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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Ibrahim, A., Osman, M. K., Yusof, N. A. M., Ahmad, K. A., Harun, N. H., & Raof, R. A. A. (2019) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v14.i2.pp810-818 · Cited by 22
“Cracking is one of the most common and important types of asphalt pavement distress [1-2].”
Background
Deep residual U-net with input of static structural responses for efficient U* load transfer path analysis
Zhao, S., Wu, N., & Wang, Q. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101184 · Cited by 22
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“Ingeneral, based on recent reviewing works performed by Koch et al. [20]; Feng and Feng [21]; Dong and Catbas [22]; and Yadhunath et al. [23], there is an increasing trend of applying image processing and machine learning for automatically detecting concrete surface distresses including spall.”
Background
Enhanced concrete crack detection and proactive safety warning based on I-ST-UNet model
Zhang, H., Ma, L., Yuan, Z., & Liu, H. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105612 · Cited by 21
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Classification of Imbalanced Data Using Deep Learning with Adding Noise
Fan, W.-W., & Lee, C. (2021) · Journal of Sensors · doi:10.1155/2021/1735386 · Cited by 23
Vision-based high-precision intelligent monitoring for shield tail clearance
Zeng, L., Shu, W., Liu, Z., Zou, X., Wang, S., Xia, J., Xu, C., Xiong, D., & Yang, Z. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.104088 · Cited by 21
Comparison of multimodal RGB-thermal fusion techniques for exterior wall multi-defect detection
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Ro, S. H., Li, Y., & Gong, J. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102427 · Cited by 23
Determination of Geometric Parameters of Cracks in Concrete by Image Processing
Vashpanov, Y., Son, J., Heo, G., Podousova, T., & Kim, Y. (2019) · Advances in Civil Engineering · doi:10.1155/2019/2398124 · Cited by 21
Toward Community Sensing of Road Anomalies Using Monocular Vision
Chen, H.-T., Lai, C.-Y., & Shih, C.-A. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2517194 · Cited by 21
“[2].”
GPU-Enabled Pavement Distress Image Classification in Real Time
Doycheva, K., Koch, C., & König, M. (2016) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000630 · Cited by 21
“Visual-based pavement assessment techniques have been widely applied recently, because they are less subjective and hazardous compared to manual observations [Koch et al. 2015].”
MethodSelf-citation
Next-Generation Building Condition Assessment: BIM and Neural Network Integration
Hosseini, M. A., Ravanshadnia, M., Rahimzadegan, M., & Ramezani, S. (2024) · Journal of Performance of Constructed Facilities · doi:10.1061/jpcfev.cfeng-4828 · Cited by 20
DSWMamba: A deep feature fusion mamba network for detection of asphalt pavement distress
Sun, P., Yang, L., Yang, H., Yan, B., Wu, T., & Li, J. (2025) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2025.140393 · Cited by 22
Parallel simulation and prediction techniques for digital twins in urban underground spaces
Gong, H., Su, D., Zeng, S., & Chen, X. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106212 · Cited by 21
A Two-Stage Crack Detection Method for Concrete Bridges Using Convolutional Neural Networks
Li, Y., Zhao, W., Zhang, X., & Qichen, Z. H. O. U. (2018) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2018edl8150 · Cited by 20
“A summary of crack detection approaches can be found in [1], in which approaches are roughly divided into pre-processing, feature-based, model-based, pattern-based and 3D reconstruction types.”
Background
Damage Analysis in Dual-Phase Steel Using Deep Learning: Transfer from Uniaxial to Biaxial Straining Conditions by Image Data Augmentation
Medghalchi, S., Kusche, C. F., Karimi, E., Kerzel, U., & Korte‐Kerzel, S. (2020) · JOM · doi:10.1007/s11837-020-04404-0 · Cited by 20
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“Likewise, we ﬁ nd vision-based algorithms being used in many engineering projects with applications to detect cracks in pavements, 18,19 road surfaces, 20,21 concrete surfaces, 22,23 concrete bridges, 24 and pothole detection.”
