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
Morphological Image Analysis
Soille, P. (2004) · Springer eBooks · doi:10.1007/978-3-662-05088-0 · Cited by 1,065
Automatic measurement of sister chromatid exchange frequency.
Zack, G. W., Rogers, W. E., & Latt, S. A. (1977) · Journal of Histochemistry & Cytochemistry · doi:10.1177/25.7.70454 · Cited by 957
Analysis of Edge-Detection Techniques for Crack Identification in Bridges
Abdel-Qader, I. M., Abudayyeh, O. Y., & Kelly, M. E. (2003) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)0887-3801(2003)17:4(255 · Cited by 894
Digital Image Processing
Jähne, B. (1997) · Springer eBooks · doi:10.1007/978-3-662-03477-4 · Cited by 729
Pothole detection in asphalt pavement images
Koch, C., & Brilakis, I. (2011) · Advanced Engineering Informatics · doi:10.1016/j.aei.2011.01.002 · Cited by 590
Self-citation
Report Card for America's Infrastructure
Gang, S.-H. (2012) · Construction Engineering and Management · Cited by 553
Fast crack detection method for large-size concrete surface images using percolation-based image processing
Yamaguchi, T., & Hashimoto, S. (2009) · Machine Vision and Applications · doi:10.1007/s00138-009-0189-8 · Cited by 414
Image-based retrieval of concrete crack properties for bridge inspection
Adhikari, R. S., Moselhi, O., & Bagchi, A. (2013) · Automation in Construction · doi:10.1016/j.autcon.2013.06.011 · Cited by 412
Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel
Yu, S.-N., Jang, J., & Han, C.-S. (2006) · Automation in Construction · doi:10.1016/j.autcon.2006.05.003 · Cited by 364
Image analysis and mathematical morphology
O'Connor, C. (1987) · Computers & Mathematics with Applications · doi:10.1016/0898-1221(87)90243-4 · Cited by 351
Pavement crack detection using the Gabor filter
Salman, M., Mathavan, S., Kamal, K., & Rahman, M. M. (2013) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2013.6728529 · Cited by 312
Automated detection of cracks in buried concrete pipe images
Sinha, S. K., & Fieguth, P. (2005) · Automation in Construction · doi:10.1016/j.autcon.2005.02.006 · Cited by 281
Self-citation
Image‐Based Crack Detection for Real Concrete Surfaces
Yamaguchi, T., Nakamura, S., Saegusa, R., & Hashimoto, S. (2007) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.20244 · Cited by 271
Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures
Jahanshahi, M. R., & Masri, S. F. (2012) · Automation in Construction · doi:10.1016/j.autcon.2011.11.018 · Cited by 269
An innovative methodology for detection and quantification of cracks through incorporation of depth perception
Jahanshahi, M. R., Masri, S. F., Padgett, C. W., & Sukhatme, G. S. (2011) · Machine Vision and Applications · doi:10.1007/s00138-011-0394-0 · Cited by 264
Bridge inspection robot system with machine vision
Oh, J.-K., Jang, G., Oh, S., Lee, J. H., Yi, B.-J., Moon, Y. S., Lee, J. S., & Choi, Y. (2009) · Automation in Construction · doi:10.1016/j.autcon.2009.04.003 · Cited by 258
Tunnel maintenance in Japan
Asakura, T., & Kojima, Y. (2003) · Tunnelling and Underground Space Technology · doi:10.1016/s0886-7798(03)00024-5 · Cited by 243
Critical Assessment of Pavement Distress Segmentation Methods
Tsai, Y. J., Kaul, V., & Mersereau, R. M. (2009) · Journal of Transportation Engineering · doi:10.1061/(asce)te.1943-5436.0000051 · Cited by 235
Wavelet-based pavement distress detection and evaluation
Zhou, J. X., Huang, P. S., & Chiang, F.-P. (2006) · Optical Engineering · doi:10.1117/1.2172917 · Cited by 232
Automatic inspection of pavement cracking distress
Huang, Y., & Xu, B. (2006) · Journal of Electronic Imaging · doi:10.1117/1.2177650 · Cited by 214
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Bovik, A. C. (2005) · Academic Press, Inc. eBooks · Cited by 205
Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments
Paal, S. G., Brilakis, I., & DesRoches, R. (2012) · Advanced Engineering Informatics · doi:10.1016/j.aei.2012.06.005 · Cited by 202
A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance
