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Proceedings Paper

Crack identification inside on-site steel box girder based on fusion convolutional neural network
Author(s): Yang Xu; Hui Li; Jiahui Chen
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Paper 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.

Paper Details

Date Published: 27 March 2018
PDF: 6 pages
Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 105981K (27 March 2018); doi: 10.1117/12.2298390
Show Author Affiliations
Yang Xu, Ministry of Industrial and Information Technology (China)
Ministry of Education (China)
Harbin Institute of Technology (China)
Hui Li, Ministry of Industrial and Information Technology (China)
Ministry of Education (China)
Harbin Institute of Technology (China)
Jiahui Chen, Ministry of Industrial and Information Technology (China)
Ministry of Education (China)
Harbin Institute of Technology (China)


Published in SPIE Proceedings Vol. 10598:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018
Hoon Sohn, Editor(s)

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