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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Items 701–800 of 1,000

Euclidean Graphs as Crack Pattern Descriptors for Automated Crack Analysis in Digital Images
Strini, A., & Schiavi, L. (2022) · Sensors · doi:10.3390/s22165942 · Cited by 4
“Surface crack monitoring is of primary importance in the structural assessment of structures made from concrete [1,2] or other materials [3,4], and the identification and analysis of surface cracks is therefore gaining increasing interest both in the field for structural diagnostics [5,6] and in the laboratory for the…”
Background
An Automatic Pavement Crack Detection System with FocusCrack Dataset
Yan, X., Shi, S., Xu, X., He, Z., Zhou, X., Wang, C., & Lu, Z. (2022) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2022-fall57202.2022.10013000 · Cited by 4
“With the rapid development of deep learning methods in computer vision [2], [3], the accuracy of algorithms for capturing cracks using cameras has improved signiﬁcantly.”
Method
Automatic pixel-level bridge crack detection using learning context flux field with convolutional feature fusion
Li, G., Liu, Y., Shen, D., & Wang, B. (2024) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-024-00775-z · Cited by 4
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Kang, S., Kim, S., & Kim, S. (2024) · Journal of Asian Architecture and Building Engineering · doi:10.1080/13467581.2024.2373832 · Cited by 4
“A manual visual inspection is a major form of evaluating the physical and functional conditions of any infrastructure (Koch et al. 2015).”
A Method and Device for Tunnel Defect Detection Based on Image Automatic Collection and 3D Reconstruction
Deng, X., Zhou, Z., Chen, Z., Xu, Y., & Cai, W. (2024) · Lecture notes in civil engineering · doi:10.1007/978-981-97-2417-8_13 · Cited by 4
Road Distress Identification Using GPR Signals and U-Net Model
Liu, Q., & Liu, Z. (2024) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp62122.2024.10743402 · Cited by 4
ADS-YOLO: An enhanced YOLO framework for high-speed MLCCs defect detection
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A method suitable for tunnel inspection robots to diagnose multi-defects of structures using the extraction of hybrid visual features
Yan, X., Wang, K., Gao, M., Chen, L., & Liu, Y. (2025) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-025-00909-x · Cited by 4
Automatic Segmentation of Asphalt Cracks on Highways After Large-Scale and Severe Earthquakes Using Deep Learning-Based Approaches
Yilmaz, M., Yalçın, E., Demir, F., Özdemir, A. M., Atar, M., Gunes, A., Yalçın, B. F., & Çambay, E. (2025) · IEEE Access · doi:10.1109/access.2025.3536554 · Cited by 4
Digitalisation and Building Information Modelling Integration of Basement Construction Using Unmanned Aerial Vehicle Photogrammetry in Urban Singapore
Chian, S. C., Yang, J., Wong, S., Yeoh, J. K. W., & Tashrif, A. (2025) · Buildings · doi:10.3390/buildings15071023 · Cited by 4
A novel MPDENet model and efficient combined loss function for real-time pixel-level segmentation detection of tunnel lining cracks
Zhao, N., Song, Y., Liu, H., Yang, A., Jiang, H., & Tan, H. S. (2025) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2025.e04618 · Cited by 4
Automated Recognition of a Wall between Windows from a Single Image
Zhang, Y., Huo, L., & Li, H. (2017) · Journal of Sensors · doi:10.1155/2017/7051931 · Cited by 4
“To develop a real-time and cost-effective method, researchers have applied computer vision technology for the assessment of damaged structures [2].”
