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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Background
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“always appear on the pavement during the use of the road [2].”
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Background
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“[7], it is crucial to visually inspect the building elements to detect cracking and appraise the physical and functional conditions.”
Background
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“This approach has gained popularity due to its effectiveness in detecting cracks in concrete structures [37]. thoroughly examined current methodologies for computer vision-based identification of defects and evaluation of conditions in civil infrastructures constructed with concrete and asphalt.”
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“[20] provide a comprehensive review on the state of computer vision in concrete defect detection and open aspects.”
Background
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“Generally, computer-based pothole detection can be divided into 3D reconstruction-based and 2D vision-based approaches [3].”
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Automated crack detection and crack depth prediction for reinforced concrete structures using deep learning
Laxman, K. C., Tabassum, N., Ai, L. M., Cole, C. A., & Ziehl, P. H. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.130709 · Cited by 173
“Concrete structures degrade over time due to harsh environmental conditions, overloading, and material deterioration [1 – 3].”
Background
Deep Active Learning for Civil Infrastructure Defect Detection and Classification
Feng, C., Liu, M.-Y., Kao, C.-C., & Lee, T. (2017) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784480823.036 · Cited by 164
“The readers are referred to some excellent literature reviews for a comprehensive understanding of existing conventional methods that usually requires explicit feature engineering (Koch et al. 2015; Yao et al. 2014).”
Background
A real-time automatic pavement crack and pothole recognition system for mobile Android-based devices
Tedeschi, A., & Benedetto, F. (2017) · Advanced Engineering Informatics · doi:10.1016/j.aei.2016.12.004 · Cited by 152
Road Surface 3D Reconstruction Based on Dense Subpixel Disparity Map Estimation
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“It also allows the government to allocate the limited resources for maintenance and appraise long-term investment schemes [1].”
Background
Multi-classifier for reinforced concrete bridge defects
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Increasing the robustness of material-specific deep learning models for crack detection across different materials
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Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources
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Automated Visual Defect Classification for Flat Steel Surface: A Survey
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“Gradually, researchers began to focus on specific planar materials, such as asphalt pavement [22], [23], fabric [24]–[26], timber [27], and semiconductors [28].”
Background
Pavement crack image acquisition methods and crack extraction algorithms: A review
Wang, W., Wang, M., Li, H., Zhao, H., Kevin, W., He, C., Wang, J., Zheng, S., & Chen, J. (2019) · Journal of Traffic and Transportation Engineering (English Edition) · doi:10.1016/j.jtte.2019.10.001 · Cited by 145
Computer vision for road imaging and pothole detection: a state-of-the-art review of systems and algorithms
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“1, such approaches generally have a four-stage pipeline: (1) image pre-processing, (2) image segmentation, (3) damaged area extraction, and (4) detection result post-processing [9].”
BackgroundInfluential
Image Processing–Based Classification of Asphalt Pavement Cracks Using Support Vector Machine Optimized by Artificial Bee Colony
Hoang, N., Nguyen, Q.-L., & Bui, D. T. (2018) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000781 · Cited by 141
Machine learning algorithms for monitoring pavement performance
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Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance
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Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features
Wei-dong, S., Jia, G., Zhu, H., Jia, D., & Gao, L. (2020) · Journal of Advanced Transportation · doi:10.1155/2020/6412562 · Cited by 141
“Pavement crack detection plays an important role in the field of road distress evaluation [1].”
Background
Vision-Based Autonomous Crack Detection of Concrete Structures Using a Fully Convolutional Encoder–Decoder Network
Islam, M. M. M., & Kim, J.-M. (2019) · Sensors · doi:10.3390/s19194251 · Cited by 142
Road pothole extraction and safety evaluation by integration of point cloud and images derived from mobile mapping sensors
Wu, H., Yao, L., Xu, Z., Li, Y., Ao, X., Chen, Q., Li, Z., & Meng, B. (2019) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.100936 · Cited by 135
Crack detection using fusion features‐based broad learning system and image processing
Zhang, Y., & Yuen, K. (2021) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12753 · Cited by 134
“As the advantages of low cost, high quality, non-contact, and portable visual sensor, the application of computer vision in civil engineering has achieved exponential growth (D. Feng & Feng, 2018; Koch et al., 2015; Yeum & Dyke, 2015).”
