Toward enhancing concrete crack segmentation accuracy under complex scenarios: a novel modified U-Net network
Author:
Funder
Nanjing International joint research and development project of China
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-18568-3.pdf
Reference48 articles.
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2. Yamaguchi T, Hashimoto S (2009) Fast crack detection method for large-size concrete surface images using percolation-based image processing. Inter Mach Visi Appli 21(5):797–809. https://doi.org/10.1007/s00138-009-0189-8
3. Nhat-Duc H, Nguyen Q-L, Tran V-D (2018) Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network. Auto Constr 94:203–213. https://doi.org/10.1016/j.autcon.2018.07.008
4. Arena A, Delle Piane C, Sarout J (2014) A new computational approach to cracks quantification from 2D image analysis: Application to micro-cracks description in rocks. Comp Geos 66:106–120. https://doi.org/10.1016/j.cageo.2014.01.007
5. Dorafshan S, Thomas RJ, Maguire M (2018) Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Constr Build Mater 186:1031–1045. https://doi.org/10.1016/j.conbuildmat.2018.08.011
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