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http://dx.doi.org/10.5573/ieie.2017.54.6.80

Crack Detection in Tunnel Using Convolutional Encoder-Decoder Network  

Han, Bok Gyu (Dept. Computer Science & Engineering., Hanyang University)
Yang, Hyeon Seok (Dept. Computer Science & Engineering., Hanyang University)
Lee, Jong Min (Dept. Computer Science & Engineering., Hanyang University)
Moon, Young Shik (Dept. Computer Science & Engineering., Hanyang University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.54, no.6, 2017 , pp. 80-89 More about this Journal
Abstract
The classical approaches to detect cracks are performed by experienced inspection professionals by annotating the crack patterns manually. Because of each inspector's personal subjective experience, it is hard to guarantee objectiveness. To solve this issue, automated crack detection methods have been proposed however the methods are sensitive to image noise. Depending on the quality of image obtained, the image noise affect overall performance. In this paper, we propose crack detection method using a convolutional encoder-decoder network to overcome these weaknesses. Performance of which is significantly improved in terms of the recall, precision rate and F-measure than the previous methods.
Keywords
Crack detection; Tunnel lining; Convolutional neural network; Deconvolutional network;
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Times Cited By KSCI : 3  (Citation Analysis)
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