This article details the development of a geometrical-based displacement extraction framework capable of automatically extracting critical infrastructure measurements in one sequence. The framework is a novel rail viaduct bearing inspection pipeline implemented on Bearing Inspector for Narrow-space Observation Version 2 (BINOv2). BINOv2 is a tethered custom unmanned aerial vehicle system utilized to supplant labor-intensive pipelines and enhance inspection accuracy of infrastructure conditions in confined remote locations. The algorithm accepts stereoscopic images taken from a single monocular camera on a bidirectional cascaded linear actuator system in a rack-and-pinion configuration. A point cloud model generated from the image sets then runs through a hierarchical neural network for 3-D segmentation to extract targeted regions of interest. Our training pipeline generates and forms the full model's training dataset using only a small sample of real point clouds. The point cloud generated is inadequate to form the full bearing geometry profile. Therefore, the proposed framework projects best-fit circles based on the point cloud curvature to form the full bearing geometry profile so that the required displacement measurement is available for extraction. Several experiments were conducted on a mock-up and actual operational site to validate the proposed framework's accuracy, its robustness and comparison with other state-of-the-art alternatives.

中文翻译：

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双向级联线性执行器上的伪立体单目相机基于几何的位移测量


本文详细介绍了一种基于几何的位移提取框架的开发，该框架能够在一个序列中自动提取关键基础设施测量值。该框架是在窄空间观测轴承检查器版本 2 (BINOv2) 上实现的新型铁路高架桥轴承检查管道。 BINOv2 是一种系留定制无人机系统，用于取代劳动密集型管道并提高有限偏远地区基础设施状况的检查准确性。该算法接受从齿轮齿条配置的双向级联线性致动器系统上的单个单目相机拍摄的立体图像。然后，从图像集生成的点云模型通过分层神经网络进行 3D 分割，以提取目标感兴趣区域。我们的训练管道仅使用一小部分真实点云样本来生成并形成完整模型的训练数据集。生成的点云不足以形成完整的轴承几何轮廓。因此，所提出的框架根据点云曲率投影最佳拟合圆，以形成完整的轴承几何轮廓，以便提取所需的位移测量。在模型和实际操作站点上进行了多项实验，以验证所提出的框架的准确性、稳健性以及与其他最先进的替代方案的比较。