The DOI matches its Crossref record; 106 of the 107 references it cites were found in a registry or an index.
A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure
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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
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Handbook of Image and Video Processing
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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
Research works printed but not linked: 5
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Cited by
Sorted by citations
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring
Spencer, B. F., Hoskere, V., & Narazaki, Y. (2019) · Engineering · doi:10.1016/j.eng.2018.11.030 · Cited by 1,273
“For further study on identification methods for some of these defects, Koch et al. [108] provided an excellent review of computer vision defect detection techniques developed prior to 2015, classi-fied based on the structure to which they are applied.”
MethodAutonomous concrete crack detection using deep fully convolutional neural network
Dung, C. V., & Anh, L. D. (2019) · Automation in Construction · doi:10.1016/j.autcon.2018.11.028 · Cited by 1,170
DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection
Zou, Q., Zhang, Z., Li, Q., Qi, X., Wang, Q., & Wang, S. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2878966 · Cited by 1,122
“, the road pavement [1], [2], the wall of nuclear power plants [3], the ceiling of tunnels [4], etc.”
BackgroundGopalakrishnan, K., Khaitan, S. K., Choudhary, A., & Agrawal, A. (2017) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2017.09.110 · Cited by 981
Metadata
- 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
- Venue
- Advanced Engineering Informatics
- Publisher
- Elsevier BV
- Published
- 2015-04
- Volume
- 29
- Issue
- 2
- Pages
- 196-210
- ISSN
- 1474-0346 (ISSN-L)
- 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
- 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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