27 March 2018 Crack identification inside on-site steel box girder based on fusion convolutional neural network
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Abstract
In this paper we propose a novel fusion convolutional neural network to identify the local fatigue cracks in steel box girder of cable-stayed bridge. Unlike conventional CNN’s chain-like structure, the proposed network fully exploits multiscale and multilevel information of input images by combining all the meaningful convolutional features together. Raw images with high resolution of 3624×4928 are decomposed into three kinds of sub-image sets with lower resolution of 64×64, background, handwriting and crack, respectively. Multi-functional layers are stacked including convolution, ReLU, softmaxResults show that the test error drops to 4% after only 50 epochs and it is more effective compared with other deep learning networks when handling large image datasets.
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Yang Xu, Hui Li, Jiahui Chen, "Crack identification inside on-site steel box girder based on fusion convolutional neural network", Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 105981K (27 March 2018); doi: 10.1117/12.2298390; https://doi.org/10.1117/12.2298390
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