Lim, R. S., La, H. M., & Sheng, W. (2014) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2013.2294687 · Cited by 202
Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation
Zhu, Z., Paal, S. G., & Brilakis, I. (2011) · Automation in Construction · doi:10.1016/j.autcon.2011.03.004 · Cited by 197
Condition assessment of the buried utility service infrastructure
Hao, T., Rogers, C. D. F., Metje, N., Chapman, D. N., Muggleton, J. M., Foo, K. Y., Wang, P., Pennock, S. R., Atkins, P. R., Swingler, S. G., et al. (2012) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2011.10.011 · Cited by 194
A Method for Crack Detection on a Concrete Structure
Fujita, Y., Mitani, Y., & Hamamoto, Y. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.98 · Cited by 192
PCA-Based algorithm for unsupervised bridge crack detection
Abdel-Qader, I. M., Pashaie-Rad, S., Abudayyeh, O. Y., & Yehia, S. A. (2006) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2006.06.002 · Cited by 191
A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures
Jahanshahi, M. R., Kelly, J. S., Masri, S. F., & Sukhatme, G. S. (2008) · Structure and Infrastructure Engineering · doi:10.1080/15732470801945930 · Cited by 190
Image-Based Automated 3D Crack Detection for Post-disaster Building Assessment
Torok, M. M., Golparvar‐Fard, M., & Kochersberger, K. B. (2013) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000334 · Cited by 187
A real-time 3D scanning system for pavement distortion inspection
Li, Q., Yao, M., Yao, X., & Xu, B. (2009) · Measurement Science and Technology · doi:10.1088/0957-0233/21/1/015702 · Cited by 181
State of the art in sensor technologies for sewer inspection
Duran, O., Althoefer, K. A., & Seneviratne, L. D. (2002) · IEEE Sensors Journal · doi:10.1109/jsen.2002.1000245 · Cited by 174
Automated Pothole Distress Assessment Using Asphalt Pavement Video Data
Koch, C., Jog, G. M., & Brilakis, I. (2012) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000232 · Cited by 173
Self-citation
Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine
Halfawy, M. R., & Hengmeechai, J. (2013) · Automation in Construction · doi:10.1016/j.autcon.2013.10.012 · Cited by 160
Unsupervised Approach for Autonomous Pavement-Defect Detection and Quantification Using an Inexpensive Depth Sensor
Jahanshahi, M. R., Jazizadeh, F., Masri, S. F., & Becerik-Gerber, B. (2012) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000245 · Cited by 160
Infrastructure Condition Prediction Models for Sustainable Sewer Pipelines
Chughtai, F., & Zayed, T. M. (2008) · Journal of Performance of Constructed Facilities · doi:10.1061/(asce)0887-3828(2008)22:5(333 · Cited by 159
Automated defect detection for sewer pipeline inspection and condition assessment
Guo, W. L., Soibelman, L., & Garrett, J. H. (2009) · Automation in Construction · doi:10.1016/j.autcon.2008.12.003 · Cited by 155
Modeling the structural deterioration of urban drainage pipes: the state-of-the-art in statistical methods
Ana, E., & Bauwens, W. (2010) · Urban Water Journal · doi:10.1080/15730620903447597 · Cited by 151
Automated Pavement Distress Collection Techniques
University of Washington (2004) · Transportation Research Board eBooks · doi:10.17226/23348 · Cited by 143
Method for Automated Assessment of Potholes, Cracks and Patches from Road Surface Video Clips
Lokeshwor, H., Das, L. K., & Sud, S. K. (2013) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2013.11.124 · Cited by 143
Assessment technologies for sewer system rehabilitation
Wirahadikusumah, R. D., Abraham, D. M., Iseley, T., & Prasanth, R. (1998) · Automation in Construction · doi:10.1016/s0926-5805(97)00071-x · Cited by 138
Achievements and Challenges in Machine Vision-Based Inspection of Large Concrete Structures
Koch, C., Paal, S. G., Rashidi, A., Zhu, Z., König, M., & Brilakis, I. (2014) · Advances in Structural Engineering · doi:10.1260/1369-4332.17.3.303 · Cited by 136
Self-citation
Detection of Pavement Distresses Using 3D Laser Scanning Technology
