Structure from Motion (SfM) is a photogrammetric technique that uses similar features between images to construct a point cloud or model. These outputs can be utilized for monitoring and inspection of civil infrastructure. However, feature matching is very delicate and prone to errors that can lead to the failure of the outputs. In our previous research, removal of unwanted features such as vegetation and boulders before processing raised the number of feature matches inside the region of interest (ROI). In theory, raising the number of feature matches should raise the quality of the SfM point cloud, but to date no quantitative evidence has been found to support this. Moreover, the removal of unwanted features must be performed manually during each inspection and is thus labor-intensive and time-consuming. To address these issues, in this study, a deep learning-based system was developed to assist the removal of unwanted features by using a deep convolutional neural network to segment and remove unwanted features from the input images before processing into an SfM application. The results showed that the removal of unwanted features increased the number of cloud points inside the ROI. The proposed system decreased the processing time by 75.33% and 85.85% compared with the manual process in the monorail pier and motorway pier samples, respectively, without complicating the image alignment. The proposed system could potentially support the monitoring and the inspection of infrastructure construction and maintenance projects, which have numerous similar components, by archiving more cloud points inside the ROI and lowering the burden from the manual removal of unwanted features.

运动结构（SfM）是一种摄影测量技术，它在图像之间使用类似的功能来构建点云或模型。这些输出可用于监视和检查民用基础设施。但是，特征匹配非常微妙，容易出错，可能导致输出失败。在我们之前的研究中，在处理之前去除不需要的特征（例如植被和巨石）会增加目标区域（ROI）内部特征匹配的数量。从理论上讲，增加特征匹配的数量应该可以提高SfM点云的质量，但是到目前为止，还没有定量证据支持这一点。此外，去除不想要的特征必须在每次检查期间手动进行，因此劳动强度大且费时。为了解决这些问题，在本研究中，开发了基于深度学习的系统，以通过使用深度卷积神经网络在处理成SfM应用程序之前从输入图像中分割和去除不需要的特征来辅助去除不需要的特征。结果表明，去除不必要的特征会增加ROI内的浊点数量。与手动处理相比，所提出的系统与手动处理相比分别减少了75.33％和85.85％的处理时间，而不会使图像对齐复杂化。提议的系统可以通过在ROI内归档更多的云点并减轻手动删除不需要的功能的负担，来潜在地支持对具有众多相似组件的基础架构建设和维护项目的监视和检查。