudi5TT58uN6nMmGTN3uuw5dFc3j3CTr9aqoSI0jn8312OUOOnNC8aboPOMapBUUd3rVU5D9yrUXuhzCx6hDJArIrI49rfo4FazqIX3M7zBnrrPI3aXbxpgoCnCaElhtSJLDaMWMWdA4gtVbY6A4S0TmolHLGCau4s15xqX9fKNbGJFUJTZlN6gnaKeQGnKHJJquLKAM5U6suY0pxAajxjH2jjRHKG6l7Oycea52KyEfH87-UbtN8dDfqTfi9_vkAHXq1r7NUl2ikXK7hCe-aznC4X18FMX4NWkbI
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2019+11th+International+Symposium+on+Image+and+Signal+Processing+and+Analysis+%28ISPA%29&rft.atitle=Automatic+Crack+Detection+using+Mask+R-CNN&rft.au=Attard%2C+Leanne&rft.au=Debono%2C+Carl+James&rft.au=Valentino%2C+Gianluca&rft.au=Di+Castro%2C+Mario&rft.date=2019-09-01&rft.pub=IEEE&rft.eissn=1849-2266&rft.spage=152&rft.epage=157&rft_id=info:doi/10.1109%2FISPA.2019.8868619&rft.externalDocID=8868619