Method
Spatiotemporal matching method for tracking pavement distress using high‐frequency detection data
Pan, N., Liu, H., Wu, D., Liu, C., & Du, Y. (2022) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12947 · Cited by 22
“However, due to the lack of pavement performance evolution data with the continuous period and enough space coverage (Koch et al., 2015), there is no related application in road management systems.”
Background
TinyML-Based Pothole Detection: A Comparative Analysis of YOLO and FOMO Model Performance
Silva, J. D., Flores, T. K. S., Júnior, S., & da Silva, I. M. D. (2023) · IEEE Latin American Conference on Computational Intelligence (LA-CCI) · doi:10.1109/la-cci58595.2023.10409357 · Cited by 20
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“Second, automated data collection in which the development and deployment of computer vision algorithms for pavement engineering systems have expanded at an exponential rate [8].”
UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite
Guo, S., Li, J., Feng, Y., Zhou, D., Zhang, D., Chen, C., Su, S., Zhu, X., Chen, Q., & Fan, R. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3351209 · Cited by 22
“The first category of algorithms generally utilizes explicit image processing algorithms to segment road RGB or disparity/depth images [16].”
Background
The Citizen Engineer: Urban Infrastructure Monitoring via Crowd-Sourced Data Analytics
Harris, D. K., Alipour, M., Acton, S. T., Messeri, L. R., Vaccari, A., & Barnes, L. E. (2017) · Structures Congress 2017 · doi:10.1061/9780784480427.042 · Cited by 20
“Relatively limited literature on the application of computer vision to solve infrastructure problems exists (Brilakis et al 2011, German et al 2012, Koch et al 2015, Halfawy and Hengmeechai 2014, Guo et al 2009); however, the of principles of computer vision-based feature detection have been…”
Background
Low-Light Image Enhancement via Retinex-Style Decomposition of Denoised Deep Image Prior
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“With the great breakthrough of deep learning in the ﬁeld of computer vision technology, image processing has been widely used in many ﬁelds, e.g., face recognition [1], defect detection [2], medical image retrieval [3], trafﬁc information systems [4], and text recognition [5].”
Experimental and probabilistic analysis of the crack propagation in fiber reinforced concrete
Tarabin, M., Leblouba, M., Barakat, S. A., & Zahri, M. (2023) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2023.107388 · Cited by 19
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“Some machine learning algorithms, including the Support Vector Machine (SVM) (Koch et al .”
MethodInfluential
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“As part of the damage assessment process, object detection and tracking algorithms can also be used to identify major infrastructure items such as bridges and highways (Koch et al. 2015).”
Automated masonry spalling severity segmentation in historic railway tunnels using deep learning and a block face plane fitting approach
Smith, J., Paraskevopoulou, C., Cohn, A. G., Kromer, R., Bedi, A., & Invernici, M. (2024) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2024.106043 · Cited by 23
Crack SAM: enhancing crack detection utilizing foundation models and Detectron2 architecture
Rakshitha, R., Srinath, S., Kumar, N. V., Rashmi, S., & Poornima, B. V. (2024) · Journal of Infrastructure Preservation and Resilience · doi:10.1186/s43065-024-00103-1 · Cited by 19
Survey of automated crack detection methods for asphalt and concrete structures
Khlifati, O., Baba, K., & Tayeh, B. A. (2024) · Innovative Infrastructure Solutions · doi:10.1007/s41062-024-01733-w · Cited by 20
“Koch et al. [4] also conducted an assessment of vision-based approaches for detecting defects and evaluating the condition of civil infrastructures constructed from various materials.”
Bridge Inspection and Condition Assessment Using Image-Based Technologies with UAVs
Hiasa, S., Karaaslan, E., Shattenkirk, W., Mildner, C., & Çatbaş, F. N. (2018) · ASCE Structures Congress · doi:10.1061/9780784481332.020 · Cited by 19
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Yamaguchi, T., Shibuya, T., Kanda, M., & Yasojima, A. (2019) · Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) · doi:10.23919/sice.2019.8859876 · Cited by 19
An innovative approach for high-performance road pavement monitoring using black box
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Phung, M. D., Dinh, T. H., Hoang, V. T., & Ha, Q. (2017) · International Symposium on Automation and Robotics in Construction (ISARC) · doi:10.22260/isarc2017/0115 · Cited by 19
“For surface detection, computer-vision based techniques are widely used to detect cracks due to its robustness and cost eﬃciency [11].”
