Cited by 1,065Infrastructure Maintenance and MonitoringIndustrial Vision Systems and Defect DetectionCivil and Structural Engineering Research

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

Open access

Koch, Christian, Georgieva, Kristina, Kasireddy, Varun, Akinci, Burcu, Fieguth, Paul·2015·Advanced Engineering Informatics

DOI 10.1016/j.aei.2015.01.008

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.aei.2015.01.008

Cites

106 of 106 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Pattern Classification
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
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Image analysis and mathematical morphology
(1982) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(82)90078-8 · Cited by 8,152
Pattern Classification
Abe, S. (2001) · Springer eBooks · doi:10.1007/978-1-4471-0285-4 · Cited by 3,758
Morphological Image Analysis: Principles and Applications
(2000) · Sensor Review · doi:10.1108/sr.2000.08720cae.001 · Cited by 3,537
Photo tourism
Snavely, N., Seitz, S. M., & Szeliski, R. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141964 · Cited by 2,977
Morphological Image Analysis
Soille, P. (1999) · Springer eBooks · doi:10.1007/978-3-662-03939-7 · Cited by 1,981
Computer Vision
Stockman, G. C., & Shapiro, L. G. (2001) · Cited by 1,706
Handbook of Image and Video Processing
Bovik, A. C. (2005) · Elsevier eBooks · doi:10.1016/b978-0-12-119792-6.x5062-1 · Cited by 1,585
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

Show all 107 references

Research works printed but not linked: 5

T. Iseley, D.M. Abraham S. Gokhale, Intelligent sewer condition evaluation technologies, in: Proceedings of the International NO-DIG Conference, 1997, pp. 254–265.
P. Kohut, K. Holak, T. Uhl, Monitoring of civil engineering structures supported by vision system, in: European Workshop on Structural Health Monitoring, 2012, pp. 1575–1582.
Miyamoto, 2007, Automatic crack recognition system for concrete structures using image processing approach, Asian J. Inf. Technol., 6, 553
S.C. Radopoulou, I. Brilakis, Improving patch distress detection using vision tracking on video data, in: Proceedings of the 21st International Workshop on Intelligent Computing in Engineering, 2014.
G. Moussa, K. Hussain, A new technique for automatic detection and parameters estimation of pavement crack, in: 4th International Multi-Conference on Engineering Technology Innovation, IMETI 2011, 2011.

