Masonry lined tunnel condition assessment is a predominantly manual process. It consists primarily of a visual inspection followed by a lengthy and subjective manual defect labelling process. There is therefore much potential for automation. Masonry spalling is a key indicator of a masonry tunnel’s condition. To obtain actionable detail about a tunnel’s condition, it is also necessary to determine the spalling severity, defined by the depth of spalling. This study presents an automated workflow to identify the depth of spalling from masonry tunnel 3D point cloud data obtained by lidar. Firstly, a tunnel point cloud is unrolled using a cylindrical projection and the points are rasterised into a 2D image taking pixel values of the offset of each point from the cylinder. Then, a 2D U-Net pretrained on both real and synthetic masonry lining data, is used to segment masonry joint locations to isolate individual blocks. A separate U-Net is used to segment areas of masonry damage and data obstructions, which are then masked out before a surface plane representing the theoretical undamaged surface location is fitted to each masonry block from the remaining points. This allows the depth of spalling to be measured directly. As a result, this method can automatically determine the depth of spalling despite the curved and often deformed nature of a masonry tunnel profile. Experiments show results competitive with those obtained by human assessors.

中文翻译：

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使用深度学习和块面平面拟合方法对历史铁路隧道中的砖石剥落严重程度进行自动分割


砖石衬砌隧道状况评估主要是手动过程。它主要包括目视检查，然后是冗长且主观的手动缺陷标记过程。因此，自动化具有很大的潜力。砌体剥落是砌体隧道状况的关键指标。为了获得有关隧道状况的可操作详细信息，还需要确定剥落严重程度（由剥落深度定义）。本研究提出了一种自动化工作流程，用于从激光雷达获得的砖石隧道 3D 点云数据中识别剥落深度。首先，使用圆柱投影展开隧道点云，并将点光栅化为二维图像，获取每个点距圆柱的偏移像素值。然后，使用在真实和合成砌体衬砌数据上预训练的 2D U-Net 来分割砌体接缝位置以隔离各个块。一个单独的 U-Net 用于分割砖石损坏和数据障碍的区域，然后在将代表理论未损坏表面位置的表面平面安装到剩余点的每个砖石块之前将其遮盖掉。这样可以直接测量剥落的深度。因此，尽管砌体隧道轮廓具有弯曲且经常变形的性质，但该方法可以自动确定剥落的深度。实验表明，结果与人类评估者获得的结果具有竞争力。