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- W4289688401 abstract "Automatic detection and quantification of all cracks on the surface of concrete structures are of great importance for evaluating structural safety and damage. Firstly, based on the electric driving platform, a method of close-range scanning and shooting is presented to obtain high-resolution panoramas of the surface of concrete structures. Then, based on convolutional neural networks, an automatic segmentation method suitable for panorama cracks is proposed. Without considering complex scenes, the method achieved crack segmentation at 51.34 frames per second for 512 × 512 images, with 97.79% mean intersection-over-union. Finally, an automatic quantification method suitable for panorama cracks is given through proposed crack matching and property calculation methods. The properties and corresponding distribution of panorama cracks, including branch cracks, are calculated automatically. The average relative error of the maximum crack width value calculated by the proposed crack width calculation method is merely 3.87% (22.57 μm), indicating high accuracy." @default.
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- W4289688401 date "2022-08-01" @default.
- W4289688401 modified "2023-10-11" @default.
- W4289688401 title "Automatic segmentation and quantification of global cracks in concrete structures based on deep learning" @default.
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- W4289688401 doi "https://doi.org/10.1016/j.measurement.2022.111550" @default.
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