Sorted by citations

Items 901–1,000 of 1,000

References
Feng, D., & Feng, M. Q. (2020) · doi:10.1002/9781119566557.refs
A Robust Defect Detection Method with Dense Differential Siamese Network
He, J., Wang, X., Zeng, Z., & Yan, F. (2022) · Frontiers in artificial intelligence and applications · doi:10.3233/faia220015
Hybrid Computing System for Real-Time Implementation of a Convolutional Neural Network Application
Jain, P., Hiran, R., Kapadia, H. K., Patel, P. V., & Patel, J. B. (2022) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat53423.2022.9726122
“Concrete crack detection is a popular application which is being solved by different convolutional neural network-based approaches [11], [12], [13].”
Background
A Vision-Based System for Structural Displacement Measurement
Perez, F. J., & Mora, O. E. (2022) · Proceedings of the World Congress on Civil, Structural, and Environmental Engineering · doi:10.11159/icsect22.127
“Other studies focus on structural defect analysis of asphalt and concrete, including their detection and condition assessment [9-11].”
Background
Review on Buildings Health Monitoring System by Using Hybrid Data Optimization Based on Machine Learning Algorithm
Shenmare, S. D. (2021) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-857
Models of Performance Measures and Quantification in Automation
Zakeri, H., Nejad, F. M., & Gandomi, A. H. (2022) · doi:10.1002/9781119800675.ch9
Pothole recognition using DeepCNN with layer permutation scheme
Aggarwal, S., Pranav, S., Lahoti, M., Singh, A. P., & Roy, N. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.4.043011
“6,7 In this work, the focus is on pothole recognition by image processing techniques applied to two-dimensional (2D) images.”
Background
A Novel Surface Crack Detection and Dimension Estimation Using Image Processing Technique
Shreyank, K., Yukta, K., Sowmya, N., Komal, M., Saroja, V., & Suhas, S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-8721-1_8
Computer Vision and Forensic Investigation
Anani-Manyo, N., & Liu, R. (2022) · ASCE Forensic Engineering Congress · doi:10.1061/9780784484548.061
Meta-learning for emerging applications: Finance, building materials, graph neural networks, program synthesis, transportation, recommendation systems, and climate science
Zou, L. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-89931-4.00004-3
Pavement Condition Detection Method Based on Time-Frequency Features and Capsule Neural Network
Chen, W., Lv, P., Li, J., & Ma, X. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020455
“For example, computer vision-based models are used to fit the surface shape, edge and contour of roads [2], [3].”
Method
An investigation study on automatic crack detection using image processing techniques
Vinodhini, V., & Sidhaarth, A. (2023) · AIP conference proceedings · doi:10.1063/5.0139421
Integrating Information in Visual and Thermal Image for Crack and Moisture Detection in Concrete Structures through CNNs
Katual, J., Kaul, A., & Joel, S. (2023) · International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN) · doi:10.1109/vitecon58111.2023.10157518
“Koch et al. conducted a comprehensive literature review of the current state of the art in computer vision for defect detection and evaluation of civil infrastructure materials like concrete and asphalt [5].”
Background
A Comparative Analysis of Two Deep Learning Neural Networks for Defect Detection in Steel Structures Using UAS
Mitra, R., Das, A., Heichel, J., Dorafshan, S., & Kaabouch, N. (2023) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit57321.2023.10187308
“The most widely used methods for steel surface defect detection are non-destructive manual visual inspections [4-5].”
Method
Research on Visual Detection Methods and Development Trends of Surface Defects of Urban Tunnels
Xu, G., Gao, M., Liu, F., & Liu, Y. (2023) · IEEE Conference on Prognostics and Health Management · doi:10.1109/icphm57936.2023.10194009
“However, threshold segmentation methods for the detection of structural surface defects in realistic urban tunnel environments are difficult to be further extended due to the grey-scale similarity between structural apparent potential defects and the background [17], which is prone to misjudgment of disease.”
Background
Diagnostics of defects of monolithic reinforced concrete structures using neural network technologies
Сергеевич, Д. Д., Gerasimov, A., & Buralkov, P. (2023) · AIP conference proceedings · doi:10.1063/5.0143450
