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

Loose defects in semi-rigid base layers can critically compromise the structural performance and long-term serviceability of asphalt pavements. However, accurate identification and quantitative assessment of such defects remain challenging due to the lack of reliable ground-penetrating radar (GPR) data sets and the limitations of existing detection methods. This paper presents an integrated framework that combines data simulation, deep learning–based segmentation, and 3D reconstruction to address these challenges. First, a high-fidelity synthetic data set was generated using a random medium-based forward modeling approach to represent varying looseness depths and configurations. Second, a modified YOLOv8-seg architecture was proposed, featuring a novel multi-scale feature fusion module (DN module) and a Weighted Intersection over Union (WIoU) loss function to enhance segmentation precision under noisy and complex GPR conditions. It achieved a mean average precision (mAP) of 97.25% and a real-time inference speed of 32.05 frames per second (FPS). Third, a 3D point cloud reconstruction approach based on inverse distance weighted (IDW) interpolation was introduced to restore the spatial morphology of the detected defect regions. Delaunay triangulation was then used to estimate the volumetric extent of the defects, achieving an overall estimation accuracy of 78.07%. Finally, the proposed framework was validated on a full-scale pavement model, confirming its effectiveness in defect detection, morphological recovery, and quantitative assessment. The findings provide a reliable computational tool for pavement condition evaluation and maintenance planning.