Method
Crack Detection and Recognition Model of Parts Based on Machine Vision
Li, D., Jiang, D., Bao, R., Chen, L., & Kerns, M. K. (2019) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.125.17 · Cited by 4
PRECISION COMPARISON OF DEEP LEARNING MODELS FOR DETECTING CONCRETE SURFACE DETERIORATION TYPES FROM DIGITAL IMAGES
Anai, S., Yabuki, N., & Fukuda, T. (2021) · Journal of Japan Society of Civil Engineers Ser F3 (Civil Engineering Informatics) · doi:10.2208/jscejcei.77.1_1 · Cited by 4
DEVELOPMENT OF AN AUTONOMOUS ROAD SURFACE DAMAGE INSPECTION PROGRAM USING DEEP CONVOLUTIONAL NEURAL NETWORK
Thuyet, D. Q., JOMOTO, M., Hirakawa, K., & Swe, Y. L. L. (2022) · Journal of JSCE · doi:10.2208/journalofjsce.10.1_235 · Cited by 4
An Automatic Defect Detection System for Synthetic Shuttlecocks Using Transformer Model
Lin, C.-S., & Hsieh, H.-Y. (2022) · IEEE Access · doi:10.1109/access.2022.3165224 · Cited by 4
“• Various size defects: It is rather difficult to identify an irregular defect pattern for the same type [5] and detect small-size defects on the curved surface of synthetic shuttlecocks.”
Background
Machine Learning Approaches for Detection/Classification and Prediction Purposes in Pavement Engineering Studies: An Overview
Karballaeezadeh, N., Maaruof, A., Danial Mohammadzadeh, S., Zamani, S., & Mudabbiruddin, M. (2023) · IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) · doi:10.1109/saci58269.2023.10158577 · Cited by 4
Corrosion Damage Detection in Headrace Tunnel Using YOLOv7 with Continuous Wall Images
Kubo, S., Nakayama, N., Matsuda, S., & Chun, P. (2023) · Applied Sciences · doi:10.3390/app13169388 · Cited by 4
Pothole detection using convolutional neural network
Srivani, B., Kamala, C., Deepti, S. R., & Aakash, G. (2024) · AIP conference proceedings · doi:10.1063/5.0198902 · Cited by 4
Rapid and Accurate Identification of Concrete Surface Cracks via a Lightweight & Efficient YOLOv3 Algorithm
Gu, H., Zhu, K., Strauß, A., Shi, Y., Šumarac, D. M., & Cao, M. (2024) · Structural durability & health monitoring · doi:10.32604/sdhm.2024.042388 · Cited by 4
An Improved Image Pre-processing Method for Concrete Crack Detection
Kapadia, H. K., Patel, R., Shah, Y., Patel, J. B., & Patel, P. V. (2019) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-030-00665-5_149 · Cited by 4
Semi-automated discrete-element modelling of arch structures incorporating SfM photogrammetry
Kassotakis, N., Sarhosis, V., Peppa, M. V., & Mills, J. P. (2022) · Proceedings of the Institution of Civil Engineers - Engineering History and Heritage · doi:10.1680/jenhh.21.00007 · Cited by 4
Automatic Crack Detection Results Using a Novel Device for Survey and Analysis of Road Pavement Condition
Bosurgi, G., Sollazzo, G., Bongiorno, N., & Pellegrino, O. (2019) · Lecture notes in civil engineering · doi:10.1007/978-3-030-29779-4_42 · Cited by 4
Assessing an Asphalt Mixture’s Self-Healing with Microwave Induction for Enhanced Durability and Structural Restoration
Sattar, M., Hafeez, I., Waleed, M., & Khalid, R. A. (2024) · Journal of Transportation Engineering Part B Pavements · doi:10.1061/jpeodx.pveng-1506 · Cited by 3
Fast and intelligent detection of concrete cracks based on sound signals and convolutional neural network
Ge, C., Qin, Y., Xie, K., & Lu, Z. (2025) · Frontiers in Built Environment · doi:10.3389/fbuil.2025.1627643 · Cited by 3
Identifying Struck-By Hazards in Unknown Lifting Loads: A Hybrid Computer Vision Approach
Tang, Y., Liu, C., Feng, K., Yang, X., Li, X., & Leng, X. (2025) · Journal of Construction Engineering and Management · doi:10.1061/jcemd4.coeng-15334 · Cited by 3
Segmentation and Quantification of Surface Defects in 3D Reconstructions for Damage Assessment and Inspection
Sterckx, J., Vlaminck, M., & Luong, H. Q. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3593967 · Cited by 3
“…our method is highly e ﬀ ective across various surfaces, it is less suited to detecting widespread shallow damage, which could beneﬁt from incorporating additional geometric constraints. infrastructure, such as bridges [1], buildings [2], roads [3], power plants [4], and wind turbine blades [5].”
