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contributor authorJinglun Feng
contributor authorLiang Yang
contributor authorJizhong Xiao
date accessioned2023-11-27T23:11:30Z
date available2023-11-27T23:11:30Z
date issued8/7/2023 12:00:00 AM
date issued2023-08-07
identifier otherJCCEE5.CPENG-5359.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4293369
description abstractIn numerous infrastructure health monitoring and inspection applications, swift and precise three-dimensional reconstruction of subsurface objects from ground penetrating radar (GPR) data is of critical importance, particularly given the recent advancements in perception modeling and the emergence of deep learning. Nonetheless, current research on the reconstruction of subsurface infrastructure scenes and objects faces limitations. Owing to the restrictions of conventional GPR data processing, these methodologies are prone to GPR data with noisy backgrounds and struggle to recreate noncylindrical objects. This paper investigates the back-projection (BP) approach for GPR-based three-dimensional (3D) subsurface target reconstruction and presents a learning model that formulates the reconstruction as an implicit BP from 2D to 3D representations, circumventing any preprocessing requirements in contrast to traditional techniques. The proposed learned model ultimately generates an explicit volumetric representation of the subsurface objects. Experimental results show at least a 33% enhancement in the performance of the proposed model compared to meticulously designed baselines.
publisherASCE
titleSubsurface Object 3D Modeling Based on Ground Penetration Radar Using Deep Neural Network
typeJournal Article
journal volume37
journal issue6
journal titleJournal of Computing in Civil Engineering
identifier doi10.1061/JCCEE5.CPENG-5359
journal fristpage04023030-1
journal lastpage04023030-17
page17
treeJournal of Computing in Civil Engineering:;2023:;Volume ( 037 ):;issue: 006
contenttypeFulltext


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