Chang, K.-T., Chang, J.-R., & Liu, J. K. (2005) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/40794(179)103 · Cited by 132
A new methodology for non-contact accurate crack width measurement through photogrammetry for automated structural safety evaluation
Jahanshahi, M. R., & Masri, S. F. (2013) · Smart Materials and Structures · doi:10.1088/0964-1726/22/3/035019 · Cited by 129
Segmentation of buried concrete pipe images
Sinha, S. K., & Fieguth, P. (2005) · Automation in Construction · doi:10.1016/j.autcon.2005.02.007 · Cited by 124
Self-citation
Automated diagnosis of sewer pipe defects based on machine learning approaches
Yang, M., & Su, T. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.08.013 · Cited by 122
Neuro-fuzzy network for the classification of buried pipe defects
Sinha, S. K., & Fieguth, P. (2005) · Automation in Construction · doi:10.1016/j.autcon.2005.02.005 · Cited by 121
Self-citation
Monitoring Crack Changes in Concrete Structures
Sohn, H., Lim, Y.-M., Yun, K.-H., & Kim, G. (2004) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/j.1467-8667.2005.00376.x · Cited by 115
Integration of computer imaging and sensor data for structural health monitoring of bridges
Zaurín, R., & Çatbaş, F. N. (2009) · Smart Materials and Structures · doi:10.1088/0964-1726/19/1/015019 · Cited by 114
Automatic pavement distress detection system
Cheng, H.-D., & Miyojim, M. (1998) · Information Sciences · doi:10.1016/s0020-0255(97)10062-7 · Cited by 114
Automated Pipe Defect Detection and Categorization Using Camera/Laser-Based Profiler and Artificial Neural Network
Duran, O., Althoefer, K. A., & Seneviratne, L. D. (2007) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2006.873225 · Cited by 111
Vision for road inspection
Varadharajan, S., Jose, S., Sharma, K., Wander, L., & Mertz, C. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836111 · Cited by 106
Experimentation of 3D Pavement Imaging through Stereovision
Hou, Z., Wang, K. C. P., & Gong, W. (2007) · First International Conference on Transportation Engineering · doi:10.1061/40932(246)62 · Cited by 101
Neuro-Fuzzy Approaches for Sanitary Sewer Pipeline Condition Assessment
Chae, M. J., & Abraham, D. M. (2001) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)0887-3801(2001)15:1(4 · Cited by 99
Detection of large-scale concrete columns for automated bridge inspection
Zhu, Z., Paal, S. G., & Brilakis, I. (2010) · Automation in Construction · doi:10.1016/j.autcon.2010.07.016 · Cited by 99
PIRAT—A System for Quantitative Sewer Pipe Assessment
Kirkham, R., Kearney, P. D., Rogers, K. J., & Mashford, J. (2000) · The International Journal of Robotics Research · doi:10.1177/02783640022067959 · Cited by 97
Primitive‐Based Classification of Pavement Cracking Images
Koutsopoulos, H. N., & Downey, A. B. (1993) · Journal of Transportation Engineering · doi:10.1061/(asce)0733-947x(1993)119:3(402 · Cited by 96
Expert System for Prioritizing the Inspection of Sewers: Knowledge Base Formulation and Evaluation
Hahn, M. A., Palmer, R. N., Merrill, M. S., & Lukas, A. B. (2002) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)0733-9496(2002)128:2(121 · Cited by 92
Pipe inspection using a laser-based transducer and automated analysis techniques
Duran, O., Althoefer, K. A., & Seneviratne, L. D. (2003) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2003.816809 · Cited by 90
Computer-vision based crack detection and analysis
Prasanna, P., Dana, K., Gucunski, N., & Basily, B. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.915384 · Cited by 85
Automatic Detection and Classification of Defect on road Pavement using Anisotropy Measure
Nguyen, T. S., Avila, M., & Bégot, S. (2009) · Cited by 84
Experiences with the development of a robot for smart multisensoric pipe inspection
Kuntze, H.-B., & Haffner, H. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1998.677423 · Cited by 79
Multi-image stitching and scene reconstruction for evaluating defect evolution in structures
