探地雷达
阈值
计算机科学
特征(语言学)
雷达
底土
遥感
地质学
人工智能
图像(数学)
语言学
电信
哲学
土壤科学
土壤水分
作者
Namgyu Kim,Kideok Kim,Yun‐Kyu An
标识
DOI:10.1080/10298436.2018.1559317
摘要
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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