Semantic Segmentation of Synthetic Images into Building Components for Automated Quality Assurance
Zhang, H., Huang, L., Cai, W., & Zou, Z. (2023) · Lecture notes in civil engineering · doi:10.1007/978-3-031-34593-7_14
Deep Learning Neural Networks in Building Superstructure Classification
Phang, J. T. S., Lim, K. H., & Lim, C. H. (2023) · International Conference on Digital Applications, Transformation & Economy (ICDATE) · doi:10.1109/icdate58146.2023.10248658
“Building superstructure [1]–[3] is part of a building that is constructed above the ground level and it serves the purpose of the structure’s intended use.”
Background
A Normal Vector Constrained Gaussian Curvature Method for Detecting Potential Damage Areas in Bridges Using Single-Temporal TlsPreprint
Liu, X., Cai, D., Zhao, S., & Wang, R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4590571
Analysis and Improvement of the Current Pothole Detection System in Google Maps through Color Segmentation
Kaneti, V. R. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.56147
Hafif Çelik Elemanlar için İki Boyutlu Görüntülerden Üç Boyutlu Yüzey Ağı Elde Edilmesi
Erkal, B. G. (2023) · Düzce Üniversitesi Bilim ve Teknoloji Dergisi · doi:10.29130/dubited.1146323
An Extensive Review on Image Classification Techniques for Expert Systems
Sharma, P., Sharma, R. K., Kansal, I., Kumar, R., & Gill, R. (2023) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/0123520965282357231123093259
A Study on End-to-End Detection Method of Concrete Crack Grade Based on Computer VisionPreprint
Qi, Z., Li, G., & Huang, Z. (2024) · Research Square · doi:10.21203/rs.3.rs-3771187/v1
Novel Technique to Measure Shape Memory Behavior of 4D Material
Tiwari, N., & Markad, K. M. (2024) · doi:10.1002/9781394198085.ch15
Structural Vision Data Collection and Dataset
Mosalam, K. M., & Gao, Y. (2024) · Synthesis lectures on mechanical engineering · doi:10.1007/978-3-031-52407-3_5
Crack-JPU – A crack segmentation method using atrous convolution
Nikhade, G., Khandelwal, P., Sonsare, P. M., Yadlapati, K., & Duvvuri, S. S. (2024) · Measurement Sensors · doi:10.1016/j.measen.2024.101080
Method and Research of Concrete Pavement Crack Detection Based on Convolution Neural Network
Wu, F. (2023) · Highlights in Science Engineering and Technology · doi:10.54097/hset.v56i.10105
An Automatic System for Detecting and Analyzing Tunnel Lining Cracks Based on Semantic SegmentationPreprint
Yang, Y.-Z., Wang, M., Liu, C., Xu, H.-T., & Zhang, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4767695
Review of Pavement Crack Types and Their Significance
Fan, L., Tang, S. H., Ariffin, M. K. A. M., Ismail, M. I. S., & Zhao, R. (2024) · International Journal of Business and Technology Management · doi:10.55057/ijbtm.2024.6.1.52
Defect Detection in Metal Surfaces Using Computer Vision
Singh, D. K. K., & Ghosh, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-9442-7_4
Adaptive Knowledge Integration for Efficient Learning of Transformer-Based Pixel-Level Crack DetectionPreprint
Li, H., Huang, L., Zhu, H. X., Gao, K., & Huang, B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4838598
Pothole Detection and Classification Using Deep LearningPreprint
Shevetaa, S., Jasthi, L. N., SaiRayapati, E., Patnala, S., Pandikuntae, M., Upputla, K. U., & Myla, S. M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4835438
An Automatic System for Detecting and Analyzing Tunnel Lining Cracks Based on Semantic SegmentationPreprint
Yang, Y.-Z., Wang, M., Liu, C., Xu, H.-T., & Zhang, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4850610
Specification of Forest Road Surface Potholes using UAVs Image Evaluation
mutlagh, A. R., Parsakhoo, A., Najafi, A., & mohamadi, J. (2023) · Risālaẗ al-Ḥusayn. · doi:10.61186/ifej.11.22.110
Crack Sealing Path Planning Through Depth Grid Mapping with Behavior Cloning and Reinforcement LearningPreprint
Wang, J., Chong, E. K. P., Fang, Y., Pan, Z., & Wang, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4887433
Employing Virtual Reality for Evaluating Infrastructure Conditions
Tatan, B., Nassar, S., Abuhalimeh, M. S., Mortula, M. M., & Beheiry, S. M. (2024) · Lecture notes in civil engineering · doi:10.1007/978-981-97-5311-6_9
Addressing Tunnel Segment Misalignment Challenges: A Comparative Analysis of Detection Techniques
Wu, X., & Liao, B.-Y. (2024) · Journal of Information Systems Engineering & Management · doi:10.55267/iadt.07.15152
Crack Recognition and Quantitative Analysis Based on Deep Learning
