期刊
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
卷 21, 期 13, 页码 1638-1650出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/10298436.2018.1559317
关键词
Ground penetrating radar; urban road; thresholding method; Gumbel distribution; deep learning; B-scan image
类别
资金
- Transportation & Logistics Research Program (TLRP) - Ministry of Land, Infrastructure and Transport of Korean government [18TLRP-C099510-04]
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.
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