Deep learning-based underground object detection for urban road pavement. Issue 13 (9th November 2020)

• Record Type: Journal Article
• Authors: Kim, Namgyu; Kim, Kideok; An, Yun-Kyu; Lee, Hyun-Jong; Lee, Jong-Jae
• Is Part Of: International journal of pavement engineering. Volume 21:Issue 13(2020)
• Page Start: 1638
• Page End: 1650
• Publication Date: 2020-11-09
• Subjects: Ground penetrating radar -- urban road -- thresholding method -- Gumbel distribution -- deep learning -- B-scan image; Pavements -- Design and construction -- Periodicals; Highway engineering -- Periodicals; 625.805
• DOI: 10.1080/10298436.2018.1559317
• Languages: English
• ISSNs: 1029-8436

Abstract

ABSTRACT: Ground penetrating radar (GPR) is a promising non-destructive evaluation technique for detecting buried underground objects in urban area. Deep learning technique is recently being applied into this field to automate the GPR data interpretation. However, there is no proper technique that can reflect the uniqueness of urban road pavements. In this study, an underground object detection technique suitable for urban road pavement is proposed by using a statistically determined threshold amplitude and a large amount of GPR B-scan image libraries. An automated thresholding technique is newly developed based on the statistical distribution of GPR data. Deep learning technique is then applied to the reconstructed GPR data to detect underground objects in urban area. The proposed method is experimentally validated by field data collected on urban roads in Seoul, South Korea. In addition, its application possibility is also tested with full-size GPR data. The proposed method successfully emphasises the feature of underground objects and classifies hyperbola, manhole cover, layer interface and subsoil background.