A review of concrete bridge surface defect detection based on deep learning
Liao, Y., Huang, C.-Y., & Soliman, A. (2025) · Optoelectronics Letters · doi:10.1007/s11801-025-4116-7 · Cited by 5
An Integrated Framework with SAM and OCR for Pavement Crack Quantification and Geospatial Mapping
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Monitoring data cleaning of in-service historic masonry structures: A case study using BiLSTM-AdaBoost and DBSCAN models
Zhao, Y., Ju, H., Zheng, L., Deng, Y., & Li, A. (2025) · Measurement · doi:10.1016/j.measurement.2025.120115 · Cited by 3
Small Defect Instance Reconstruction Based on 2D Connectivity-3D Probabilistic Voting
Hong, K., Wang, H., & Zhu, B. (2021) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio54168.2021.9739608 · Cited by 3
Multiple Classification Network of Concrete Defects Based on Improved EfficientNetV2
Ni, J., Wang, B., Lu, K., Zhang, J., Chen, P., Pan, L., Zhu, C., Wang, B., & Wang, W. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-4742-3_52 · Cited by 3
A Systematic Review of the Use of Computer Vision and Photogrammetry Tools in Learning-Based Dimensional Road Pavement Defect Detection for Smart Transportation
Tafida, A., Zawawi, N. A. W. A., Alaloul, W. S., & Musarat, M. A. (2024) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets61466.2024.10577707 · Cited by 3
“Some techniques, like GPR, drones, laser imaging, and 3D laser imaging, were noted to carry a significant cost, potentially hampering their widespread implementation [10, 46, 84].”
Method
Damage classification and segmentation in extended shear tab connection using convolutional neural networks and transfer learning
Satarkar, P. R., Dixit, P. R., & Londhe, S. N. (2024) · Asian Journal of Civil Engineering · doi:10.1007/s42107-024-01185-8 · Cited by 3
“Such as (Jahanshahi et al., 2009; Koch et al., 2015; Mohan & Poobal, 2017; Zakeri et al., 2017) studied several types of image processing methods for detection of cracks, identification of damage patterns in concrete pavement, pipes, tunnels and bridges.”
Potential damage area detection of bridges based on single-temporal point cloud
Liu, X., Yu, B., Cai, D., & Wang, R. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad9041 · Cited by 3
“Although the accuracy of single-temporal point cloud is lower than those of traditional leveling and total stations [17], it can build a full bridge surface 3D representation, which helps achieve timely and accurate overall bridge surface inspections [18, 19].”
Pavement crack detection based on local enhancement attention mechanism and deep semantic-guided multi-feature fusion
Li, J., Li, H., Tu, J., Liu, Z., Yao, J., & Li, L. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.6.063027 · Cited by 3
A hybrid artificial neural network–genetic algorithm back-analysis technique for local anomaly detection in railway track substructure
Fathi, S., Mehravar, M., & Rahman, M. (2024) · Proceedings of the Institution of Civil Engineers - Transport · doi:10.1680/jtran.23.00066 · Cited by 3
A Generative Approach to Generalize Deep Learning Models for Pavement Distress Segmentation
Prajapati, A. K., & Guler, S. I. (2025) · Data Science for Transportation · doi:10.1007/s42421-025-00118-4 · Cited by 3
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Qiao, Y., Wang, J., Zhang, X., Huang, J., He, L., & Zhang, R. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adb641 · Cited by 3
“This approach has been criticized for being time-consuming, inefficient, unsafe, and highly subjective [6, 7].”
