The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Autonomous concrete crack detection using deep fully convolutional neural network
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
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
Flagged first, then by citations
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
Items 1,001–1,001 of 1,001
Transfer learning for leveraging computer vision in infrastructure maintenancePreprint
Żarski, M., Wójcik, B., & Miszczak, J. A. (2020) · arXiv (Cornell University)
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
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.
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