Background
Design of a New Vision-Based Method for the Bolts Looseness Detection in Flange Connections
Wang, C., Wang, N., Ho, S.-C., Chen, X., & Song, G. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2899555 · Cited by 133
“Computer vision-based structural health monitoring as a research area has started attracting the attention of the researchers from all around the world [10], [11], [12].”
Road Damage Detection Based on Unsupervised Disparity Map Segmentation
Fan, R., & Liu, M. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2947206 · Cited by 130
“On the other hand, computer vision-based methods can recognize road damage with low computational complexity, but the achieved detection accuracy is still far from satisfactory [4].”
BackgroundMethodInfluential
Vision and Deep Learning-Based Algorithms to Detect and Quantify Cracks on Concrete Surfaces from UAV Videos
Bhowmick, S., Nagarajaiah, S., & Veeraraghavan, A. (2020) · Italian National Conference on Sensors · doi:10.3390/s20216299 · Cited by 115
“[2] present a review of computer vision algorithms that have been used for damage detection and condition assessment of civil infrastructure.”
Method
MaDnet: multi-task semantic segmentation of multiple types of structural materials and damage in images of civil infrastructure
Hoskere, V., Narazaki, Y., Hoang, T. A., & Spencer, B. F. (2020) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-020-00409-0 · Cited by 130
“In the United States, current practice for assessing structural health is predominantly reliant on manual visual inspections [1].”
Background
Imaging-based crack detection on concrete surfaces using You Only Look Once network
Deng, J., Lü, Y., & Lee, V. C. S. (2020) · Structural Health Monitoring · doi:10.1177/1475921720938486 · Cited by 128
“Inspections of these prevalent systems allow agencies to predict future conditions, to support investment planning and to allocate their limited maintenance and repair resources.(3) Several incidents have been directly related to insufficient inspection and condition assessment of concrete structures.”
Background
Construction and maintenance of urban underground infrastructure with digital technologies
Wang, M., & Yin, X. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104464 · Cited by 138
Machine vision-based surface crack analysis for transportation infrastructure
Hu, W., Wang, W., Ai, C., Wang, J., Wang, W., Meng, X., Liu, J., Tao, H., & Qiu, S. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103973 · Cited by 123
DefectTR: End-to-end defect detection for sewage networks using a transformer
Dang, L. M., Wang, H., Li, Y., Nguyen, T. N., & Moon, H. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2022.126584 · Cited by 121
Automated robotic monitoring and inspection of steel structures and bridges
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“Along with the development of climbing robots, computer-vision based methods for crack detection of concrete have been extensively researched and gradually employed in the field to assist with the reduction of costs and hazards of manual data collection.(20) Previously, J.”
Method
Postdisaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks
Pan, X., & Yang, T. Y. (2020) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12549 · Cited by 123
Surface defect detection of civil structures using images: Review from data perspective
Guo, J., Liu, P., Xiao, B., Deng, L., & Wang, Q. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105186 · Cited by 121
Deep learning-based roadway crack classification using laser-scanned range images: A comparative study on hyperparameter selection
Zhou, S., & Song, W. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103171 · Cited by 117
Tunnel lining detection and retrofitting
Jiang, Y., Wang, L., Zhang, B., Dai, X., Ye, J., Sun, B., Liu, N., Wang, Z., & Zhao, Y. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104881 · Cited by 118
Mapping computer vision research in construction: Developments, knowledge gaps and implications for research
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Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms
Fan, R., Wang, H., Wang, Y., Liu, M., & Pitas, I. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3112316 · Cited by 124
“Moreover, manual road pothole detection results are qualitative and subjective as they depend entirely on individual inspectors’ experience [5].”
Background
Application of image technology on pavement distress detection: A review
Du, Z., Yuan, J., Xiao, F., & Hettiarachchi, C. (2021) · Measurement · doi:10.1016/j.measurement.2021.109900 · Cited by 120
Dual attention deep learning network for automatic steel surface defect segmentation
Pan, Y., & Zhang, L. (2021) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12792 · Cited by 115
“) on infrastructure elements, such as buildings, bridges, tunnels, roads, sewer pipes, and others (Koch et al., 2015).”
Background
Review of image-based analysis and applications in construction
Mostafa, K., & Hegazy, T. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103516 · Cited by 116

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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