Current maintenance practices consume significant time, cost, and manpower. Thus, a new technique for maintenance is required. Construction information technologies, including building information modeling (BIM), have recently been applied to the field to carry out systematic and productive planning, design, construction, and maintenance. Although BIM is increasingly being applied to new structures, its application to existing structures has been limited. To apply BIM to an existing structure, a three-dimensional (3D) model of the structure that accurately represents the as-is status should be constructed and each structural component should be specified manually. This study proposes a method that constructs a 3D model and specifies the structural component automatically using photographic data with a camera installed on an unmanned aerial vehicle. This procedure is referred to as semantic structure from motion because it constructs a 3D point cloud model together with semantic information. A validation test was carried out on a railroad bridge to validate the performance of the proposed system. The average precision, intersection over union, and BF scores were 80.87%, 66.66%, and 56.33%, respectively. The proposed method could improve the current scan-to-BIM procedure by generating the as-is 3D point cloud model by specifying the structural component automatically.

中文翻译：

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深度学习的铁路桥梁运动语义结构

当前的维护实践消耗大量时间，成本和人力。因此，需要一种新的维护技术。最近，包括建筑信息模型（BIM）在内的建筑信息技术已应用于该领域，以进行系统且富有成效的规划，设计，建造和维护。尽管BIM越来越多地应用于新结构，但其在现有结构中的应用受到限制。要将BIM应用于现有结构，应构建该结构的三维（3D）模型以准确表示现状，并应手动指定每个结构组件。这项研究提出了一种方法，该方法可以构建3D模型并使用安装在无人驾驶飞机上的摄像头使用摄影数据自动指定结构组件。此过程从运动中被称为语义结构，因为它与语义信息一起构造了3D点云模型。在铁路桥梁上进行了验证测试，以验证所提出系统的性能。平均精度，联合相交和BF得分分别为80.87％，66.66％和56.33％。所提出的方法可以通过自动指定结构组件来生成3D点云模型，从而改善当前的“扫描到BIM”程序。分别为66％和56.33％。所提出的方法可以通过自动指定结构组件来生成3D点云模型，从而改善当前的“扫描到BIM”程序。分别为66％和56.33％。所提出的方法可以通过自动指定结构组件来生成3D点云模型，从而改善当前的“扫描到BIM”程序。