Method
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Jung, H. K., & Park, G. (2019) · Structural Health Monitoring · doi:10.1177/1475921718811157 · Cited by 19
Advanced cover glass defect detection and classification based on multi-DNN model
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Sarmiento, J.-A. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.06467 · Cited by 27
“Zhang et. al [33] used a low-cost smartphone to capture pavement images and applied a Deep ConvNet trained on square image patches for the classification of patches with and without cracks which outperformed SVM and Boosting methods in terms of F1-score [33].”
Influential
Segment anything model-based crack segmentation using low-rank adaption fine-tuning
Guo, Y., Xu, Y., Cui, H., Dang, M., & Li, S. (2024) · Structural Health Monitoring · doi:10.1177/14759217241261089 · Cited by 18
Bridge substructure damage morphology identification based on the underwater sonar point cloud data
Zhang, S., Zhu, Y., Xiong, W., Cai, C. S. S., & Zhang, J. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102936 · Cited by 18
Automatic detection of subsurface defects in concrete structures using state-of-the-art deep learning-based object detectors on the infrared dataset
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Building construction crack detection with BCCD YOLO enhanced feature fusion and attention mechanisms
Ren, W., & Zhong, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-05665-y · Cited by 18
IMU based adaptive blur removal approach using image processing for railway inspection
Santur, Y., Karaköse, M., Aydın, İ., & Akın, E. (2016) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip.2016.7502729 · Cited by 18
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“Researchers have developed methods for the detection of various types of damage: concrete cracks, delamination and spalling of concrete, asphalt cracks, steel fatigue cracks and corrosion [8].”
Method
Research on a pavement pothole extraction method based on vehicle-borne continuous laser scanning point cloud
Liu, R., Jiben, Y., Hongwei, R., Bori, C., & Chenhao, C. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac875c · Cited by 18
“Firstly, the gray image is pre-processed, for example, the morphological filter [15] is used to reduce the noise of the pavement image and enhance the pothole boundary [14, 16].”
Long-Standing Themes and Future Prospects for the Inspection and Maintenance of Façade Falling Objects from Tall Buildings
Chew, M. Y. L., & Gan, V. J. L. (2022) · Sensors · doi:10.3390/s22166070 · Cited by 18
Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete
Jakubowski, J., & Tomczak, K. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.135768 · Cited by 18
Lightweight concrete crack recognition model based on improved MobileNetV3
Wang, R., Chen, R., Yan, H., & Guo, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-00468-7 · Cited by 18
Participatory sensing-based geospatial localization of distant objects for disaster preparedness in urban built environments
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Shape recovery analysis of the additive manufactured 3D smart surfaces through reverse engineering
Tiwari, N., Gagare, S. W., & Shaikh, A. A. (2021) · Progress in Additive Manufacturing · doi:10.1007/s40964-020-00162-2 · Cited by 18
Efficient visual inspection of fire safety equipment in buildings
Lin, F., Wang, B., Chen, Z., Zhang, X., Song, C., Yang, L., & Cheng, J. C. P. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.105970 · Cited by 18
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Paramanandham, N., Koppad, D., & Sasithradevi, A. (2022) · Traitement du signal · doi:10.18280/ts.390210 · Cited by 17
“Koch et al. [9] reviewed various machine learning techniques for detecting the defects.”
Background
Watch out, pothole! featuring road damage detection in an end-to-end system for autonomous driving
Kortmann, F., Fassmeyer, P., Funk, B., & Drews, P. (2022) · Data & Knowledge Engineering · doi:10.1016/j.datak.2022.102091 · Cited by 17
Residual Pyramidal GAN (RP-GAN) for crack detection and prediction of crack growth in engineered cementitious composites
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Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching
Du, Y., Weng, Z., Liu, C., & Wu, D. (2020) · Journal of Advanced Transportation · doi:10.1155/2020/5804835 · Cited by 17
“Automated pavement detections using cameras [4], lasers, and ultrasonic sensors [5] are widely used as replacements to manual work, which significantly improves the efficiency and lowers the cost [6].”
