In this paper, a novel accurate and economical 3D computer vision-based framework is proposed to quantify out-of-plane displacements of steel plate structures. First, a sequence of image frames of the steel plate structures of interest is collected. Second, using image association, structure-from-motion, and multi-view stereo algorithms, a 3D point cloud of the steel plate structures and their surroundings is created. Third, an efficient 3D object detection method based on convolutional neural networks is developed and implemented to identify the steel plate structures in the 3D point cloud. Last, the out-of-plane displacements of the steel plate structures are quantified using point cloud postprocessing algorithms. The proposed framework has been implemented on a steel plate damper and a full-scale steel corrugated plate wall panel, which are commonly used in structural and earthquake engineering applications. The results indicate the developed framework can successfully localize the steel plate components in the 3D scene and accurately quantify the out-of-plane structural displacements with an average accuracy of ∼1 mm. The implementation shows the proposed framework can accurately and efficiently quantify the out-of-plane displacements of steel plate structures in realistic engineering applications.

中文翻译：

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使用运动结构、深度学习和点云处理对钢板结构进行基于 3D 视觉的平面外位移量化

在本文中，提出了一种新颖且经济的基于 3D 计算机视觉的框架来量化钢板结构的面外位移。首先，收集感兴趣的钢板结构的一系列图像帧。其次，使用图像关联、运动结构和多视图立体算法，创建钢板结构及其周围环境的 3D 点云。第三，开发并实施了一种基于卷积神经网络的高效 3D 对象检测方法，以识别 3D 点云中的钢板结构。最后，使用点云后处理算法量化钢板结构的平面外位移。拟议的框架已在钢板阻尼器和全尺寸钢波纹板墙板上实施，常用于结构和地震工程应用。结果表明，开发的框架可以成功定位 3D 场景中的钢板组件，并准确量化平面外结构位移，平均精度为 ∼1 mm。实施表明，所提出的框架可以在实际工程应用中准确有效地量化钢板结构的面外位移。