Abstract
Exposed aggregate is a typical feature of the abrasion erosion in stilling basin slabs concrete surface. Although a variety of underwater robots are designed for inspection, the exposed aggregate detection for identifying abrasion is often done by manual work. The scarcity of image samples, large differences in aggregate size, color and shape are the main difficulties in automatic detection. To address this problem, an improved Attention U-Net deep fully convolutional network-based detection method was proposed. To realize this method, underwater images in site were captured via a self-developed operated underwater robot. Through randomly separating and the cropping of the 128 underwater images, the 512×512 pixels images dataset was built according to the ratio of 8:1:1, including 408 training images, 52 validation images and 52 test images. After the data augmentation, loss function and the optimizer were carefully designed and selected, the proposed Attention U-Net architecture was evaluated on this dataset. For comparative research, the full convolution network (FCN) and U-Net network were trained with the same training and validation dataset. The performance comparison on the test dataset showed that the Attention U-Net architecture has better detection accuracy.
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This research was supported by Sichuan Science and TechnologyProgram (2018JZ0001, 2018GZDZX0043, 2019YFG0143, 2019YFG0144, 2019YFS0057, 2019YJ0449).
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Li, Y., Li, X., Wang, H. et al. Exposed Aggregate Detection of Stilling Basin Slabs Using Attention U-Net Network. KSCE J Civ Eng 24, 1740–1749 (2020). https://doi.org/10.1007/s12205-020-1431-1
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DOI: https://doi.org/10.1007/s12205-020-1431-1