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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“…being equipped with access for inspection, if inspectors rely solely on basic tools such as tapelines and protractors, detecting defects with irregular edges (e.g., leakage) or small areas (e.g., cracks) becomes challenging, resulting in unsatisfactory inspection ef ﬁ cacy (Koch et al., 2015).”
Background
Review on identify road potholes using image semantic segmentation for advance driver assistant system
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Method
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“that can produce consistent performance on a wide range of noise levels, lighting conditions, irregularly patterned background, structure surfaces, and camera setups [32].”
Background
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“To address this, several strategies have been proposed that use computer vision and image processing methods for the automatic detection of surface defects [9–11].”
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“The use of computer vision technology for identifying ﬂaws and evaluating the state of concrete and asphalt civil infrastructure was studied by Koch et al. (2015).”
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Implementation of Computer Vision Technique for Crack Monitoring in Concrete Structure
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“An overview of various image-based vibration measurement techniques and methodologies has been provided, along with details of the camera settings used [31–33].”
Method
Advancing smart transportation: A review of computer vision and photogrammetry in learning-based dimensional road pavement defect detection
Tafida, A., Alaloul, W. S., Zawawi, N. A. W. A., Musarat, M. A., & Sani, A. A. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100729 · Cited by 5
Impact and Failure Analysis of U-Shaped Concrete Containing Polyurethane Materials: Deep Learning and Digital Imaging Correlation-Based Approach
Laqsum, S. A., Zhu, H., Haruna, S. I., Ibrahim, Y. E., Amer, M., Al-shawafi, A., & Ahmed, O. S. (2025) · Polymers · doi:10.3390/polym17091245 · Cited by 5
“However, the performance of concrete can be significantly affected by harsh environmental conditions and when subjected to dynamic loads, resulting in structural cracks within stress regions [2–4].”
ROBUST DETECTION OF SURFACE ANOMALY USING LIDAR POINT CLOUD WITH INTENSITY
Ono, Y., Tsuji, A., Abe, J. W., Noguchi, H., & Abe, J. W. (2020) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2020-1129-2020 · Cited by 6
Characterising Penetrometer Tip Contact during Concrete Condition Assessment
Hall, R. N., Stumpf, A., Baji, A., Ross, R., & Barnett, D. (2022) · Sensors · doi:10.3390/s22030737 · Cited by 5
“The primary advantage of assessing the condition of a concrete surface by touch is that looks can sometimes be deceiving, particularly under variable lighting [25].”
Background
Deep Learning-Based Automatic Detection and Evaluation on Concrete Surface Bugholes
Wei, F., Shen, L., Xiang, Y., Zhang, X., Tang, Y., & Tan, Q. (2022) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2022.019082 · Cited by 5
Large-Scale Automated Sustainability Assessment of Infrastructure Projects Using Machine Learning Algorithms with Multisource Remote Sensing Data
Shamshirgaran, A., Nourzad, S. H. H., Keshtkar, H., Golabchi, M., & Sadeghi, M. (2022) · Journal of Infrastructure Systems · doi:10.1061/(asce)is.1943-555x.0000703 · Cited by 5
Science Mapping for Recent Research Regarding Urban Underground Infrastructure
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INVESTIGATION AND ANALYSIS OF CRACK DETECTION USING UAV AND CNN: A CASE STUDY OF HOSPITAL RAJA PERMAISURI BAINUN
Sheng, G. W., Din, W. I. S. W., Waseem, Q., & Zabidi, A. (2023) · International Journal of Computer Systems & Software Engineering · doi:10.15282/ijsecs.9.1.2023.2.0106 · Cited by 5
Algorithms for Vision-Based Quality Control of Circularly Symmetric Components
Brambilla, P., Conese, C., Fabris, D. M., Chiariotti, P., & Tarabini, M. (2023) · Sensors · doi:10.3390/s23052539 · Cited by 5
“Among these 84 documents, ﬁve are surveys and reviews [3,5–8].”
Background
Estimation of multiple crack propagation pattern in concrete using Voronoi tessellation method
Bayar, G., & Bilir, T. (2023) · Sadhana · doi:10.1007/s12046-023-02223-y · Cited by 5
“…some mathematical approaches (like integrated algorithms, morphological approach, percolation based method and practical technique, etc.) were also used to create image based crack detection system (ﬁgure 1), improve the detection results and increase the detection/ estimation accuracy [1, 2].”
Background
The influence of internally architected voids in the creation of high-strength, low-weight 3D-printed cobalt-chromium prototypes
Mohammed, A. J., & Al-Ali, A. A. (2024) · Dental Research Journal · doi:10.4103/drj.drj_627_23 · Cited by 5
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Tao, X., Zeng, Z., Zeng, J., Zhao, T., & Dai, Q. (2024) · International Journal of Cognitive Informatics and Natural Intelligence · doi:10.4018/ijcini.356363 · Cited by 5
“Automated pavement crack detection technology not only can improve detection efficiency and precision but also can significantly reduce maintenance costs and human errors (Koch et al., 2015).”
