| Title |
GPR Data-Based Computer Vision for the Detection of Material Buried Underground |
| ID_Doc |
42828 |
| Authors |
Park, S; Kim, J; Jeong, S; Park, S |
| Title |
GPR Data-Based Computer Vision for the Detection of Material Buried Underground |
| Year |
2019 |
| Published |
|
| DOI |
10.1109/ICSGSC.2019.00-21 |
| Abstract |
In recent scenario, the information about buried objects needed for redevelopment and reorganization of the complicated urban environment. Accidents caused by pipeline damages such as gas, communication and underground electric power lines result in human and financial loss. Therefore, the information of underground obscured materials is essential for smart city realization and construction. GPR (Ground Penetrating Radar) investigation has advantages of high resolution detection, ease of utilization and strong electromagnetic signal to noise ratio when using high frequency. However, the GPR detected image data is not visible and has a problem that it may be interpreted differently based on the approaching skill of the inspector. Therefore, this study was conducted to verify the visualization of detected data using computer vision based on the data from GPR. Canny edge and Harris corner detection were applied to the GPR image data to detect the hyperbolic shape. Using these techniques to enhance visibility will contribute to the reliable result in the buried materials detection. |
| Author Keywords |
ground penetrating radar; image-processing; computer vision |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000619356900008 |
| WoS Category |
Computer Science, Theory & Methods; Energy & Fuels; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Energy & Fuels; Engineering |
| PDF |
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