Cunjun, F. A. N., Songyan, J. I. N., Nan, J. I. N., Shi, Z., Yongjingbang, W. U., & Xintian, H. A. O. (2024) · Industrial Construction · doi:10.3724/j.gyjzg24061802
“于其生产工艺简单抗压强度高耐久性好等优点被广泛应用于建筑与基础设施 [1] 。裂缝是混凝土结构中最为常见的表观损伤之一 [2-3] 能 [4] 。因此裂缝检测对于保障混凝土结构服役安全具有重要意义 [5] 。传统的裂缝检测方法依赖于人眼识别 [6] , 效率低、费用高且主观性较强 [7] , 测技术成为热点 [8-9] 特征尺度的分析工作较少而裂缝几何特征量化是评估裂缝对混凝土结构影响程度的重要指标 [10] 。为了获得裂缝几何特征参数部分国内外学者提出像素标定法网格线标定法 [11] 、双目视觉法 [12] 、多视角几何…”
Bridge Pier Verticality Detection Considering Surface Point Cloud Curvature Characteristics
Liu, X., Cai, D., & Wang, R. (2024) · Sensors and Materials · doi:10.18494/sam5025
Appearance Defect Detection and Localisation using a Lightweight CNN-based Detector
Cogranne, R., & Derouet, L. (2025) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1109/iccai66501.2025.00016
Introduction to Unmanned Aerial Vehicles Swarm and Smart Cities
Taj, S., Ma, X., Liu, Y., Khan, R., Cheng, X., Hassan, H., Al‐Khasawneh, M. A., & Ullah, I. (2025) · Apress eBooks · doi:10.1007/979-8-8688-1047-3_1
Comparative Study on Deep Learning Algorithms for Drain Quality Monitoring
Sunil, S., & Megalingam, R. K. (2025) · Algorithms for intelligent systems · doi:10.1007/978-981-96-0228-5_23
Retracted: Anomaly Detection in Industrial Quality Control with Computer Vision and Deep Learning
Mary, A. J., Umesh, V., Khairnar, P. N., Sahana, R., Sangeethaa, S. N., & Shanthi, H. J. (2024) · International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC) · doi:10.1109/icdscnc62492.2024.10939633
“In their study, Koch et al. [17] conducted a comprehensive review of the application of computer vision techniques for defect detection and condition assessment of civil infrastructure.”
Retracted
A Rapid GBDT-BiLSTM Based Damage Detection Technique in Bridges using Accurate Parameters
Babu, U. R., Patil, D. T., Qasim, J., Soni, M., Sharma, N., & Bhoopathy, V. (2024) · Asian Conference on Intelligent Technologies (ACOIT) · doi:10.1109/acoit62457.2024.10939126
“The collected photographs of the bridge can be processed using a wide variety of image analysis methods to aid in defect discovery, classification, and evaluation[16].”
Deep Internal Learning-Based Video Compressive Sensing for Spatiotemporal Super-Resolution in Vision-Based High-Frequency ShmPreprint
Wang, J., Zhu, Q., Zhang, Q., Wang, X., & Du, Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5251692
Inspection of concrete structures in the nuclear industry: an AI perspective
Ali, R. (2025) · Digital Twins, AI, and NDE for Industry Applications and Energy Systems · doi:10.1117/12.3051397
“Traditional manual (user implemented) inspection procedures for defect identification are time-consuming, error-prone, and vary between inspectors [3].”
UAS-Assisted Corrosion Detection in Steel Using Combined Human and Machine Intelligence
Das, A., & Dorafshan, S. (2025) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas65942.2025.11007918
“The most common method of structural health monitoring is the visual inspection by the trained inspectors where they have used their own judgement [1].”
Automated image acquisition system for visual inspection with positioning error correction
TANIGUCHI, T., Nakatsuji, H., & NISHIDA, I. (2025) · Journal of Advanced Mechanical Design Systems and Manufacturing · doi:10.1299/jamdsm.2025jamdsm0022
Utilizing Accelerometer Data and LSTM Model for Road Surface Detection
Van, C. N., Tran, D.-N., Duong, D. T., & Tran, D. (2025) · Lecture notes in civil engineering · doi:10.1007/978-981-96-5206-8_2
Automated Identification and Numbering of Brick in Orthoimages of Ancient City Walls with Settlement
Zou, Z., Zhao, X., Zhao, P., Qi, F., Li, J., & Jie, Y. (2025) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-6672
Asphalt pavement deformation distress evaluation using deep learning
Tofighi, A., Golroo, A., & Fahmani, M. (2025) · International Journal of Pavement Engineering · doi:10.1080/10298436.2025.2524738
“Investigating and evaluating the physical and functional condition of roads is essential to ensure the safety and usability of infrastructure (Koch et al., 2015).”
Retrospective Risk Assessment of Wakal-Taeno Road Functionality Failure Based on SNI IEC/ISO 31010:2016
Mumu, M. G., Sumajouw, M. D. J., & Wallah, S. E. (2025) · Journal Of Social Research · doi:10.55324/josr.v4i7.2684
Cybersecurity in infrastructure management
Ai, C., Zaheer, Q., Qiu, S., Atta, Z., Malik, M., Hu, W., & Shah, S. M. A. H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34017-8.00007-7
Computational techniques
