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Autonomous concrete crack detection using deep fully convolutional neural network
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“The network achieved about 90% average precision on a subset of 500 annotated 227 × 227-pixel crack-labeled images.”
From An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets (2021) · cites this paper as [23]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 44,708
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
Contour Detection and Hierarchical Image Segmentation
Arbeláez, P., Maire, M., Fowlkes, C. C., & Malik, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.161 · Cited by 5,581
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
Cha, Y., Choi, W., & Büyüköztürk, O. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12263 · Cited by 3,295
Conditional Random Fields as Recurrent Neural Networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.179 · Cited by 2,386
Road crack detection using deep convolutional neural network
Zhang, L., Yang, F., Zhang, Y. D., & Zhu, Y. J. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7533052 · Cited by 1,655
Cha, Y., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12334 · Cited by 1,558
Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images
Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., & Omata, H. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12387 · Cited by 1,124
Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., & Fieguth, P. (2015) · Advanced Engineering Informatics · doi:10.1016/j.aei.2015.01.008 · Cited by 1,065
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
Chen, F., & Jahanshahi, M. R. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2764844 · Cited by 949
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
Teichmann, M., Weber, M., Zollner, M., Cipolla, R., & Urtasun, R. (2018) · 2018 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2018.8500504 · Cited by 681
Class segmentation and object localization with superpixel neighborhoods
Fulkerson, B., Vedaldi, A., & Soatto, S. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459175 · Cited by 657
Associative hierarchical CRFs for object class image segmentation
Ladický, Ľ., Russell, C., Kohli, P., & Torr, P. H. S. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459248 · Cited by 607
Automated Crack Detection on Concrete Bridges
Prasanna, P., Dana, K., Gucunski, N., Basily, B., La, H. M., Lim, R. S., & Parvardeh, H. (2014) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2014.2354314 · Cited by 482
Computer Vision (ICCV), 2011 IEEE International Conference on
Vezhnevets, A., Ferrari, V., & Buhmann, J. M. (2011) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 464
2018 IEEE Intelligent Vehicles Symposium (IV)
(2018) · doi:10.1109/iv43497.2018 · Cited by 428
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
3D Traffic Scene Understanding From Movable Platforms
Geiger, A., Lauer, M., Wojek, C., Stiller, C., & Urtasun, R. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.185 · Cited by 397
Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
Ferrari, V., Marín-Jiménez, M. J., & Zisserman, A. (2008) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 397
Fast image scanning with deep max-pooling convolutional neural networks
Giusti, A., Ciresan, D. C., Masci, J., Gambardella, L. M., & Schmidhuber, J. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738831 · Cited by 322
Road Damage Detection Using Deep Neural Networks with Images Captured Through a SmartphonePreprint
Maeda, H., Sekimoto, Y., Seto, T., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.09454 · Cited by 250
Zhang, K., Cheng, H.-D., & Zhang, B. (2018) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000736 · Cited by 237
Fan, Z., Wu, Y., Lu, J., & Li, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.02208 · Cited by 234
Deeper Networks for Pavement Crack Detection
Pauly, L., Peel, H., Luo, S., Hogg, D., & Fuentes, R. (2017) · International Symposium on Automation and Robotics in Construction (ISARC) · doi:10.22260/isarc2017/0066 · Cited by 173
2017 Chinese Automation Congress (CAC)
(2017) · doi:10.1109/cac42904.2017 · Cited by 142
Multifractal analysis of crack patterns in reinforced concrete shear walls
Ebrahimkhanlou, A., Farhidzadeh, A., & Salamone, S. (2016) · Structural Health Monitoring · doi:10.1177/1475921715624502 · Cited by 119
Computer vision-based method for concrete crack detection
