Figure - available from: International Journal of Machine Learning and Cybernetics
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![a Stem block. Conv denotes convolution, MaxPooling denotes Max pooling, and C denotes the concatenate operation. N represents the base number of feature map channels, which is set to 64 in the experiments. The activation function layer of the first 3×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document}3 convolution is removed. b the residual block](publication/385561059/figure/fig4/AS:11431281288784095@1730863276415/a-Stem-block-Conv-denotes-convolution-MaxPooling-denotes-Max-pooling-and-C-denotes-the.png)
a Stem block. Conv denotes convolution, MaxPooling denotes Max pooling, and C denotes the concatenate operation. N represents the base number of feature map channels, which is set to 64 in the experiments. The activation function layer of the first 3×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document}3 convolution is removed. b the residual block
Source publication
Crack detection is an important task to ensure structural safety. Traditional manual detection is extremely time-consuming and labor-intensive. However, existing deep learning-based methods also commonly suffer from low inference speed and continuous crack interruption. To solve the above problems, a novel bilateral crack detection network (BiCrack...
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