The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
DeepCrack: A deep hierarchical feature learning architecture for crack segmentation
Abstract
Abstract Automatic crack detection from images of various scenes is a useful and challenging task in practice. In this paper, we propose a deep hierarchical convolutional neural network (CNN), called as DeepCrack, to predict pixel-wise crack segmentation in an end-to-end method. DeepCrack consists of the extended Fully Convolutional Networks (FCN) and the Deeply-Supervised Nets (DSN). During the training, the elaborately designed model learns and aggregates multi-scale and multi-level features from the low convolutional layers to the high-level convolutional layers, which is different from the standard approaches of only using the last convolutional layer. DSN provides integrated direct supervision for features of each convolutional stage. We apply both guided filtering and Conditional Random Fields (CRFs) methods to refine the final prediction results. A benchmark dataset consisting of 537 images with manual annotation maps are built to verify the effectiveness of our proposed method. Our method achieved state-of-the-art performances on the proposed dataset (mean I/U of 85.9, best F-score of 86.5, and 0.1 s per image).
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Cites
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Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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Fully convolutional networks for semantic segmentation
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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
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Cited by
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
Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review
Flah, M., Nunez, I., Ben Chaabene, W., & Nehdi, M. L. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09471-9 · Cited by 533
SDDNet: Real-Time Crack Segmentation
Choi, W., & Cha, Y.-J. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2945265 · Cited by 462
Dais, D., Bal, İ. E., Smyrou, E., & Sarhosis, V. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103606 · Cited by 429
Machine Learning for Crack Detection: Review and Model Performance Comparison
Hsieh, Y.-A., & Tsai, Y. J. (2020) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000918 · Cited by 420
Metadata
- Authors
- Liu, Y., Yao, J., Lu, X., Xie, R., & Li, L.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 338
- Pages
- 139-153
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2912350898
- Cited by
- 1,158
- References
- 50
- FWCI
- 48.21
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liu, Y., Yao, J., Lu, X., Xie, R., & Li, L. (2019). DeepCrack: A deep hierarchical feature learning architecture for crack segmentation. Neurocomputing. https://doi.org/10.1016/j.neucom.2019.01.036
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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