Dual-frequency ground penetrating radar underground target saliency feature fusion method

By designing a semantic feature extraction network and phase constraints, the contradiction between accuracy and depth resolution in ground penetrating radar data fusion in traditional methods is resolved, efficient and accurate feature information fusion is achieved, and the performance of the detection system is improved.

CN120766084APending Publication Date: 2025-10-10HEZHOU UNIV
0 Cites 1 Cited by

Patent Information

Application Number
CN202510918489.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-03
Publication Date
2025-10-10

AI Technical Summary

Technical Problem

Traditional methods have difficulty in effectively utilizing the characteristic information in the original echo data when fusing dual-frequency ground penetrating radar data, and the accuracy and quality of image fusion are difficult to guarantee. Especially in complex geological environments, the contradiction between detection depth and resolution is difficult to resolve.

Method used

A parallel network architecture combining deep convolutional neural networks with attention mechanism is adopted to design a semantic feature extraction network. Direct wave interference is removed through data preprocessing. The feature information of high-resolution and low-resolution GPR echo data is extracted and fused by combining phase consistency loss and phase change region gradient loss.

Benefits of technology

It improves the extraction accuracy and fusion quality of feature information, effectively solves the contradiction between detection depth and resolution, and enhances the saliency and accuracy of target features.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a dual-frequency ground penetrating radar underground target saliency feature fusion method, which comprises the following steps of: before target feature detection, removing a direct wave strong interference signal in GPR echo data; designing a semantic feature extraction model, fusing a channel attention SE module and a space attention PSANet module into a feature extraction network, and adding phase consistency loss and phase change region gradient loss into a loss function during network model training; transpose convolution is carried out on the extracted low-frequency-band semantic feature map in the double-frequency GPR data, upsampling is carried out to the scale of the high-frequency-band semantic feature map, and high-frequency-band and low-frequency-band feature fusion is carried out. According to the fusion method, the high-frequency-band GPR echo signal contains target information with higher resolution, the low-frequency-band GPR can detect a target located at a deeper position underground, the contradiction problem between the GPR detection depth and the detection precision is effectively solved, and the method has the advantages of being high in precision and robustness.
Need to check novelty before this filing date? Find Prior Art