Jahanshahi, M. R., Masri, S. F., & Sukhatme, G. S. (2011) · Structural Health Monitoring · doi:10.1177/1475921710395809 · Cited by 74
Robust Automated Concrete Damage Detection Algorithms for Field Applications
Lattanzi, D., & Miller, G. R. (2012) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000257 · Cited by 73
Application of Morphological Segmentation to Leaking Defect Detection in Sewer Pipelines
Su, T., & Yang, M. (2014) · Sensors · doi:10.3390/s140508686 · Cited by 69
Ultrasonic signal processing methods for detection of defects in concrete pipes
Iyer, S., Sinha, S. K., Tittmann, B. R., & Pedrick, M. K. (2011) · Automation in Construction · doi:10.1016/j.autcon.2011.06.012 · Cited by 67
Image-Based Framework for Concrete Surface Crack Monitoring and Quantification
Chen, Z., & Hutchinson, T. C. (2010) · Advances in Civil Engineering · doi:10.1155/2010/215295 · Cited by 67
Automatic Pavement Crack Recognition Based on BP Neural Network
Li, L., Sun, L., Ning, G., & Tan, S.-G. (1970) · PROMET - Traffic&Transportation · doi:10.7307/ptt.v26i1.1477 · Cited by 62
Challenges and Feasibility for Comprehensive Automated Survey of Pavement Conditions
Wang, K. C. P. (2004) · doi:10.1061/40730(144)99 · Cited by 61
Efficient Algorithm for Crack Detection in Sewer Images from Closed-Circuit Television Inspections
Halfawy, M. R., & Hengmeechai, J. (2013) · Journal of Infrastructure Systems · doi:10.1061/(asce)is.1943-555x.0000161 · Cited by 59
Automated pavement distress detection using advanced image processing techniques
Sun, Y., Salari, E., & Chou, E. (2009) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2009.5189645 · Cited by 57
Evaluation of ultrasonic inspection and imaging systems for concrete pipes
Iyer, S., Sinha, S. K., Pedrick, M. K., & Tittmann, B. R. (2011) · Automation in Construction · doi:10.1016/j.autcon.2011.06.013 · Cited by 55
Tunnel crack detection and classification system based on image processing
Liu, Z., Suandi, S. A., Ohashi, T., & Ejima, T. (2002) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.460191 · Cited by 54
Detection and avoidance of simulated potholes in autonomous vehicle navigation in an unstructured environment
Karuppuswamy, J., Selvaraj, V., Ganesh, M. M., & Hall, E. L. (2000) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.403788 · Cited by 50
Robust Method for Automated Segmentation of Frames with/without Distress from Road Surface Video Clips
Lokeshwor, H., Das, L. K., & Goel, S. (2013) · Journal of Transportation Engineering · doi:10.1061/(asce)te.1943-5436.0000564 · Cited by 49
Real-Time Automated Survey System of Pavement Cracking in Parallel Environment
Wang, K. C. P., & Gong, W. (2005) · Journal of Infrastructure Systems · doi:10.1061/(asce)1076-0342(2005)11:3(154 · Cited by 47
Evaluation Of Pavement Surface Distress Using Digital Image Collection And Analysis
Cafiso, S. D., Graziano, A. D., & Battiato, S. (2006) · Cited by 41
Evolution of an automated crack sealer: a study in construction technology development
Haas, C. T. (1996) · Automation in Construction · doi:10.1016/0926-5805(95)00010-0 · Cited by 35
Probabilistic modeling of sewer deterioration using inspection data
Dirksen, J., & Clemens, F. H. L. R. (2008) · Water Science & Technology · doi:10.2166/wst.2008.308 · Cited by 34
Modeling of Crack Depths in Digital Images of Concrete Pavements Using Optical Reflection Properties
Amarasiri, S., Gunaratne, M., & Sarkar, S. (2009) · Journal of Transportation Engineering · doi:10.1061/(asce)te.1943-5436.0000095 · Cited by 29
Vision based condition assessment of structures
Uhl, T., Kohut, P., Holak, K., & Krupinski, K. (2011) · Journal of Physics Conference Series · doi:10.1088/1742-6596/305/1/012043 · Cited by 27
Chronological development history of X–Y table based pavement crack sealers and research findings for practical use in the field
Kim, Y., Yoo, H. S., Lee, J. H., & Han, S. W. (2009) · Automation in Construction · doi:10.1016/j.autcon.2009.02.007 · Cited by 26
A Study of Image-Based Element Condition Index for Bridge Inspection