Scalable probabilistic deterioration model based on visual inspections and structural attributes from large networks of bridges
Fakhri, S. A. K., Hamida, Z., & Goulet, J.-A. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103035 · Cited by 5
CNN-Based Deep Architecture for Health Monitoring of Civil and Industrial Structures Using UAVs
Harweg, T., Peters, A., Bachmann, D., & Weichert, F. (2019) · International Electronic Conference on Sensors and Applications (ECSA) · doi:10.3390/ecsa-6-06640 · Cited by 5
“[8] gave a comprehensive overview of current practice in visual inspection and damage types of civil structures including associated severity scales.”
Background
Gradient-Based Markov Chain Monte Carlo for Bayesian Inference With Non-differentiable Priors
Goldman, J. V., Sell, T., & Singh, S. S. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1909600 · Cited by 5
“In mission-critical cases, such as calculating the probability of disease via MRIs, see for example [48], or defect detection of large-scale civil infrastructure via visual sensing technologies [36], it follows that end users will be interested in as exact sampling as possible to most accurately quantify the resulting…”
Background
Automatic tarmac crack identification application
Coca, L.-G., Cusmuliuc, C.-G., & Iftene, A. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.08.049 · Cited by 5
Image-based crack recognition of tunnel lining using residual U-Net convolutional neural network
Hou, S. K., Ou, Z., Qin, P. X., Wang, Y., & Liu, Y. (2021) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/861/7/072001 · Cited by 5
“The efficiency of this method is low, and the accuracy of crack identification depends on the experience and judgment of the inspector to a large extent [4].”
Method
A Novel Path Voting Algorithm for Surface Crack Detection
Yu, J., Chen, Z., & Xiong, Z. (2022) · Electronics · doi:10.3390/electronics11030501 · Cited by 5
Corrosion Assessment in Reinforced Concrete Structures by Means of Embedded Sensors and Multivariate Analysis—Part 1: Laboratory Validation
Ramón, J. E., Lliso-Ferrando, J. R., Martínez-Ibernón, A., & Gandía‐Romero, J. M. (2023) · Sensors · doi:10.3390/s23218869 · Cited by 5
“This is a complex task that is restricted to detecting damage to parts’ exteriors and depends on the subjectivity of the worker who makes the inspection [27,28].”
Background
Surface damage detection for concrete bridges using single-stage convolutional neural networks
Zhang, C., & Chang, C. C. (2019) · SPIE Health Monitoring of Structural and Biological Systems · doi:10.1117/12.2513571 · Cited by 5
“Once visual data is collected from a bridge, there is an entire suite of image processing techniques (IPTs) to extract relevant information for defect detection, classification, and assessment [5].”
BackgroundMethod
Influence of initial defects on the degradation of steel reinforced mortar exposed to cyclic wetting and drying environment based on 3D scanning
Zhang, X., Jiang, C., Chen, Y., Yuan, B., Memon, S. A., Ren, J., Xing, F., & Sun, H. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2022.126591 · Cited by 5
Brick wall efflorescence detection technology using target detection illustrated by Wuhan Gao’s Hospital
Luo, X., Wang, H., Qian, Y., Huang, W., Yan, Z., & Xie, G. (2025) · npj Heritage Science · doi:10.1038/s40494-025-01690-2 · Cited by 5
An improved cross-dimensional collaborative bridge defect detection model based on UAV images
Ni, J., Ji, Q., Zhao, Y., Cao, W., & Shi, P. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae09c0 · Cited by 4
Real-time robotic teleoperation for pavement pothole segmentation, quantification, and localization using multimodal sensing and efficient multi-scale attention-enhanced edge deep learning
Hu, X., & Assaad, R. H. (2026) · Automation in Construction · doi:10.1016/j.autcon.2026.106806 · Cited by 4
A UAV-deployable lightweight framework for real-time bridge crack detection via YOLO-LY algorithm
Yin, X., Wang, C., Chen, W., Zeng, Z., Quan, Y., & Huang, Z. (2026) · Engineering Structures · doi:10.1016/j.engstruct.2026.122629 · Cited by 4
A Decade of Artificial Intelligence in the Construction Industry: Trends, Applications, and Future Directions
Sholeh, M. N., Hatmoko, J. U. D., & Widoanindyawati, V. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10583-x · Cited by 4
“…activities, including design optimization, planning and scheduling, construction operations, quality and safety management, and operation and asset management. use of computer vision techniques, which facilitated auto - mated site monitoring, hazard detection, and quality inspec - tion [ 60– 62 ].”
Spall Repair Patch Health Monitoring System Using BIM and IoT
Kim, C., Cho, J., Kim, J., Song, Y., Kang, J. H., & Yeon, J. (2024) · Buildings · doi:10.3390/buildings14061589 · Cited by 5
Research on concrete structure defect repair based on three-dimensional printing
Gu, Y., Li, W., Yao, X., & Liu, G. (2024) · Frontiers of Structural and Civil Engineering · doi:10.1007/s11709-024-1088-9 · Cited by 4
Pixel-level bridge crack detection and 3D localization: An improved hybrid approach combining segmentation and 3D reconstruction
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Deep Neural Networks for Visual Bridge Inspections and Defect Visualisation in Civil Engineering
Bush, J., Corradi, T., Ninić, J., Thermou, G. E., & Bennetts, J. (2021) · Repository@Nottingham (University of Nottingham) · Cited by 4

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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