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http://dx.doi.org/10.7472/jksii.2019.20.4.39

A Study on the Pipe Position Estimation in GPR Images Using Deep Learning Based Convolutional Neural Network  

Chae, Jihun (School of Computer Science and Engineering, Kyonggi University)
Ko, Hyoung-yong (School of Computer Science and Engineering, Kyonggi University)
Lee, Byoung-gil (Department of Civil Engineering, Kyonggi University)
Kim, Namgi (School of Computer Science and Engineering, Kyonggi University)
Publication Information
Journal of Internet Computing and Services / v.20, no.4, 2019 , pp. 39-46 More about this Journal
Abstract
In recently years, it has become important to detect underground objects of various marterials including metals, such as detecting the location of sink holes and pipe. For this reason, ground penetrating radar(GPR) technology is attracting attention in the field of underground detection. GPR irradiates the radar wave to find the position of the object buried underground and express the reflected wave from the object as image. However, it is not easy to interpret GPR images because the features reflected from various objects underground are similar to each other in GPR images. Therefore, in order to solve this problem, in this paper, to estimate the piping position in the GRP image according to the threshold value using the CNN (Convolutional Neural Network) model based on deep running, which is widely used in the field of image recognition, As a result of the experiment, it is proved that the pipe position is most reliably detected when the threshold value is 7 or 8.
Keywords
sink holes; pipe; GPR; image recognition; underground detection; CNN; deep-learning;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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