###### Abstract  
To address the limitations of traditional tunnel structural plane modeling--such as low automation, insufficient smoothness, and poor adaptability to real construction environments--this study proposes a novel three-dimensional (3D) modeling framework based on B-spline interpolation combined with deep learning. The method first employs YOLOv5 for rapid detection of structural regions and DeepLabV3+ for precise boundary segmentation, followed by skeleton extraction and coordinate transformation to obtain spatial structural traces. Finally, B-spline interpolation is applied across multiple tunnel sections to construct continuous 3D surfaces. In model training and testing, the segmentation network achieved an F1 score of 94.01%, and the final modeling accuracy demonstrated a mean relative error (MRE) below 2.5%, confirming the reliability of the geometric reconstruction. Additionally, the proposed method was applied to excavation face images from the Paiyashan Tunnel, where multiple structural surfaces were successfully reconstructed in 3D, validating the approach's applicability and robustness in real geological conditions. Compared to traditional triangulated or linear surface methods, the proposed approach achieves higher smoothness, better geological continuity, and improved automation, making it suitable for real-world geotechnical applications.  
t  
24 June 202524 June 202512 July  
and tunnel face interpretation [12; 13; 14]. Meanwhile, 3D reconstruction methods such as structure-from-motion (SfM), laser scanning, and structured light scanning have also been explored [15; 16; 17]. However, these techniques often rely on expensive equipment and are sensitive to environmental conditions, which limits their field deployment in tunnel engineering.  
Existing 3D modeling methods include triangulated irregular networks (TINs) [18], linear surface interpolation [19], cubic splines [20], NURBS [21], and Delaunay triangulation [22]. In contrast, B-spline interpolation offers high-order continuity, strong local controllability, and superior geometric fidelity, making it especially suitable for modeling geologically complex and fragmented surfaces. [PERSON] et al. proposed a spline-based regularization method for complex geological body reconstruction, demonstrating its effectiveness in representing stratigraphic boundaries and discontinuities with high smoothness and accuracy [23]. [PERSON] et al. applied high-order B-spline basis functions in the material point method to simulate the deformation and failure of layered rock masses, significantly improving computational precision [24]. Despite these advantages, few studies have integrated B-spline modeling with automated feature recognition under practical tunnel conditions. Most existing works either focus solely on feature extraction or apply B-splines in idealized scenarios without addressing real-world data uncertainties, automation requirements, or computational efficiency.  
To bridge this gap, this study proposes a fully automated 3D modeling framework that combines YOLOv5-based object detection [25], DeepLabV3+-based semantic segmentation [26], and B-spline surface interpolation. Validated using real-world images from the Paiyashan Tunnel, the proposed method achieves high segmentation accuracy and precise surface reconstruction. Compared to conventional techniques, it offers an improved balance between accuracy, efficiency, and adaptability in challenging field conditions.