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基于有限元方法和 Blender 图形程序的合成图像生成,用于基于视觉的测量系统建模。

Synthetic Image Generation Using the Finite Element Method and Blender Graphics Program for Modeling of Vision-Based Measurement Systems.

机构信息

Department of Robotics and Mechatronics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland.

出版信息

Sensors (Basel). 2021 Sep 9;21(18):6046. doi: 10.3390/s21186046.

Abstract

Computer vision is a frequently used approach in static and dynamic measurements of various mechanical structures. Sometimes, however, conducting a large number of experiments is time-consuming and may require significant financial and human resources. On the contrary, the authors propose a simulation approach for performing experiments to synthetically generate vision data. Synthetic images of mechanical structures subjected to loads are generated in the following way. The finite element method is adopted to compute deformations of the studied structure, and next, the Blender graphics program is used to render images presenting that structure. As a result of the proposed approach, it is possible to obtain synthetic images that reliably reflect static and dynamic experiments. This paper presents the results of the application of the proposed approach in the analysis of a complex-shaped structure for which experimental validation was carried out. In addition, the second example of the process of 3D reconstruction of the examined structure (in a multicamera system) is provided. The results for the structure with damage (cantilever beam) are also presented. The obtained results allow concluding that the proposed approach reliably imitates the images captured during real experiments. In addition, the method can become a tool supporting the vision system configuration process before conducting final experimental research.

摘要

计算机视觉在各种机械结构的静态和动态测量中被广泛应用。然而,有时进行大量实验既耗时又需要大量的财力和人力资源。相比之下,作者提出了一种模拟实验的方法,用于综合生成视觉数据。通过以下方式生成受载机械结构的合成图像:采用有限元法计算研究结构的变形,然后使用 Blender 图形程序渲染表示该结构的图像。通过所提出的方法,可以获得可靠地反映静态和动态实验的合成图像。本文介绍了所提出方法在对经过实验验证的复杂形状结构进行分析中的应用结果。此外,还提供了在多相机系统中对被检测结构进行 3D 重建过程的第二个示例。还呈现了带有损伤(悬臂梁)的结构的结果。所得结果表明,所提出的方法可靠地模拟了实际实验中捕获的图像。此外,该方法可以成为在进行最终实验研究之前支持视觉系统配置过程的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3346/8472785/736c1fbbf174/sensors-21-06046-g001.jpg

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