Visual change detection on tunnel linings

We describe an automated system for detecting, localising, clustering and ranking visual changes on tunnel surfaces. The system is designed to provide assistance to expert human inspectors carrying out structural health monitoring and maintenance on ageing tunnel networks. A three-dimensional tunnel...

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Published in	Machine vision and applications Vol. 27; no. 3; pp. 319 - 330
Main Authors	Stent, Simon, Gherardi, Riccardo, Stenger, Björn, Soga, Kenichi, Cipolla, Roberto
Format	Journal Article
Language	English
Published	Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2016 Springer Nature B.V
Subjects	Change detection Clustering Communications Engineering Computer Science Devices Feature maps Ground truth Image detection Image Processing and Computer Vision Lighting Mathematical models Motion perception Networks Nuisance Parallax Pattern Recognition Ranking Special Issue Paper Structural health monitoring Three dimensional models Tunnel linings Tunnels (transportation) Vision systems Infrastructure inspection Structure from motion Structural health monitoring Tunnel inspection Change detection Anomaly detection
Online Access	Get full text
ISSN	0932-8092 1432-1769
DOI	10.1007/s00138-014-0648-8

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Abstract	We describe an automated system for detecting, localising, clustering and ranking visual changes on tunnel surfaces. The system is designed to provide assistance to expert human inspectors carrying out structural health monitoring and maintenance on ageing tunnel networks. A three-dimensional tunnel surface model is first recovered from a set of reference images using Structure from Motion techniques. New images are localised accurately within the model and changes are detected versus the reference images and model geometry. We formulate the problem of detecting changes probabilistically and evaluate the use of different feature maps and a novel geometric prior to achieve invariance to noise and nuisance sources such as parallax and lighting changes. A clustering and ranking method is proposed which efficiently presents detected changes and further improves the inspection efficiency. System performance is assessed on a real data set collected using a low-cost prototype capture device and labelled with ground truth. Results demonstrate that our system is a step towards higher frequency visual inspection at a reduced cost.
AbstractList	We describe an automated system for detecting, localising, clustering and ranking visual changes on tunnel surfaces. The system is designed to provide assistance to expert human inspectors carrying out structural health monitoring and maintenance on ageing tunnel networks. A three-dimensional tunnel surface model is first recovered from a set of reference images using Structure from Motion techniques. New images are localised accurately within the model and changes are detected versus the reference images and model geometry. We formulate the problem of detecting changes probabilistically and evaluate the use of different feature maps and a novel geometric prior to achieve invariance to noise and nuisance sources such as parallax and lighting changes. A clustering and ranking method is proposed which efficiently presents detected changes and further improves the inspection efficiency. System performance is assessed on a real data set collected using a low-cost prototype capture device and labelled with ground truth. Results demonstrate that our system is a step towards higher frequency visual inspection at a reduced cost.
Author	Gherardi, Riccardo Stent, Simon Stenger, Björn Soga, Kenichi Cipolla, Roberto
Author_xml	– sequence: 1 givenname: Simon surname: Stent fullname: Stent, Simon email: sais2@cam.ac.uk, sistent@cantab.net organization: Department of Engineering, University of Cambridge – sequence: 2 givenname: Riccardo surname: Gherardi fullname: Gherardi, Riccardo organization: Cambridge Research Laboratory, Toshiba Research Europe Ltd – sequence: 3 givenname: Björn surname: Stenger fullname: Stenger, Björn organization: Cambridge Research Laboratory, Toshiba Research Europe Ltd – sequence: 4 givenname: Kenichi surname: Soga fullname: Soga, Kenichi organization: Department of Engineering, University of Cambridge – sequence: 5 givenname: Roberto surname: Cipolla fullname: Cipolla, Roberto organization: Department of Engineering, University of Cambridge
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Cites_doi	10.1016/j.autcon.2006.05.003 10.1109/TIP.2004.838698 10.1109/CVPR.2007.383073 10.1007/s00138-011-0394-0 10.1109/LGRS.2009.2031686 10.1080/0143116031000139863 10.1109/34.1000236 10.1007/s00138-014-0648-8 10.2219/rtriqr.48.94 10.1007/s11263-007-0107-3 10.1109/CVPR.2013.25 10.1023/B:VISI.0000029664.99615.94 10.1109/ICIP.2009.5413902 10.1109/83.597272 10.1109/ICCV.2011.6126515 10.1007/978-3-642-33709-3_24 10.1007/s00138-009-0189-8 10.1109/OCEANSKOBE.2008.4531004
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StartPage	319
SubjectTerms	Change detection Clustering Communications Engineering Computer Science Devices Feature maps Ground truth Image detection Image Processing and Computer Vision Lighting Mathematical models Motion perception Networks Nuisance Parallax Pattern Recognition Ranking Special Issue Paper Structural health monitoring Three dimensional models Tunnel linings Tunnels (transportation) Vision systems
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Title	Visual change detection on tunnel linings
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