Background
Extracting water channels from aerial videos based on image-to-BIM registration and spatio-temporal continuity
Chen, J., Liu, D., & Li, X. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103970 · Cited by 17
Bridge Damage Detection using a Single-Stage Detector and Field Inspection ImagesPreprint
Zhang, C., Chang, C. C., & Jamshidi, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.10590 · Cited by 17
“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].”
Digital-Twin-Based High-Precision Assembly of a Steel Bridge Tower
Li, J., Li, Q., Mao, Q., & Xu, H. (2023) · Buildings · doi:10.3390/buildings13010257 · Cited by 16
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Marino, S., Galantucci, R. A., & Saponieri, A. (2023) · Measurement · doi:10.1016/j.measurement.2023.112656 · Cited by 16
Image-based preliminary emergency assessment of damaged buildings after earthquake: Taiwan case studies
Cheng, M., Khasani, R. R., & Citra, R. J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107164 · Cited by 18
Machine learning-based pavement crack detection, classification, and characterization: a review
Ashraf, A., Sophian, A., Shafie, A. A., Gunawan, T. S., & Ismail, N. N. (2023) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v12i6.5345 · Cited by 16
Surface Feature and Defect Detection Method for Shield Tunnel Based on Deep Learning
Lin, L., Zhu, H., Ma, Y., Peng, Y., & Xia, Y. (2025) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-5986 · Cited by 16
Statistical learning of small data with domain knowledge --- sample size- and pre-notch length- dependent strength of concrete
Wang, J., Jia, J.-N., Sun, S., & Zhang, T. (2021) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2021.108160 · Cited by 16
3D surface defect detection and measurement using point cloud unwrapping via dimension reduction
Li, X., Liu, C., Yang, X., & Yan, H. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106349 · Cited by 16
Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
Tanveer, M., Kim, B., Hong, J., Sim, S., & Cho, S. (2022) · Applied Sciences · doi:10.3390/app122412786 · Cited by 14
Computer Vision-Based Concrete Crack Identification Using MobileNetV2 Neural Network and Adaptive Thresholding
Hui, L., Ibrahim, A. A., & Hindi, R. A. (2025) · Infrastructures · doi:10.3390/infrastructures10020042 · Cited by 15
“In addressing the complexities of infrastructure evaluation, computer vision, a specialized branch of artificial intelligence dedicated to equipping machines with visual data processing capabilities, has emerged as a significant innovation [17].”
Degradation Mechanism Detection in Photovoltaic Backsheets by Fully Convolutional Neural Network
Zhang, B., Grant, J., Bruckman, L. S., Wodo, O., & Rai, R. (2019) · Scientific Reports · doi:10.1038/s41598-019-52550-6 · Cited by 15
A Complex for Monitoring Transport Infrastructure Facilities Based on Video Surveillance Cameras and Laser Scanners
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Intelligent Damage Assessment for Post-Earthquake Buildings Using Computer Vision and Augmented Reality
Liu, Z., Xue, J., Wang, N., Bai, W., & Mo, Y. (2023) · Sustainability · doi:10.3390/su15065591 · Cited by 15
Long-Awaited Next-Generation Road Damage Detection and Localization System is Finally Here
Fan, R., Cheng, J., Yu, Y., Deng, J., Giakos, G. K., & Pitas, I. (2021) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco54536.2021.9615990 · Cited by 15
Knowledge Management in Construction Quality Management: Current State, Challenges, and Future Directions
Xiahou, X., Chen, G., Li, Z., Xu, X., & Li, Q. (2025) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2025.3550354 · Cited by 14
“Historically, the diagnosis of structures relied on manual methods [109], and this passive inspection approach hindered comprehensive inspections of components.”
Artificial intelligence in construction risk management: a decade of developments, challenges, and integration pathways
Tian, K., Zhu, Z., Mbachu, J., Moorhead, M., & Ghanbaripour, A. N. (2025) · Journal of Risk Research · doi:10.1080/13669877.2025.2512080 · Cited by 23
“Studies by Koch et al. (2015) and Cha, Choi, and Büyüköztürk (2017) have demonstrated CV’s effectiveness in identifying structural defects and specification deviations, thus improving quality control and reducing costly rework.”
