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November 28, 2025Computer-Aided Civil and Infrastructure EngineeringOpen Access

Three‐dimensional reconstruction of loose defects in semi‐rigid base layers using enhanced deep learning and point cloud from GPR images

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Authors

BZBei ZhangSXShuo XuYZYanhui Zhong

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Overview

Integrated framework demonstrates improved quantitative assessment through 3D reconstruction in asphalt pavements, highlighting maintenance planning benefits.

Key Points

  • Accurate quantitative assessment of defects was achieved with a mean average precision of 97.25%.
  • A high-fidelity synthetic data set was generated for effective deep learning segmentation under complex conditions.
  • 3D reconstruction used Delaunay triangulation to estimate volumetric extent, yielding an accuracy of 78.07%.
  • The framework supports enhanced maintenance planning for asphalt pavements, proving effective in real applications.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/6928f106a65b730b9ea79ca2https://doi.org/10.1111/mice.70157
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  1. 1Three‐dimensional reconstruction of loose defects in semi‐rigid base layers using enhanced deep learning and point cloud from GPR images2025 · 2 citations
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  5. 5From Simulation to Reality: GAN-Based Transformation of Pavement Defect Images for YOLO Detection2026 · 1 citations