Iterative building condition assessment and prediction using unified 3D point clouds and a BIM model: A case study
Wettewa, S., Chai, P., Weerasinghe, N. P., Hou, L., Edirisinghe, R. K., Zhang, G. (., & Setunge, S. (2025) · Journal of Infrastructure Intelligence and Resilience · doi:10.1016/j.iintel.2025.100180 · Cited by 3
IMAGE MOSAICKING AND SEMI-AUTOMATIC CRACK EVALUATION FOR VISUAL INSPECTION OF CONCRETE STRUCTURES
Fujita, Y., TAGUCHI, T., & Hamamoto, Y. (2018) · Journal of Japan Society of Civil Engineers Ser F3 (Civil Engineering Informatics) · doi:10.2208/jscejcei.74.18 · Cited by 3
Real-time Quantitative Visual Inspection using Extended Reality
Al‐Sabbag, Z. A., Connelly, J. P., Yeum, C. M., & Narasimhan, S. (2021) · Journal of Computational Vision and Imaging Systems · doi:10.15353/jcvis.v6i1.3557 · Cited by 3
“Most state-of-the-art vision-based methods for detecting and localizing damage rely on automated image segmentation algorithms that either use DNN-based semantic segmentation [3–5] or hand-crafted features [7–9].”
Method
CrackLabel: A Thresholding-Based Crack Labeling Tool for Asphalt Pavement Images
Yusof, N. A. M., Osman, M. K., Ahmad, F., Idris, M., Ibrahim, A., Tahir, N. M., & Yusof, N. M. (2021) · Civil Engineering and Architecture · doi:10.13189/cea.2021.091307 · Cited by 3
“This method completely depends on the surveyors’ level of experience and knowledge that is expected to influence the subjectivity of human interpretation and perception of pavement rating [3][4] since the surveyors provide different result analyses.”
Technology of three-dimensional laser scanning as a tool to provide safety for sport facilities
Gura, D. A., Bespyatchuk, D. A., Samarin, S. V., Kiryunikova, N. M., & Lesovaya, E. D. (2021) · Nanotechnologies in Construction A Scientific Internet-Journal · doi:10.15828/2075-8545-2021-13-4-259-263 · Cited by 3
Intelligent Road Surface Deep Embedded Classifier for an Efficient Physio-Based Car Driver Assistance
Rundo, F., Leotta, R., Piuri, V., Genovese, A., Scotti, F., & Battiato, S. (2021) · IEEE International Conference on Autonomous Systems (ICAS) · doi:10.1109/icas49788.2021.9551124 · Cited by 3
Ground Robotic Systems for the Inspection of Concrete Bridges
Tesic, K., Baričević, A., Serdar, M., & Pečur, I. B. (2021) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro52101.2021.9596813 · Cited by 3
Distress Detection in Subway Tunnel Images via Data Augmentation Based on Selective Image Cropping and Patching
Maeda, K., Takada, S., Haruyama, T., Togo, R., Ogawa, T., & Haseyama, M. (2022) · Sensors · doi:10.3390/s22228932 · Cited by 3
Road Defect Detection Based on Semantic Transformed Disparity Image Segmentation
Wang, L., Shi, W., Zhu, H., Zhang, D., Zhang, Y., Fan, J., & Bocus, M. J. (2022) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio55434.2022.10011748 · Cited by 3
“The 2D image analysis-based road defect detection meth-ods usually consist of four main procedures: a) image pre-processing, b) image segmentation, c) shape extraction, and d) object recognition [4].”
BackgroundMethod
Research on Feature Identification and Trajectory Planning of Pavement Cracks
Zhou, Z., Cai, S., Lin, B., & Lin, J. (2023) · Applied Sciences · doi:10.3390/app13042241 · Cited by 3
Convolutional Neural Network Based Improved Crack Detection In Concrete Cubes
Kapadia, H. K., Patel, P. V., & Patel, J. B. (2023) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/130127 · Cited by 5
“Researchers have presented a very comprehensive review in the domain of crack detection using computer vision [7][8][9][10][11].”