Zaheer, Q., Qiu, S., Wang, W., Shah, S. M. A. H., Yamin, S., Zhang, Y., Xiao, C., Zhao, D., Wajid, S., & Zhao, Y. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34017-8.00006-5
What Predicts Broadband Usage? A Comparison of Computer Vision and Rural Urban Typology Machine Learning ModelsPreprint
Andrews, J., Hollman, A. K., & Lee, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5374619
Crack Background Removal for Improving Concrete Crack Classification Using Mask Inverted Otsu Thresholding in Structural Health Monitoring
Anggara, D. W., Husain, A. R., Rahim, M. S. M., Ibrahim, I. S., & Abdullah, A. N. H. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-97578-3_75
Dual-branch networks with texture-guided feature enhancement for intelligent recognition of concrete surface quality defects
Wang, Y., Qi, H., Liu, J., Feng, X., & Yang, Z. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.114105
Two-phase convolutional neural network at pixel level for road crack detection and segmentation
Sharma, A., & Gupta, J. (2025) · AIP conference proceedings · doi:10.1063/5.0286823
Detection and Classification of Defects on Metal Surfaces Based on a Lightweight YOLOX-Tiny COCO Network
Duarte, J., Claro, M. F., Vitoriano, P. M. A., Amaral, T. G., & Pires, V. F. (2025) · Eng—Advances in Engineering · doi:10.3390/eng6110302 · Cited by 3
“…vision-based defect detection systems have been widely researched across industries, such as transportation [3], renewable energies [4], civil infrastructure [5], hardwood flooring [6], printed circuit board [7], Mask Manufacturing Process [8], Leather products [9], and Steel products [10].”
Composite anti-disturbance control for tunnel lining inspection robots to enhance tunnel health monitoring accuracy
Gao, X., Zhong, D., & Zhou, J. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ae2060
Utilizing Transformer-Based Semantic Segmentation for Damage and Component Detection in Civil Infrastructure Inspection
Roy, R., Saravanan, T. J., & Narazaki, Y. (2025) · Journal of Infrastructure Systems · doi:10.1061/jitse4.iseng-2748
CRAFT-Net: Contour-Refining Attention and Fusion Network for Concrete Crack Segmentation
Yang, X. H., Shen, B., Liu, L., & Zhang, Z. (2025) · International Conference on Artificial Intelligence and Computer Information Technology (AICIT) · doi:10.1109/aicit65974.2025.11282723
“Automated crack detection and segmentation in concrete structures represents a critical component of modern structural health monitoring systems, enabling early identification of potential structural failures and reducing maintenance costs [1], [2].”
Multisource Data Fusion Approach for Predicting the Deterioration of Sign Structures along Highways
Wang, X., & Bai, Y. (2025) · Journal of Infrastructure Systems · doi:10.1061/jitse4.iseng-2710
High-Resolution Pixel-Level Segmentation Algorithm for Concrete Cracks Based on Multi-Head Crossattention Mechanism
Qi, J., Wang, S., Wang, D., & Lin, L. (2025) · IEEE International Conference on Information Systems and Computer Aided Education (ICISCAE) · doi:10.1109/iciscae66104.2025.11307804
“Fracture phenomena constitute the most frequently observed degradation mode in these materials and function as vital metrics for infrastructure health assessment [1].”
Real-Time Defect Detection and Classification in Fasteners
Kaur, M., Chauhan, K. K., Gunjan, Joshi, G., Aggarwal, N., Vig, R., Ihianle, I. K., & Owa, K. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-11491-4_2
A Critical Overview on Using Image Computing Techniques for Threat Identification
Tuteja, D., Sharma, R. K., Dhand, R., & Sharma, P. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-11491-4_24
CRFPI-Net: context-aware risk feature perception and inference network for pixel-level urban traffic risk mapping
Guo, W., Xu, W., Yang, C., Zhao, Z., Gao, X., Yao, W., & Jin, S. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104299
Enhancing Infrastructure Monitoring with Calibrated Vision Language Model Ensembles: A Graffiti Detection Case Study
Evangelista, G., Lucas, H., & Kelley, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-14495-9_33
Attention Based Infrared Targets Localization Method in Power SystemPreprint
Han, Y., Yan, B., & Chen, L. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6097229
Automated surface damage detection with an embodied intelligence robotic platform
Altinay, K., Ye, Z., Mitoulis, S.-A., & Ninić, J. (2026) · Data-Centric Engineering · doi:10.1017/dce.2025.10033
