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@article{ART002685879},
author={Son jeong woo and Gwi-Seong Moon and Kim, yoon},
title={Automatic Detection System of Underground Pipe Using 3D GPR Exploration Data and Deep Convolutional Neural Networks},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2021},
volume={26},
number={2},
pages={27-37},
doi={10.9708/jksci.2021.26.02.027}

TY - JOUR
AU - Son jeong woo
AU - Gwi-Seong Moon
AU - Kim, yoon
TI - Automatic Detection System of Underground Pipe Using 3D GPR Exploration Data and Deep Convolutional Neural Networks
JO - Journal of The Korea Society of Computer and Information
PY - 2021
VL - 26
IS - 2
PB - The Korean Society Of Computer And Information
SP - 27
EP - 37
SN - 1598-849X
AB - In this paper, we propose Automatic detection system of underground pipe which automatically detects underground pipe to help experts. Actual location of underground pipe does not match with blueprint due to various factors such as ground changes over time, construction discrepancies, etc. So, various accidents occur during excavation or just by ageing. Locating underground utilities is done through GPR exploration to prevent these accidents but there are shortage of experts, because GPR data is enormous and takes long time to analyze. In this paper, To analyze 3D GPR data automatically, we use 3D image segmentation, one of deep learning technique, and propose proper data generation algorithm. We also propose data augmentation technique and pre-processing module that are adequate to GPR data. In experiment results, we found the possibility for pipe analysis using image segmentation through our system recorded the performance of F1 score 40.4%.
KW - Underground Pipe;GPR;Deep Learning;3D Image Segmentation;Automatic Detection System of Underground Pipe
DO - 10.9708/jksci.2021.26.02.027
ER -

Son jeong woo, Gwi-Seong Moon and Kim, yoon. (2021). Automatic Detection System of Underground Pipe Using 3D GPR Exploration Data and Deep Convolutional Neural Networks. Journal of The Korea Society of Computer and Information, 26(2), 27-37.

Son jeong woo, Gwi-Seong Moon and Kim, yoon. 2021, "Automatic Detection System of Underground Pipe Using 3D GPR Exploration Data and Deep Convolutional Neural Networks", Journal of The Korea Society of Computer and Information, vol.26, no.2 pp.27-37. Available from: doi:10.9708/jksci.2021.26.02.027

Son jeong woo, Gwi-Seong Moon, Kim, yoon "Automatic Detection System of Underground Pipe Using 3D GPR Exploration Data and Deep Convolutional Neural Networks" Journal of The Korea Society of Computer and Information 26.2 pp.27-37 (2021) : 27.

Son jeong woo, Gwi-Seong Moon, Kim, yoon. Automatic Detection System of Underground Pipe Using 3D GPR Exploration Data and Deep Convolutional Neural Networks. 2021; 26(2), 27-37. Available from: doi:10.9708/jksci.2021.26.02.027

Son jeong woo, Gwi-Seong Moon and Kim, yoon. "Automatic Detection System of Underground Pipe Using 3D GPR Exploration Data and Deep Convolutional Neural Networks" Journal of The Korea Society of Computer and Information 26, no.2 (2021) : 27-37.doi: 10.9708/jksci.2021.26.02.027

Son jeong woo; Gwi-Seong Moon; Kim, yoon. Automatic Detection System of Underground Pipe Using 3D GPR Exploration Data and Deep Convolutional Neural Networks. Journal of The Korea Society of Computer and Information, 26(2), 27-37. doi: 10.9708/jksci.2021.26.02.027

Son jeong woo; Gwi-Seong Moon; Kim, yoon. Automatic Detection System of Underground Pipe Using 3D GPR Exploration Data and Deep Convolutional Neural Networks. Journal of The Korea Society of Computer and Information. 2021; 26(2) 27-37. doi: 10.9708/jksci.2021.26.02.027

Son jeong woo, Gwi-Seong Moon, Kim, yoon. Automatic Detection System of Underground Pipe Using 3D GPR Exploration Data and Deep Convolutional Neural Networks. 2021; 26(2), 27-37. Available from: doi:10.9708/jksci.2021.26.02.027

Son jeong woo, Gwi-Seong Moon and Kim, yoon. "Automatic Detection System of Underground Pipe Using 3D GPR Exploration Data and Deep Convolutional Neural Networks" Journal of The Korea Society of Computer and Information 26, no.2 (2021) : 27-37.doi: 10.9708/jksci.2021.26.02.027