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GPR Bscan Change Detection Network for Structural Defect Evolution
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AI
DOI：10.1109/TGRS.2024.3480122.png)
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摘要
En 中文
Shape change detection is crucial for monitoring structural defect evolution. However, variations in temperature and precipitation dynamically alter the dielectric properties of structural defects and underground soil media, impacting the position and shape of scattering curves in ground penetrating radar (GPR) Bscan. This poses a challenge in determining the shape of the changing area by using dual-time GPR Bscan comparisons. To extract the structural defect features and shape change detection under changing backgrounds, this article proposes a change detection network based on GPR Bscan (GPR_CDNet). This method includes an encoder-decoder structure. In the encoder, we design a multiscale and multidilated convolution (MSMDconv) to construct a pseudo-Siamese backbone feature extraction network to enhance contextual understanding and capture fine-grained details. Furthermore, to improve the accuracy of reconstructing the shape of the changing region, the feature transformation module (FTM) is designed to convert Bscan features into spatial model features to enhance the representation of change feature information. The Bscan features are then fused with the spatial model features and input into the decoder to reconstruct the shape of the changing area. In addition, this article conducts experiments in the background medium and structural defect relative permittivity changes, rebar shielding, and actual sandbox scenarios. The results show that the network can adapt to the changes in the background medium's relative permittivity and the structural defects' relative permittivity and reconstruct the regional shape of structural defects that change over time with better F-1 performance and less computational complexity.
Keyword:
Permittivity
Feature extraction
Shape
Soil
Imaging
Convolution
Decoding
Data models
Data mining
Accuracy
Change detection
feature transformation
ground penetrating radar (GPR)
reconstruction
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W