GPR image recognition and three-dimensional reconstruction method for loose disease of semi-rigid base

By constructing a stochastic medium model of loose diseases in semi-rigid base courses and improving the YOLOv8 algorithm, combined with the Delaunay triangulation method, the problems of low identification accuracy and three-dimensional reconstruction of loose diseases in semi-rigid base courses were solved, achieving high-precision disease identification and quantification, and supporting road maintenance decision-making.

CN121685818APending Publication Date: 2026-03-17ZHENGZHOU UNIV +1
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Patent Information

Application Number
CN202511672411.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-14
Publication Date
2026-03-17

AI Technical Summary

Technical Problem

In existing technologies, the accuracy of identifying loose diseases in semi-rigid base layers is low, and three-dimensional reconstruction and quantification are not possible. Traditional manual inspection is inefficient and highly subjective. Existing GPR detection simulation data lacks authenticity, the generalization ability of the algorithm after training is weak, the accuracy of disease identification is limited, and three-dimensional quantitative analysis is lacking.

Method used

A stochastic medium model of loose disease in semi-rigid base layers was constructed. Simulation data was generated by the finite-difference time-domain method and the dataset was expanded by combining data augmentation techniques. A DN module integrating dynamic deformation convolution module and RepNCSPELAN4 module was designed. The WIoU loss function was introduced, the YOLOv8 algorithm was improved, and the disease volume was calculated by combining the Delaunay triangulation method.

Benefits of technology

It achieves high-precision disease identification and 3D reconstruction. The improved algorithm has an mAP of 97.25% and an FPS of 32.05, providing a quantitative assessment of disease development and a basis for maintenance decisions.

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Abstract

The invention is suitable for the technical field of road detection, and relates to a semi-rigid base layer loose disease GPR image recognition and three-dimensional reconstruction method, which comprises the following steps: constructing a semi-rigid base layer loose disease random medium model, carrying out forward modeling on a road structure, generating simulation data, and expanding a data set in combination with a data enhancement technology; a DN module fusing a dynamic deformation convolution module and a RepNCSPELAN4 module is designed, meanwhile, a WIoU loss function is introduced, training is conducted through an obtained data set, and identification of the semi-rigid base layer loose disease is achieved; calculating the area of a disease region through pixel and physical size conversion based on an instance segmentation result obtained through identification; a three-dimensional point cloud is generated by adopting inverse distance weighted interpolation, and the disease volume is calculated in combination with a Delaunay triangulation method. According to the method, accurate disease recognition is achieved, meanwhile, two-dimensional recognition is converted into three-dimensional reconstruction, and a quantitative basis is provided for maintenance priority division and repair scheme formulation.
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