Ground penetrating radar intelligent inversion method based on deep learning

A ground-penetrating radar and deep learning technology, applied in the field of intelligent inversion of ground-penetrating radar based on deep learning, can solve problems such as inability to accurately describe the shape of anomalies and obtain dielectric distribution, achieve fast processing speed, and ensure real-time performance and high detection accuracy

Pending Publication Date: 2020-10-16
SHANDONG UNIV
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Problems solved by technology

However, these methods can neither accurately describe the shape of the anomalous body nor obtain the dielectric distribution of the structure

Method used

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  • Ground penetrating radar intelligent inversion method based on deep learning
  • Ground penetrating radar intelligent inversion method based on deep learning
  • Ground penetrating radar intelligent inversion method based on deep learning

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Embodiment 1

[0038] This embodiment discloses a ground penetrating radar intelligent inversion method based on deep learning, including the following steps:

[0039] Step S1: Establishing a simulation training data set, the simulation training data set includes multiple sets of radar profile-permittivity distribution map data pairs.

[0040] Aiming at the problem of tunnel lining disease structure detection, a corresponding simulation data set is established. The step S1 specifically includes:

[0041]Step S101: Randomly combine the background medium and the internal medium of the disease, and generate a dielectric constant distribution map of the lining section for each combination mode. Specifically, the interlayer interfaces and disease contours between the background media of each layer on the lining section are fitted, and multiple permittivity distribution maps are generated according to the permittivity corresponding to various media.

[0042] Wherein, the background medium type i...

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Abstract

The invention discloses a ground penetrating radar intelligent inversion method based on deep learning, and the method comprises the following steps: obtaining a simulation training data set which comprises a plurality of groups of radar profile map-dielectric constant distribution map data pairs; obtaining a radar inversion deep learning network model according to the simulation training data set; and performing dielectric constant inversion according to radar detection data acquired in real time based on the radar inversion deep learning network model. The method can achieve the automatic inversion of the complex radar detection data, achieves the higher detection precision and higher processing speed at the same time, and guarantees the real-time performance of radar data processing.

Description

[0001] This application is a divisional application with application number 2020100192030, application date January 8, 2020, and the title of the invention "a multi-arm robot for tunnel lining detection and disease diagnosis during the operation period". technical field [0002] The invention belongs to the technical field of disease detection, and in particular relates to an intelligent ground penetrating radar inversion method based on deep learning. Background technique [0003] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0004] With the construction of a large number of tunnel projects and put into operation one after another, the importance of its safe operation is particularly important. During its long-term service, under the influence of various factors such as natural environment and climate change, and periodic fatigue loads such as driving, a large number of t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/41G01S13/88G06N3/08G06N3/04
CPCG01S7/414G01S7/417G01S13/885G01S13/881G06N3/08G06N3/045
Inventor 王正方王静刘斌蒋鹏隋青美康文强
Owner SHANDONG UNIV
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