@inproceedings{ce5eb5de9e2949cda5b76c0167c7a15e,
title = "Crack identification inside on-site steel box girder based on fusion convolutional neural network",
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.",
keywords = "Crack identification, convolutional neural network, multiscale and multilevel features, steel box girder",
author = "Yang Xu and Hui Li and Jiahui Chen",
note = "Publisher Copyright: {\textcopyright} 2018 SPIE.; Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018 ; Conference date: 05-03-2018 Through 08-03-2018",
year = "2018",
doi = "10.1117/12.2298390",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Kon-Well Wang and Hoon Sohn and Lynch, \{Jerome P.\}",
booktitle = "Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018",
address = "美国",
}