An automatic detection method for road damage in UAV images based on multi-level perception and feature aggregation
Li, H., Chen, R., Nyirandayisabye, R., Wang, J., & Chen, Z. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103814 · Cited by 21
Automated defect segmentation and quantification in concrete structures via unmanned aerial vehicle‐based lightweight deep learning
Li, Y., Zhao, H., Gu, H., Wei, Y., Xiang, Z., Wang, Y., Yu, Y., & Bao, T. (2025) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.70062 · Cited by 14
“Integrated 2D image segmentation with laser-scanned 3D point clouds to achieve defect localization in structures (Attard et al., 2018; Koch et al., 2015).”
Weighing Votes in Human–Machine Collaboration for Hazard Recognition: Inferring a Hazard-Based Perceptual Threshold and Decision Confidence from Electroencephalogram Wavelets
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Automatic measurement of grid structures displacement through fusion of panoramic camera and laser scanning data
Wang, F., Jiang, S., & Zhang, J. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.117701 · Cited by 14
A multiscale enhanced pavement crack segmentation network coupling spectral and spatial information of UAV hyperspectral imagery
Chen, X., Zhang, X., Ren, M., Zhou, B., Sun, M., Feng, Z., Chen, B., & Zhi, X. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103772 · Cited by 15
Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation
Jaziri, A., Mundt, M., Rodriguez, A. F., & Ramesh, V. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00844 · Cited by 19
“The work of Koch et. al [29] presents an exhaustive literature review on the common practices of assessing the state of concrete infrastructure and crack detection.”
Background
Condition assessment of concrete structures using automated crack detection method for different concrete surface types based on image processing
Shalaby, Y., Badawy, M., Ebrahim, G. A., & Abdelalim, A. M. (2024) · Discover Civil Engineering · doi:10.1007/s44290-024-00089-5 · Cited by 14
“On the other hand, structure monitoring techniques based on acoustic and ultrasonic waves are more expensive, require human intervention, require expertise, and are more challenging to put into practice [6, 7].”
Unsupervised method for detection of high severity distresses on asphalt pavements
Buza, E., Akagić, A., Omanović, S., & Hasić, H. (2017) · IEEE International Scientific Conference on Informatics (Informatics) · doi:10.1109/informatics.2017.8327220 · Cited by 14
“Unfortunately, no publicly available data set exists for detection and classification of any defect type [27].”
Background
Quality of the surface finish of self-compacting concrete
Belmonte, I. M., Benito, F. J., Parra, C. J., & Payá, M. V. (2019) · Journal of Building Engineering · doi:10.1016/j.jobe.2019.101068 · Cited by 14
TACK PROJECT: TUNNEL AND BRIDGE AUTOMATIC CRACK MONITORING USING DEEP LEARNING AND PHOTOGRAMMETRY
Belloni, V., Sjölander, A., Ravanelli, R., Crespi, M., & Nascetti, A. (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-b4-2020-741-2020 · Cited by 14
“Cracks are early indicators of damage; crack detection and measurement represent, indeed, key parameters in evaluating the safety and durability of structural components (Koch et al., 2015).”
Background
Crack recognition automation in concrete bridges using Deep Convolutional Neural Networks
Zoubir, H., Rguig, M., & Elaroussi, M. (2021) · MATEC Web of Conferences · doi:10.1051/matecconf/202134903014 · Cited by 15
“[9] provided a synthesis of several research studies related to crack detection as well as crack properties extraction in reinforced concrete bridges using computer vision methods.”
Background
Deep Convolution Neural Network-Based Crack Feature Extraction, Detection and Quantification
Teng, S., & Chen, G. (2022) · Journal of Failure Analysis and Prevention · doi:10.1007/s11668-022-01430-9 · Cited by 14
“) are gradually damaged due to the influence of the environment and/or traffic loads [1].”
