TY - GEN
T1 - GPR Data-Based Computer Vision for the Detection of Material Buried Underground
AU - Park, Sehwan
AU - Kim, Juwon
AU - Jeong, Seokhun
AU - Park, Seunghee
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - 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.
AB - 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.
KW - computer vision
KW - ground penetrating radar
KW - image-processing
UR - https://www.scopus.com/pages/publications/85076105273
U2 - 10.1109/ICSGSC.2019.00-21
DO - 10.1109/ICSGSC.2019.00-21
M3 - Conference contribution
AN - SCOPUS:85076105273
T3 - Proceedings - 2019 3rd International Conference on Smart Grid and Smart Cities, ICSGSC 2019
SP - 41
EP - 44
BT - Proceedings - 2019 3rd International Conference on Smart Grid and Smart Cities, ICSGSC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Smart Grid and Smart Cities, ICSGSC 2019
Y2 - 25 June 2019 through 28 June 2019
ER -