Background
Evaluation of machine learning in recognizing images of reinforced concrete damage
Fan, C. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14911-2 · Cited by 3
“Koch et al. [33] noted that the accuracy of detection varies depending on camera pose, camera distances, and environmental conditions (lighting and shadows at different locations).”
Background
Vision based defect detection technologies in civil structures: a review study
Chen, X., Ma, Y., & Lv, S. (2023) · Journal of Optics · doi:10.1007/s12596-023-01304-9 · Cited by 3
“Vision-based defect detection systems can provide a more efficient and accurate method for identifying and locating defects than traditional manual inspection methods [19–21].”
Development of an Autonomous Component Testing System with Reliability Improvement Using Computer Vision and Machine Learning
Phan, H.-A., Duong, V. T., Thi, M. N., Thi, A. N., Thu, H. K. T., Duc, T. L., Dang, V. H., Tran, H. Q. D., Van, N. T. T., & Bui, T. T. (2024) · ECTI Transactions on Computer and Information Technology (ECTI-CIT) · doi:10.37936/ecti-cit.2024181.253854 · Cited by 3
A simple image correlation technique for imaging subsurface damage from low-velocity impacts in composite structures
Abbott, T. B., & Yuan, F. (2024) · Structural Health Monitoring · doi:10.1177/14759217241228884 · Cited by 3
“17 However, like visual inspection, this category of computer vision techniques has historically only been able to identify damage that is visible on the surface and is not suitable for inspection of subsurface BVID.”
SpotCrack: Leveraging a Lightweight Framework for Crack Segmentation in Infrastructure
Khan, A., Khan, M., Gueaieb, W., Saddik, A. E., Masi, G. D., & Karray, F. (2024) · International Conference on Consumer Electronics · doi:10.1109/icce59016.2024.10444358 · Cited by 3
“However, manual infrastructure assessment is labor-intensive and subjective, yielding inaccurate results, particularly if the inspector lacks experience [2].”
Background
Life Cycle Assessment Comparison Between Traditional and Innovative Tunnel Retrofitting Approaches
Marcucci, A., Vegeto, E., Arbelaez, J. M. T., Guanziroli, S., Borroni, M., Magnelli, F., Pierani, M., Frisani, S., Susani, S., & Ferrara, L. (2024) · Rilem bookseries · doi:10.1007/978-3-031-70277-8_59 · Cited by 3
BUBC-Net: Bottom-Up-Based CNN Model With Bounding Circle for Efficient Detection of Circular Sewer Pipe Joints
He, J., & Wang, Z. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3575961 · Cited by 3
“These algorithms, however, rely on handcrafted features designed for speciﬁc scenarios and are prone to failure in critical pipe joint detection [17].”
Three-dimensional Pavement Disease Detection based on Three components Decomposition and TUFF Algorithm
Zhou, R., Lin, M., Xia, Y., & Shi, X. (2019) · International Conference on Information Communication and Signal Processing (ICICSP) · doi:10.1109/icicsp48821.2019.8958594 · Cited by 3
“The normal operation of the huge highway system highly depends on timely maintenance and repair, while the current highway maintenance work in China relies on the regular implementation of the plan, and the post-accident inspection is carried out at a prescribed time interval [1].”
Background
Geodetic Monitoring System to Ensure Safe Operation of Infrastructure Facilities
Gura, D. A., Kiryunikova, N. M., Lesovaya, E. D., Khusht, N., Pavlukova, A. P., & Podtelkov, V. V. (2020) · International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon) · doi:10.1109/fareastcon50210.2020.9271604 · Cited by 3
“00 © 2020 IEEE identification of deformations in the inspected facility (changes in the geometry of the object during its operation) [4].”