“Civil infrastructures, geotechnical structures, transport, and utility networks play a crucial role in supporting modern urban and industrial systems (Koch et al., 2015; Gamra et al., 2024; Babanagar et al., 2025).”
A framework based on deep learning and the intelligent sensors for pavement assessment condition
Altabey, W. A. (2025) · Sound&Vibration · doi:10.59400/sv3458
“…and research on asphalt pavements in the national highway network, comprehensively collected performance data of typical asphalt pavements in China, and conducted in-depth research on the main influencing factors and changing patterns of long-term performance of asphalt pavements in China [12,13].”
Semi-Supervised Crack Segmentation on Large-Scale Infrastructure Surfaces via Dual-Branch Contrastive Learning
Zou, Y., Xu, T., Wang, Y., Zhu, X., Xu, M., & Wang, C. (2025) · IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES) · doi:10.1109/ieses66335.2025.11359840
“With the rapid advancement of smart city technologies, intelligent inspection systems have become a cornerstone in the operation and maintenance of civil infrastructure [1], [2].”
A Point Cloud-Based Multiscale Fusion Segmentation Model for Surface Pore Structures in 3-D Printed Concrete
Ma, Z., Li, B., Deng, R., Shang, J., Cao, B., Bai, H., Song, L., & Yang, W. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3659673
“They may struggle to analyze more complex structures, geometries, and materials [30], [31].”
Cross‐Modal Fusion for 3D Point Cloud and 2D Images in Bridge Defect Inspection
Xiong, W., Ma, X., Zhu, Y., Cai, C. S. S., & Jiang, M. (2026) · Structural Control and Health Monitoring · doi:10.1155/stc/8695692
CrackGAN: Generative adversarial networks based predictive visualization mechanism of crack propagation in RC bridge elements
Khan, W. A., Asad, R. B., Arif, F., Khan, A.-U.-R., & Fareed, S. (2026) · Advances in Structural Engineering · doi:10.1177/13694332261433703
“Despite its durability, concrete structures are vulnerable to degradation over time due to factors including overloading, aggressive environmental exposure, and material aging (Koch et al., 2015; Han et al., 2022 & Laxman et al., 2023).”
An image enhancement processing algorithm for robotic dogs in concrete surface defect inspection
Yang, F., Liu, Y., Liu, F., & Ding, N. (2026) · Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems · doi:10.1117/12.3097364
“Traditional manual inspection methods are not only inefficient and hazardous but also heavily reliant on the subjective experience of engineers, making it difficult to achieve objective and quantifiable precision [4,5].”
A vision-based water leakage detection method using a quadruped robot for concrete structures
Shao, J., Liu, Y., & Xun, J. (2026) · Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems · doi:10.1117/12.3089444
“In recent years, with advancements in computer vision technology, vision-based water leakage detection methods have garnered significant attention due to their non-contact and remotely controllable advantages [3,4].”
A Novel Image-Based Forensic Framework for Concrete in Historical and Modern Structures
Ahmad, A., Ullah, M., Plevris, V., Mir, J., & Husain, S. S. (2026) · Rilem bookseries · doi:10.1007/978-3-032-16767-5_43
VAD-MambaYOLO: improvement and application of MambaYOLO for weld detection on metal roofing systems
Bai, J., Yu, W., Xin, Z., Wang, D., & Huang, Z. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae5874
Extraction de caractéristiques sur des images acquises en contexte mobile : Application à la reconnaissance de défauts sur ouvrages d’art
Faula, Y. (2020) · theses.fr (ABES) · doi:10.70675/bffd488az2ca0z484ezb6cfz5c45dd3b9fb1
Integrating YOLOv11 and DualUNet for Precise Bridge Crack Detection and Segmentation
Wang, Z. (2026) · Journal of Hunan University Natural Sciences · doi:10.55463/issn.1674-2974.53.4.2
Container damage detection based on lightweight U-Net and vision transformer
Li, Y., Wang, J., Fang, Y., Heting, S., Hao, J., & Zhao, F. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3770
Knocking-Induced Vibration and Sound Signal Characteristics and Fractal Analysis for Distinguishing Concrete and Masonry Walls
Zhou, Y., Zhang, W., Liu, D., Li, J., Teng, S., Liang, D., Li, F., & Zhao, F. (2026) · ACS Omega · doi:10.1021/acsomega.5c13406
Deep Learning Based Crack Detection MLOps Pipeline using Incremental Learning
Rai, S., Mathur, R., Saad, A., Tonse, M. T., & Zualkernan, I. A. (2026) · International Conference on Advances in Artificial Intelligence and Machine Learning (AAIML) · doi:10.1109/aaiml67890.2026.11498221
