Crack detection and documentation play a vital role in the asset management of large-scale tunnel complexes. This study proposes a computer vision-based tunnel crack data management method enabling 3D visualization, quantification, and documentation into structured data. The method reconstructs sparse point clouds with Structure from Motion (SfM) and cleans the irrelevant tunnel facilities with a two-stage filtering method. The denoised 3D point clouds are then fitted with customised meshes and textured into 3D reconstruction models. The flat scaled orthomosaic is generated by the cylindrical unrolling. Deep learning methods are employed for pixel-level crack detection in this high-resolution image for the extraction of crack location and quantification.

中文翻译：

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从像素到基础设施：基于摄影测量的隧道裂缝数字化和深度学习记录方法


裂缝检测和记录在大型隧道综合体的资产管理中起着至关重要的作用。本研究提出了一种基于计算机视觉的隧道裂缝数据管理方法，能够将 3D 可视化、量化和记录为结构化数据。该方法使用运动结构 （SfM） 重建稀疏点云，并使用两阶段过滤方法清理无关的隧道设施。然后，将去噪后的 3D 点云拟合为自定义网格，并纹理化为 3D 重建模型。平面缩放的正射镶嵌由圆柱展开生成。在此高分辨率图像中，采用深度学习方法进行像素级裂纹检测，以提取裂纹位置和量化。