Wire detection using ground penetrating radar using image processing techniques

This thesis investigates wire detection using various techniques applied to Ground Penetrating Radar (GPR) images. In this study, a step-frequency GPR with a co-pole configuration was used to collect data. The first approach proposes the use of Hough Transform (HT) to detect surface wires. The collected data is processed by beamforming and projecting onto the surface plane before applying HT. The features extracted for wire detection include orientation, strength, and relative strength. The second and third approaches propose an ellipse fitting technique to detect surface-laid and shallowly buried wires. After filtering and edge detection in the cross-section image, ellipse fitting is applied to obtain an ellipse feature for indicating how well the shape fits to an ellipse. The third approach improves the ellipse feature extraction method to obtain the feature regardless of the orientation of a wire. The method first applies HT to the surface projection of an object image from the GPR, and then rotates the 3-D data image according to the orientation angle from the HT to align with the cross-track, before the extraction of ellipse feature. The fourth approach proposes features for curved wire detection from GPR images. The processing involves projecting the 3-D GPR beamformed image onto the ground plane, applying the Canny edge detector to extract the edge points, and fitting the edge points to a parabola through a voting scheme. The features consist of the orientation angle, the fitted parabolic parameters, and the fitting confidence. The combination of these approaches offers a comprehensive framework for wire detection in GPR images, with potential applications in explosive detection.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Electrical Engineering (MU)

Grantor dc:publisher

University of Missouri--Columbia

Year dc:date.issued

2023

Author dc:creator

• Moeller, Jackson

Advisor dc:contributor.advisor

• Ho, Dominic

Language dc:language.iso

eng, English

DOI

https://doi.org/10.32469/10355/97247

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/97247