“Traditional manual inspection methods are labor-intensive ($50–$200 per hour), produce inconsistent results due to subjective interpretation, and are inherently unscalable [2].”
Evaluación de las condiciones de infraestructura física del laboratorio de calidad fisicoquímica de los alimentos del ISER según la resolución 2674 de 2013, Artículo 6 y 7
Atilua-Bello, E. A., & Trillos-Pérez, P. J. (2026) · Respuestas · doi:10.22463/0122820x.5113
Image-based bridge surface damage comprehension using category-constrained captioning network
Li, S., Yang, R., Dang, M., Wang, K., Cui, H., & Guo, Y. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104773
Automated quantitative assessment of underwater concrete abrasion in dam stilling basins using deep learning
Lv, X., Chen, Y., Wang, H., Li, Y., & Huang, X. (2026) · Automation in Construction · doi:10.1016/j.autcon.2026.107049
Classification of building defects using ensemble machine learning models: a case study
Olanrewaju, A., & Shobowale, K. O. (2026) · Journal of Facilities Management · doi:10.1108/jfm-04-2025-0046
Detecting Beam Damage Using Machine Learning Algorithms—A Comparative Study
Ghaemifard, S., & Ghannadiasl, A. (2026) · Journal of Vibration Engineering & Technologies · doi:10.1007/s42417-026-02532-6
Research on the Design of an Automated Cover Plate Control Device for Road Depressions
Yanxin, S., Kang, Z., Xuemei, W., Wei, L., & Zhang, Y. (2026) · Actuators · doi:10.3390/act15060310
“…adverse and fluctuating climatic conditions, intrinsic variability in pavement material properties, and inconsistencies in construction quality—pavement surfaces ubiquitously exhibit surface distresses such as abrasion, cracking, and potholes, the severity of which progressively intensifies [1–3].”
CFM-Net with Multi-Scale Attention and Adaptive Fusion for Robust UAV-Based Bridge Crack Segmentation
Wang, F., He, J., Chen, X., & Mia, M. M. (2026) · Applied Sciences · doi:10.3390/app16115420
Accuracy Enhancement of Homography-Based Crack Width Calculation Using RGB-D Sensors
Zhou, S., Li, Y., Wang, S., & Narazaki, Y. (2026) · Buildings · doi:10.3390/buildings16112282
Research on road crack classification based on ResUNet semantic segmentation and projection features
Wang, Z., Han, Z., Li, Z., Ren, M., Niu, Y., & Song, S. (2026) · Intelligent geoengineering. · doi:10.1016/j.ige.2026.06.003
Camera-Only Unsupervised Visual Anomaly Detection for IEQ-Oriented Building Operations
Kim, E., Choi, J., & Suh, D. (2026) · E3S Web of Conferences · doi:10.1051/e3sconf/202671602016
A Unified Multitask Learning Framework for Automated Health Monitoring of Concrete Structures
Honarjoo, A., Amiri, G. G., Darvishan, E., & Michele, C. D. (2026) · Shock and Vibration · doi:10.1155/vib/5352622
A Comparative Study of Convolutional Neural Networks and Vision Transformers for Automated Traffic Sign Condition Assessment
Pandey, S. R., & Esteghamati, M. Z. (2026) · Intermountain Engineering, Technology and Computing (IETC) · doi:10.1109/ietc69527.2026.11568642 · Cited by 1
“Examples include post-disaster assessments [9,10], automated defect detection in bridges and pavements [11,12], and quality control during construction [13], among many others.”
Single-device on-site inspection system for non-destructive internal defect detection in trees near constructed facilities
Zhang, Y., Skibniewski, M. J., Chow, C. L., & Lau, D. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.111732
Research on the intelligent recognition method for pavement pothole diseases based on 3D point cloud technology
Huang, G., Zhao, C., Wang, Y., Zhang, X., Zhou, C., Huang, Y., Jiang, J., Ma, J., & Wang, S. (2026) · International Journal of Pavement Engineering · doi:10.1080/10298436.2026.2695147
A Structure-Aware Deep Learning Framework for Automated Bridge Inspection Integrating SegFormer-Based Structural Member Segmentation and YOLOv8 Damage Detection
Silva, S. D., & Chun, P. (2026) · Sensors · doi:10.3390/s26134255
Pavement crack segmentation with YOLOv8 using median-enhanced spatial and channel attention
Li, Q., Xia, H., Ren, J., Ren, Y., Liu, Y., Li, P., Fan, X., Zhang, M., Li, N., Shi, D., et al. (2026) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1016/j.cacaie.2026.100144

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

Overview

Integrity signals

Verification

Claims the paper makes

Citations

Cites

Cited by

Related

Metadata

Record

About these checks