Background
Real-time 3D work-piece tracking with monocular camera based on static and dynamic model libraries
Zhu, W., Wang, P., Li, R., & Nie, X. (2017) · Assembly Automation · doi:10.1108/aa-02-2017-018 · Cited by 3
“Vision system is widely used in industrial applications such as defects detection (Koch et al., 2015), board manufacturing (Wang et al.”
Background
Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method
Zhang, Z., Wei, H.-H., Yum, S., & Chen, J. (2019) · Applied Sciences · doi:10.3390/app9183915 · Cited by 3
A Method for Selecting Strategic Deployment Opportunities for Unmanned Aircraft Systems (UAS) for Transportation Agencies
Hubbard, S. M. L., & Hubbard, B. (2020) · Drones · doi:10.3390/drones4030029 · Cited by 3
“and are useful in pavement infrastructure monitoring [6,7].”
Background
Gradient Boosting Coupled with Oversampling Model for Prediction of Concrete Pipe-Joint Infiltration Using Designwise Data Set
Wong, L. S., Marani, A., & Nehdi, M. L. (2021) · Journal of Pipeline Systems Engineering and Practice · doi:10.1061/(asce)ps.1949-1204.0000557 · Cited by 3
Automatic Vision System for Surface Inspection and Monitoring : Application to Wheel Inspection
Tout, K. (2018) · doi:10.70675/87b1f587zce5ez47a7za786z0fae013b8bf5 · Cited by 3
“For a more detailed review on methods for automatic defects detection, the reader is referred to [111,112,133,134].”
Method
Crack Width Detection for Concrete Structures Using Thermal Images and Artificial Neural Network
Hamdan, A., Bingamil, A. A., Hamdan, S., Soares, T. G., Dabous, S. A., Alsyouf, I., & Hosny, F. (2024) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese63988.2024.10911945 · Cited by 2
“Visual inspection is currently the predominant non-destructive evaluation technique for assessing the physical and functional conditions of structures regularly to ensure the expected service requirements of the structures are met [7] and guarantee their safety and serviceability.”
RLD-Net: an enhanced deep learning model for accurate pavement distress detection using UAV captured images
Cheng, H. D., Zhang, J., Yan, H., Wang, Y., & Dong, L. (2025) · Multiscale and Multidisciplinary Modeling Experiments and Design · doi:10.1007/s41939-025-00950-9 · Cited by 4
“Thus,the establishment of a benchmark dataset for pavement defects would provide consistent training data and enable fair performance comparisons, thereby accelerating advancements in this crucial area (Koch et al. 2015; Voulodimos et al. 2018).”
Automatic dimensional quality inspection system for regular precast concrete elements based on 3D structure lighting scan technology
Yang, Z., Tang, D., Li, D., Chen, T., Liu, J., Qi, H., Wu, Z. P., & Zhou, J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129149 · Cited by 2
Automatic defect localization method based on BIM projected image
Xu, L., Feng, X., Liu, J., Qi, H., & Wang, Y. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103874 · Cited by 2
Automated Detection and Quantification of Sewer Pipe Cracks Using a CNN-Based Approach
Yang, C., Zhu, D. Z., Zhang, T., Wan, X., & Ma, Y. (2025) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-6549 · Cited by 2
An Intelligent Robotic System for Surface Defect Detection on Stay Cables: Mechanical Design and Defect Recognition Framework
Yang, Y., Zhang, Q., Ji, Y., & Gui, Z. (2025) · Buildings · doi:10.3390/buildings15213907 · Cited by 2
Investigation of the Transferability of Measured Data for Application of YOLOv8s in the Identification of Road Defects: An SA-Indian Case Study
Babawarun, T., Pandelani, T., & Ngwangwa, H. (2025) · Sustainability · doi:10.3390/su172310641 · Cited by 2