Adhikari, R. S., Moselhi, O., & Bagchi, A. (2013) · Proceedings of the ... ISARC · doi:10.22260/isarc2013/0038 · Cited by 25
Review of Physical Based Monitoring Techniques for Condition Assessment of Corrosion in Reinforced Concrete
Lei, Y., & Zheng, Z. (2013) · Mathematical Problems in Engineering · doi:10.1155/2013/953930 · Cited by 23
Damage detection and monitoring for tunnel inspection based on computer vision
Chaiyasarn, K. (2014) · Apollo (University of Cambridge) · doi:10.17863/cam.14071 · Cited by 21
Optical flow techniques for estimation of camera motion parameters in sewer closed circuit television inspection videos
Halfawy, M. R., & Hengmeechai, J. (2013) · Automation in Construction · doi:10.1016/j.autcon.2013.10.016 · Cited by 21
Image mosaicing via quadric surface estimation with priors for tunnel inspection
Chaiyasarn, K., Kim, T., Viola, F., Cipolla, R., & Soga, K. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5413902 · Cited by 19
Technology for Measurement and Diagnosis. Development of Image Processing Technique for Detection of Tunnel Wall Deformation Using Continuously Scanned Image.
Masato, U. K. A. I. (2000) · Quarterly Report of RTRI · doi:10.2219/rtriqr.41.120 · Cited by 18
Optical crack following on tunnel surfaces
Paar, G., Caballo-Perucha, M., Kontrus, H., & Sidla, O. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.685987 · Cited by 16
Shape Measurement of a Sewer Pipe Using a Mobile Robot with Computer Vision
Kawasue, K., & Komatsu, T. (2013) · International Journal of Advanced Robotic Systems · doi:10.5772/55261 · Cited by 15
Research on Automatic Identification Method of Pavement Sag Deformation
Wang, J., Ma, S., & Jiang, L. (2009) · doi:10.1061/41064(358)386 · Cited by 12
Supervised Computer-Vision-Based Sensing of Concrete Bridges for Crack-Detection and Assessment
Rose, P., Aaron, B., Tamir, D. E., Lu, L., Hu, J., & Shi, H. (2014) · Transportation Research Board 93rd Annual MeetingTransportation Research Board · Cited by 11
Micro-measurement and monitoring system for ageing underground infrastructure (UndergroundM3)
Soga, K., Ledesma, A., Roncaglia, A., & Vaníček, I. (2012) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 9
Positioning and Imaging Sensors for Automated Asset Management of Transportation Facilities
Wang, K. C. P. (2007) · First International Conference on Transportation Engineering · doi:10.1061/40932(246)4 · Cited by 7
I-35W Bridge Collapse and Response, Minneapolis, Minnesota, August 1, 2007
Stambaugh, H., & Cohen, H. (2008) · Cited by 4
Micro-measurement and monitoring system for aging underground infrastructure (Underground M3)
Soga, K., & Bennett, P. (2008) · CRC Press eBooks · doi:10.1201/9781439828441.ch23 · Cited by 3
Computer vision for automated surface evaluation of concrete bridge decks
Prasanna, P. (2013) · Rutgers University Community Repository (Rutgers University) · doi:10.7282/t3kk99c9 · Cited by 2

Flagged first, then by citations

1 of the 107 references couldn’t be matched to a known paper.

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

Roles of Artificial Intelligence in Construction Engineering and Management: A Critical Review and Future Trends
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring
Spencer, B. F., Hoskere, V., & Narazaki, Y. (2019) · Engineering · doi:10.1016/j.eng.2018.11.030 · Cited by 1,273
Autonomous concrete crack detection using deep fully convolutional neural network
Dung, C. V., & Anh, L. D. (2019) · Automation in Construction · doi:10.1016/j.autcon.2018.11.028 · Cited by 1,170
DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection
Zou, Q., Zhang, Z., Li, Q., Qi, X., Wang, Q., & Wang, S. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2878966 · Cited by 1,122
Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection
Gopalakrishnan, K., Khaitan, S. K., Choudhary, A., & Agrawal, A. (2017) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2017.09.110 · Cited by 981

See all 1,065

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