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http://dx.doi.org/10.9708/jksci.2021.26.02.027

Automatic Detection System of Underground Pipe Using 3D GPR Exploration Data and Deep Convolutional Neural Networks  

Son, Jeong-Woo (Dept. of Computer Science and Engineering, Kangwon National University)
Moon, Gwi-Seong (Dept. of Computer Science and Engineering, Kangwon National University)
Kim, Yoon (Dept. of Computer Science and Engineering, Kangwon National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.2, 2021 , pp. 27-37 More about this Journal
Abstract
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%.
Keywords
Underground Pipe; GPR; Deep Learning; 3D Image Segmentation; Automatic Detection System of Underground Pipe;
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