Accurate concrete spalling segmentation from bounding box supervision using Segment Anything
Zhang, C., Mishra, D. K., Yuen, M. M. F., Yu, Y., & Zhang, J. (2026) · Automation in Construction · doi:10.1016/j.autcon.2025.106752 · Cited by 2
Crack Identification and Severity Analysis via Computer Vision: Comparative Study of RGB and Grayscale Imagery
Fan, C. (2026) · Journal of Construction Engineering and Management · doi:10.1061/jcemd4.coeng-16994 · Cited by 2
Computer vision for infrastructure defect detection: Methods and trends
ZHANG, Y., Li, G., & Shen, R. (2026) · Automation in Construction · doi:10.1016/j.autcon.2026.106795 · Cited by 2
YOLO-ELC: Enhanced Detection of Pavement Distresses Using UAV-Captured Images
Zhang, J., Cheng, H. D., Gao, Y., & Wang, Y. (2026) · International Journal of Pavement Research and Technology · doi:10.1007/s42947-026-00726-5 · Cited by 2
Efficient and Interpretable Image Processing Approach for Crack Segmentation
Zaheer, Q., Shah, S. F. H., Shah, S. M. A. H., Ai, C., Wang, J., & Qiu, S. (2026) · Journal of Infrastructure Systems · doi:10.1061/jitse4.iseng-2759 · Cited by 2
Enhancing crack detection and severity assessment in historical Tabiya basins using U-Net and adaptive thresholding
Matich, H., Attmani, J., & Mousannif, H. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1741082 · Cited by 2
Accuracy improvement in a calibration test bench for accelerometers by a vision system
D’Emilia, G., Gasbarro, D. D., Gaspari, A., & Natale, E. (2016) · AIP conference proceedings · doi:10.1063/1.4952690 · Cited by 2
Warning System to Identify Pothole and Speed Breakers on Roads Using JSON Data
Thendral, R., & Balachandar, A. (2021) · Turkish Online Journal of Qualitative Inquiry · Cited by 2
A Learning-Based Surface Defect Detection Method for Internal Surfaces of Bellows
Zhang, Z., Xu, J., Wang, S., Li, F., & Ling, Q. (2021) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc52312.2021.9601703 · Cited by 2
Detection and Classification of Road Damage Using Deep Learning Approach with Smartphone Images
Shila, S., Bayshakhy, F. T., & Sattar, A. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307090 · Cited by 2
YOLO v3-based Concrete Wall Damage Detection with Single Class Classification
Philip, R. E., Andrushia, A. D., Mathews, M. E., & Anand, N. (2023) · International Conference on Smart Computing and Communications (ICSCC) · doi:10.1109/icscc59169.2023.10334995 · Cited by 2
A Large-Crack Image-Stitching Method with Cracks as the Regions of Interest
Kao, S., Lin, J.-S., Wang, F.-L., & Hung, P.-S. (2024) · Infrastructures · doi:10.3390/infrastructures9040074 · Cited by 2
How to Make a State of the Art Report—Case Study—Image-Based Road Crack Detection: A Scientometric Literature Review
Fan, L., Tang, S., Ariffin, M. K. A. M., Ismail, M. I. S., & Zhao, R. (2024) · Applied Sciences · doi:10.3390/app14114817 · Cited by 2
A method of hybrid dilated and global convolution networks for pavement crack detection
Qu, Z., Li, M., Yuan, B., & Mu, G. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01408-7 · Cited by 2
Computer vision-based cascade detection of peeling and cracking in masonry house walls
Zhang, C., Lin, S., Yu, J. Z., & Zhang, T. (2024) · European Journal of Environmental and Civil engineering · doi:10.1080/19648189.2024.2409266 · Cited by 2
“Fortunately, with the rapid advancements in artificial intelligence, machine vision technology has been successfully applied to surface defect detection in concrete buildings, pavements, and underground structures (Huang et al., 2021; Koch et al., 2015; Mishra, 2021; Rosso et al., 2023).”
