Cited by 1,065Infrastructure Maintenance and MonitoringIndustrial Vision Systems and Defect DetectionCivil and Structural Engineering Research

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A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

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Koch, Christian, Georgieva, Kristina, Kasireddy, Varun, Akinci, Burcu, Fieguth, Paul·2015·Advanced Engineering Informatics

DOI 10.1016/j.aei.2015.01.008

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Pattern Classification
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Image analysis and mathematical morphology
(1982) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(82)90078-8 · Cited by 8,152
Pattern Classification
Abe, S. (2001) · Springer eBooks · doi:10.1007/978-1-4471-0285-4 · Cited by 3,758
Morphological Image Analysis: Principles and Applications
(2000) · Sensor Review · doi:10.1108/sr.2000.08720cae.001 · Cited by 3,537
Photo tourism
Snavely, N., Seitz, S. M., & Szeliski, R. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141964 · Cited by 2,977
Morphological Image Analysis
Soille, P. (1999) · Springer eBooks · doi:10.1007/978-3-662-03939-7 · Cited by 1,981
Computer Vision
Stockman, G. C., & Shapiro, L. G. (2001) · Cited by 1,706
Handbook of Image and Video Processing
Bovik, A. C. (2005) · Elsevier eBooks · doi:10.1016/b978-0-12-119792-6.x5062-1 · Cited by 1,585
CrackTree: Automatic crack detection from pavement images
Zou, Q., Cao, Y., Li, Q., Mao, Q., & Wang, S. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.11.004 · Cited by 1,131

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T. Iseley, D.M. Abraham S. Gokhale, Intelligent sewer condition evaluation technologies, in: Proceedings of the International NO-DIG Conference, 1997, pp. 254–265.
P. Kohut, K. Holak, T. Uhl, Monitoring of civil engineering structures supported by vision system, in: European Workshop on Structural Health Monitoring, 2012, pp. 1575–1582.
Miyamoto, 2007, Automatic crack recognition system for concrete structures using image processing approach, Asian J. Inf. Technol., 6, 553
S.C. Radopoulou, I. Brilakis, Improving patch distress detection using vision tracking on video data, in: Proceedings of the 21st International Workshop on Intelligent Computing in Engineering, 2014.
G. Moussa, K. Hussain, A new technique for automatic detection and parameters estimation of pavement crack, in: 4th International Multi-Conference on Engineering Technology Innovation, IMETI 2011, 2011.

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Authors: Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., & Fieguth, P.
Affiliations: University of Nottingham (GB)
Ruhr University Bochum (DE)
Carnegie Mellon University (US)
University of Waterloo (CA)
Corresponding author: Koch, Christian
ORCID: Koch, Christian
Kasireddy, Varun
Akinci, Burcu
Fieguth, Paul
Venue: Advanced Engineering Informatics
Publisher: Elsevier BV
Published: 2015-04
Volume: 29
Issue: 2
Pages: 196-210
ISSN: 1474-0346 (ISSN-L)
DOI: 10.1016/j.aei.2015.01.008
OpenAlex: W2128880484
Cited by: 1,065
References: 107
Citations by year: 2026: 126 · 2025: 113 · 2024: 139 · 2023: 112 · 2022: 132 · 2021: 121 · 2020: 104 · 2019: 95 · 2018: 68 · 2017: 35 · 2016: 17 · 2015: 3
FWCI: 67.81
Type: journal article
Language: en
Update notices: erratum (2016-04-01)
Open access: green
doi.org
Licence: other-oa
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Advanced Engineering Informatics (publishedVersion)
Repository@Nottingham (University of Nottingham) (acceptedVersion, other-oa)
Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by-nc-nd)
Nottingham ePrints (University of Nottingham) (submittedVersion, cc-by-nc-nd)
Topics: Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
Physical Sciences › Engineering › Civil and Structural Engineering › Civil and Structural Engineering Research
Keywords: computer vision, defect detection, condition assessment, concrete infrastructure, asphalt pavement, reinforced concrete bridges, visual inspection, infrastructure safety

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A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

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