Dinh, T. H., Ha, Q. P., & La, H. M. (2016) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2016.7838682 · Cited by 93
Li, H., Zhao, R., & Wang, X. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.4526 · Cited by 86
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013
Lynch, J. P., Wang, K., & Sohn, H. (2013) · SPIE eBooks · Cited by 83
Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering
Noh, Y., Koo, D., Kang, Y.-M., DongGyu, P., & Lee, D. (2017) · International Conference on Applied System Innovation · doi:10.1109/icasi.2017.7988574 · Cited by 76
Deep Learning for Asphalt Pavement Cracking Recognition Using Convolutional Neural Network
Wang, K. C. P., Zhang, A. A., Li, Q., Fei, Y., Chen, C., & Li, B. (2017) · ASCE International Airfield and Highway Pavements Conference · doi:10.1061/9780784480922.015 · Cited by 69
Davoudi, R., Miller, G. R., & Kutz, J. N. (2018) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000766 · Cited by 61
Camilo, J. A., Wang, R., Collins, L. M., Bradbury, K., & Malof, J. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.04018 · Cited by 52
Vision-Based Concrete Crack Detection Using a Convolutional Neural Network
Cha, Y., & Choi, W. (2017) · Conference proceedings of the Society for Experimental Mechanics · doi:10.1007/978-3-319-54777-0_9 · Cited by 44
Ebrahimkhanlou, A., Farhidzadeh, A., & Salamone, S. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2084052 · Cited by 37
Observations and Trends among Collapsed Bridges in New York State
Cook, W., & Barr, P. J. (2017) · Journal of Performance of Constructed Facilities · doi:10.1061/(asce)cf.1943-5509.0000996 · Cited by 37
Structural Health Monitoring (SHM)
Zinno, R., Artese, S., Clausi, G., Magarò, F., Meduri, S., Miceli, A., & Venneri, A. (2018) · Internet of things · doi:10.1007/978-3-319-96550-5_10 · Cited by 34
Vision-based concrete crack detection technique using cascade features
Cha, Y., Gopal, D. L., & Ali, R. (2018) · Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems · doi:10.1117/12.2295962 · Cited by 23
Automatic crack inspection for concrete bridge bottom surfaces based on machine vision
Zhang, H., Tan, J., Liu, L., Wu, Q. M. J., Wang, Y., & Liu, J. (2017) · Chinese Automation Congress · doi:10.1109/cac.2017.8243654 · Cited by 22
Computer Vision Based Inspection Approach to Predict Damage State and Load Level for RC Members
Davoudi, R., Miller, G. R., & Kutz, J. N. (2017) · International Workshop on Structural Health Monitoring (IWSHM) · doi:10.12783/shm2017/14225 · Cited by 13
Airfield and Highway Pavements 2017
Al‐Qadi, I. L., Özer, H., Vélez-Vega, E. M., & Murrell, S. (2017) · doi:10.1061/9780784480922 · Cited by 7
Research works printed but not linked: 1
- Sato, 2018, Crack detection on concrete surfaces using V-shaped features, World Comp. Sci. Inform. Technol. J., 8
Cited by
Sorted by citations
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
Cha, Y., Choi, W., & Büyüköztürk, O. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12263 · Cited by 3,295
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
Computer vision-based concrete crack detection using U-net fully convolutional networks
Liu, Z., Cao, Y., Wang, Y., & Wang, W. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.04.005 · Cited by 841
A review of computer vision–based structural health monitoring at local and global levels
Dong, C.-Z., & Catbas, F. (2020) · Structural Health Monitoring · doi:10.1177/1475921720935585 · Cited by 761
Azimi, M., Eslamlou, A., & Pekcan, G. (2020) · Sensors · doi:10.3390/s20102778 · Cited by 769
Metadata
- Authors
- Dung, C. V., & Anh, L. D.
- Affiliations
- Tokyo City University (JP)
- Corresponding author
- Dung, Cao Vu
- Venue
- Automation in Construction
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 99
- Pages
- 52-58
- ISSN
- 0926-5805 (ISSN-L)
- OpenAlex
- W2905163589
- Cited by
- 1,170
- References
- 58
- Citations by year
- 2026: 100 · 2025: 155 · 2024: 192 · 2023: 204 · 2022: 207 · 2021: 158 · 2020: 121 · 2019: 33
- FWCI
- 70.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Automation in Construction (publishedVersion)
- Funders
- Tokyo City University
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- concrete crack detection, semantic segmentation, fully convolutional network, encoder-decoder architecture, cyclic loading test, structural health monitoring, video image analysis
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