Displacement plays a pivotal role in bridge assessment, but accurate displacement monitoring remains a challenging task. Unmanned Aerial Vehicles (UAVs) provide a cost-effective, time-efficient, and high maneuverability alternative to infrastructure monitoring, as they overcome the spatial limitations of the fixed camera and acquire the high-resolution image sequence. However, the measurement accuracy is often affected by the movement of the UAV. To address these constraints, this study proposed a computer vision-based nontarget displacement measurement method and a two-stage UAV movement correction method using fixed point and variational mode decomposition (VMD). Initially, the adaptive fusion of deep features and shallow features can efficiently encode the informative representation of the natural texture on the structural surface. Subsequently, the movement of the UAV is eliminated by stationary fixed points (Step Ⅰ) and VMD techniques (Step Ⅱ). Finally, the performance of the proposed methodology is verified with the field tests on a concrete wall and an arch bridge. Through mode decomposition and reconstruction, the measurement accuracy is greatly improved compared to the correction method only using fixed points, which proves the reliability and effectiveness of the proposed non-target displacement measurement method.

中文翻译：

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一种基于非目标计算机视觉的位移测量中无人机运动诱发误差的两阶段校正方法


位移在桥梁评估中起着关键作用，但准确的位移监测仍然是一项具有挑战性的任务。无人机 （UAV） 为基础设施监控提供了一种经济高效、时间高效且高度可操作性的替代方案，因为它们克服了固定相机的空间限制并获取高分辨率图像序列。然而，测量精度往往受到无人机运动的影响。为了解决这些限制，本研究提出了一种基于计算机视觉的非目标位移测量方法和一种使用定点和变分模态分解 （VMD） 的两阶段无人机运动校正方法。最初，深层特征和浅层特征的自适应融合可以有效地编码结构表面上自然纹理的信息表示。随后，通过固定固定点（步骤 I.）和 VMD 技术（步骤 II.）消除无人机的运动。最后，通过在混凝土墙和拱桥上的现场测试验证了所提出的方法的性能。通过模态分解和重构，与仅使用固定点的校正方法相比，测量精度大大提高，证明了所提出的非目标位移测量方法的可靠性和有效性。