Sample spacing variations on the feature performance for subsurface object detection using handheld ground penetrating radar

2018 ◽  
Author(s):  
Dominic K. C. Ho ◽  
Brendan Alvey ◽  
Alina Zare
Keyword(s):  
Object Detection ◽  
Ground Penetrating Radar ◽  
Sample Spacing ◽  
Ground Penetrating

A Ground-penetrating Radar Object Detection Method Based on Deep Learning

2021 ◽  
Author(s):  
Siyu Chen ◽  
Li Wang ◽  
Zheng Fang ◽  
Zhensheng Shi ◽  
Anxue Zhang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Ground Penetrating Radar ◽  
Detection Method ◽  
Ground Penetrating

scholarly journals Object Detection of Ground-Penetrating Radar Signals Using Empirical Mode Decomposition and Dynamic Time Warping

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 230 ◽  
Author(s):  
Xi Wu ◽  
Christopher Adam Senalik ◽  
James Wacker ◽  
Xiping Wang ◽  
Guanghui Li
Keyword(s):  
Object Detection ◽  
Empirical Mode Decomposition ◽  
Ground Penetrating Radar ◽  
Dynamic Time Warping ◽  
Dielectric Constants ◽  
Time Warping ◽  
Feature Identification ◽  
Mode Decomposition ◽  
Dynamic Time ◽  
Ground Penetrating

An object detection method of ground-penetrating radar (GPR) signals using empirical mode decomposition (EMD) and dynamic time warping (DTW) is proposed in this study. Two groups of timber specimens were examined. The first group comprised of Douglas fir (Pseudotsuga menziesii) timber sections prepared in the laboratory with inserts of known internal characteristics. The second group comprised of timber girders salvaged from the timber bridges on historic Route 66 over 80 years. A GSSI Subsurface Interface Radar (SIR) System 4000 with a 2 GHz palm antenna was used to scan these two groups of specimens. GPR sensed differences in dielectric constants (DC) along the scan path caused by the presence of water, metal, or air within the wood. This study focuses on the feature identification and defect classification. The results show that the processing methods were efficient for the illustration of GPR information.

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