Deep learning-based automated underground cavity detection using three-dimensional ground penetrating radar. (January 2020)

• Record Type: Journal Article
• Authors: Kang, Man-Sung; Kim, Namgyu; Lee, Jong Jae; An, Yun-Kyu
• Is Part Of: Structural health monitoring. Volume 19:Number 1(2020)
• Page Start: 173
• Page End: 185
• Publication Date: 2020-01
• Subjects: Ground penetrating radar -- deep convolutional neural network -- underground object detection -- signal processing -- basis pursuit; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/1475921719838081
• Languages: English
• ISSNs: 1475-9217

Abstract

Three-dimensional ground penetrating radar data are often ambiguous and complex to interpret when attempting to detect only underground cavities because ground penetrating radar reflections from various underground objects can appear like those from cavities. In this study, we tackle the issue of ambiguity by proposing a system based on deep convolutional neural networks, which is capable of autonomous underground cavity detection beneath urban roads using three-dimensional ground penetrating radar data. First, a basis pursuit-based background filtering algorithm is developed to enhance the visibility of underground objects. The deep convolutional neural network is then established and applied to automatically classify underground objects using the filtered three-dimensional ground penetrating radar data as represented by three types of images: A-, B-, and C-scans. In this study, we utilize a novel two-dimensional grid image consisting of several B- and C-scan images. Cavity, pipe, manhole, and intact features extracted from in situ three-dimensional ground penetrating radar data are used to train the convolutional neural network. The proposed technique is experimentally validated using real three-dimensional ground penetrating radar data obtained from urban roads in Seoul, South Korea.