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

Jihun Chae; Hyoung-yong Ko; Byoung-gil Lee; Namgi Kim

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

Jihun Chae, Hyoung-yong Ko, Byoung-gil Lee, Namgi Kim, Journal of Internet Computing and Services, Vol. 20, No. 4, pp. 39-46, Aug. 2019

10.7472/jksii.2019.20.4.39, Full Text:

Keywords: sink holes, pipe, GPR, Image recognition, underground detection, CNN, deep-learning

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.

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Cite this article

[APA Style]

Chae, J., Ko, H., Lee, B., & Kim, N. (2019). A Study on the Pipe Position Estimation in GPR Images Using Deep Learning Based Convolutional Neural Network. Journal of Internet Computing and Services, 20(4), 39-46. DOI: 10.7472/jksii.2019.20.4.39.

[IEEE Style]

J. Chae, H. Ko, B. Lee, N. Kim, "A Study on the Pipe Position Estimation in GPR Images Using Deep Learning Based Convolutional Neural Network," Journal of Internet Computing and Services, vol. 20, no. 4, pp. 39-46, 2019. DOI: 10.7472/jksii.2019.20.4.39.

[ACM Style]

Jihun Chae, Hyoung-yong Ko, Byoung-gil Lee, and Namgi Kim. 2019. A Study on the Pipe Position Estimation in GPR Images Using Deep Learning Based Convolutional Neural Network. Journal of Internet Computing and Services, 20, 4, (2019), 39-46. DOI: 10.7472/jksii.2019.20.4.39.