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An Integrated GPR B-Scan Preprocessing Model Based on Image Enhancement for Detecting Subsurface Pipes.

Zhengyi Shi1, Fanruo Li2, Hanchao Ma1

  • 1School of Safety Science, Tsinghua University, Beijing 100084, China.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary

This study introduces an automatic preprocessing model to improve ground-penetrating radar (GPR) B-scan interpretation for subsurface pipe detection. The model enhances hyperbola segmentation, even with noise and varying conditions, improving generalization.

Keywords:
GPR B-scanhyperbola segmentationimage enhancementsubsurface pipethresholding

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Area of Science:

  • Geophysics
  • Signal Processing
  • Non-destructive Testing

Background:

  • Ground-penetrating radar (GPR) is crucial for nondestructive subsurface pipe detection.
  • Current automatic segmentation models struggle with diverse pipe sizes, subsurface conditions, and noise in GPR B-scans.
  • Accurate hyperbola segmentation is essential for reliable GPR data interpretation.

Purpose of the Study:

  • To develop an automatic preprocessing model for enhancing GPR B-scan interpretation.
  • To improve the generalization capability of automatic hyperbola segmentation models.
  • To address challenges posed by noise, varying pipe radii, and complex field conditions.

Main Methods:

  • A novel automatic preprocessing model combining ground reflection removal algorithm (GRRA), data gravitational force enhancement (DGFE), and a global-local thresholding technique (DLTS).
  • GRRA utilizes frequency and spatial filters to remove ground reflections.
  • DGFE amplifies target hyperbolas, and DLTS segments them using dilation-based local thresholding.

Main Results:

  • The proposed model effectively enhances GPR B-scan interpretation under challenging conditions.
  • Demonstrated superior performance in segmenting diverse and small-scale hyperbolas amidst noise.
  • Achieved significant cross-dataset generalization capabilities compared to state-of-the-art methods.

Conclusions:

  • The developed automatic preprocessing model significantly improves GPR data interpretation for subsurface utility detection.
  • The model offers enhanced robustness and generalization for hyperbola segmentation in complex GPR environments.
  • This approach provides a more reliable tool for nondestructive subsurface pipe detection.