Real-time data sensing and digital twin model development for pavement material mixing: enhancing workability and optimisation
Wang, C., Zhou, X., Zhang, Y., Wang, H., Oeser, M., & Liu, P. (2024) · International Journal of Pavement Engineering · doi:10.1080/10298436.2024.2417973 · Cited by 2
“The mixture without enough mixing can cause some performance distresses such as segregation, cracks, or lack of loading capacity (Koch et al. 2015).”
Concrete and Pavement Cracks Detection Leveraging Vision Transformer (ViT) Model
Zaman, F., Xu, Z., Hussain, A., Tariq, Aslam, A., & Abdullahi, M. R. (2024) · International Conference on Electrical and Information Technology (IEIT) · doi:10.1109/ieit64341.2024.10762971 · Cited by 2
“To prevent such occurrences, it is crucial to carry out routine independent assessments of public infrastructure [7].”
Displacement time series forecasting and anomaly detection based on EGMS-PSInSAR data towards effective bridge monitoring
Pięk, M., & Pawłuszek-Filipiak, K. (2024) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2024.101433 · Cited by 3
Data-driven methods for data quality evaluation, maintenance assessment and decision model fusion in asphalt pavement management system
Han, C., Han, T., Yang, S., Ma, T., Zheng, T., & Wang, S. (2025) · Road Materials and Pavement Design · doi:10.1080/14680629.2025.2497060 · Cited by 2
“Throughout the entire lifecycle of a road, proper maintenance management is crucial for ensuring its long-term durability and continued serviceability (Koch et al., 2015).”
Evaluating Road Crack Segmentation Performance in Participatory Sensing: An Exploration of Alternative Metrics
Hyeon, J., Jeong, M., Chern, W., Asari, V. K., & Kim, H. (2025) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-6011 · Cited by 2
Enhanced pothole detection in low-light conditions using DCE-Pothole and YOLOv10 fusion
Xu, X., Wu, T., Ge, W., Dai, L., Gong, J. W., Chen, D., & Li, S. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107322 · Cited by 6
Evaluating Deterioration Levels of Utility Poles Using Participatory Sensing and Fuzzy Inference
Kim, H., & Ham, Y. (2020) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784482865.087 · Cited by 3
Low-cost, quantitative assessment of highway bridges through the use of unmanned aerial vehicles
Ellenberg, A., Kontsos, A., Moon, F., & Bartoli, I. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2219741 · Cited by 2
“The use of image processing techniques for identification of damage has been applied to many types of damage [7].”
Method
Potholes Road Classification by Shape and Area Features
Rosita, Y. D., & Sugianto, S. (2019) · Record and Library Journal · doi:10.20473/rlj.v5-i1.2019.72-79 · Cited by 2
Artificial Auditory Perception Pattern Recognition System Based on Spatiotemporal Convolutional Neural Network
Fang, X., Fang, H., Feng, Z. Z., Wang, J., & Zhou, L. (2019) · Applied Sciences · doi:10.3390/app10010139 · Cited by 2
Yol Yüzey Anormalisinin Tespiti ve Analizi
DEVECİ, E., & Ergen, B. (2021) · Bitlis Eren Üniversitesi Fen Bilimleri Dergisi · doi:10.17798/bitlisfen.942386 · Cited by 2
One-Class Detection and Classification of Defects on Concrete Surfaces
Faula, Y., Églin, V., & Bres, S. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9659221 · Cited by 2
“The images are most often private, as the authors point out in [10].”
Background
Automatic Detection of Brick Pavement Defects Using Convolutional Neural Network
Ji, A., Xue, X., Dou, Y., & Wang, Y. (2021) · International Conference on Construction and Real Estate Management (ICCREM) · doi:10.1061/9780784483848.029 · Cited by 2
Intelligent Road Surface Categorization for Self-Adaptive Driving Assistance Systems
Rundo, F., Leotta, R., & Battiato, S. (2021) · AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) · doi:10.23919/aeitautomotive52815.2021.9662922 · Cited by 2

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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