Background
Road anomaly detection using a dynamic sliding window technique
Chibani, N., Sebbak, F., Cherifi, W., & Belmessous, K. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07436-6 · Cited by 14
“These samples are then compared to base samples to detect anomalies in the road surface; • A Vision approach [6–9], based on image processing analysis of already captured images by a camera/video system, which are then processed to automatically detect road anomalies; • A Vibration approach…”
Background
A multi-level 3D data registration approach for supporting reliable spatial change classification of single-pier bridges
Kalasapudi, V. S., Tang, P., Xiong, W., & Shi, Y. (2018) · Advanced Engineering Informatics · doi:10.1016/j.aei.2018.06.010 · Cited by 14
Computer Vision and Deep Learning for Real-Time Pavement Distress Detection
Doycheva, K., Koch, C., & König, M. (2018) · Springer eBooks · doi:10.1007/978-3-030-00220-6_72 · Cited by 14
Self-citation
Deep learning for bridge load capacity estimation in post-disaster and -conflict zones
Pamuncak, A. P., Guo, W., Khaled, A. S., & Laory, I. (2019) · Royal Society Open Science · doi:10.1098/rsos.190227 · Cited by 15
“In addition, in image processing field, quantifying bridge condition from its image still remains a challenging task [22].”
Background
Intelligent Multitasking Framework for Boundary-Preserving Semantic Segmentation, Width Estimation, and Propagation Modeling of Concrete Cracks
Zaheer, Q., Qiu, S., Shah, S. M. A. H., Ai, C., & Wang, J. (2025) · Journal of Infrastructure Systems · doi:10.1061/jitse4.iseng-2574 · Cited by 17
Convolutional Neural network based Online Rail surface Crack Detection
Akhila, C., Diamond, C. A., & Posonia, A. M. (2021) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs51141.2021.9432080 · Cited by 13
“Convolution neural networks [5] for steel have proposed the progress in this field of neural networks was maintained by many achievements.”
Background
Application of Edge Detection Technique for Concrete Surface Crack Detection
Prasetyo, A., Yuniarto, E. M., Suprobo, P., & Tambusay, A. (2022) · International Seminar on Intelligent Technology and Its Applications · doi:10.1109/isitia56226.2022.9855280 · Cited by 13
Motion control and positioning system of multi-sensor tunnel defect inspection robot: from methodology to application
Liu, K.-Q., Zhong, S., Zhao, K., & Song, Y. (2023) · Scientific Reports · doi:10.1038/s41598-023-27402-z · Cited by 13
A machine learning-based structural load estimation model for shear-critical RC beams and slabs using multifractal analysis
Osei, J. B., Adom‐Asamoah, M., Twumasi, J. O., András, P., & Zhang, H. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.132250 · Cited by 13
Convolutional Neural Network for Predicting Failure Type in Concrete Cylinders During Compression Testing
Ojeda, J. M. P., Calderón, B. A. C., Huatangari, L. Q., Tineo, J. L. P., Pino, M. E. M., & Pintado, W. R. (2023) · Civil Engineering Journal · doi:10.28991/cej-2023-09-09-01 · Cited by 13
“Where TP (True positive), TN (True negative), FN (False negative), and FP (False positive) represent different scenarios based on the actual and predicted classes of the images [15].”
BackgroundMethod
AI-infused characteristics prediction and multi-objective design of ultra-high performance concrete (UHPC): From pore structures to macro-performance
Xu, W., Zhang, L., Fan, D., Xu, L., Liu, K., Dong, E., Yin, T., & Yu, R. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.111170 · Cited by 13
A computational method for real‐time roof defect segmentation in robotic inspection
Zhao, X., & Jebelli, H. (2025) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13471 · Cited by 17
“Diverse damage types—such as granule loss, cracks, and missing shingles—exhibit overlapping visual features that confuse algorithms, especially under variable lighting or occluded conditions (Koch et al., 2015).”
Deep Learning on Construction Sites: A Case Study of Sparse Data Learning Techniques for Rebar Segmentation
Cuypers, S., Bassier, M., & Vergauwen, M. (2021) · Sensors · doi:10.3390/s21165428 · Cited by 13
“Similarly, Koch et al. [9] describe several traditional visual inspection methods and computer vision methods for civil engineering applications.”

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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