基于双目立体视觉的DIC测量方法进行图像三维位移检测

IF 0.6 Q4 ENGINEERING, MECHANICAL Journal of Measurements in Engineering Pub Date : 2023-10-09 DOI:10.21595/jme.2023.23448
Xue Dong
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引用次数: 0

摘要

大型机械的变形检测通常采用三维位移测量来实现。双目立体视觉测量技术作为一种常用的数字图像相关方法,受到了学术界的广泛关注。双目立体视觉通过模拟人眼的工作模式来达到三维位移测量的目的,但测量结果容易受到光折射的影响。在此基础上,本研究引入了针对Canon成像数据集目标位移测量的粒子群优化算法,并引入反向传播神经网络对粒子群算法中的粒子进行突变处理,生成融合算法。它结合了世界、像素、物理和相机四个坐标系来建立联系。考虑环境因素和镜头误差,通过拍摄黑白棋盘对相机参数和变形系数进行修正。最后,本研究首先对双目立体视觉技术进行了三维误差分析,在约60秒内相对误差稳定在1%。同时,引入斑点鬣狗算法等三种算法,利用粒子群优化算法和反向传播网络算法进行性能对比实验。实验表明,随着时间的推移,融合算法的三维误差逐渐稳定在[- 0.5%,0.5%]的范围内,而二维误差一般在0值附近徘徊。其性能明显优于其他算法,因此该融合算法的双目立体视觉可以获得良好的测量效果。
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DIC measurement method based on binocular stereo vision for image 3D displacement detection
The deformation detection of large machinery is usually achieved using three-dimensional displacement measurement. Binocular stereo vision measurement technology, as a commonly used digital image correlation method, has received widespread attention in the academic community. Binocular stereo vision achieves the goal of three-dimensional displacement measurement by simulating the working mode of the human eyes, but the measurement is easily affected by light refraction. Based on this, the study introduces particle swarm optimization algorithm for target displacement measurement on Canon imaging dataset, and introduces backpropagation neural network for mutation processing of particles in particle swarm algorithm to generate fusion algorithm. It combines the four coordinate systems of world, pixel, physics, and camera to establish connections. Taking into account environmental factors and lens errors, the camera parameters and deformation coefficients were revised by shooting a black and white checkerboard. Finally, the study first conducted error analysis on binocular stereo vision technology in three dimensions, and the relative error remained stable at 1 % within about 60 seconds. At the same time, three algorithms, including the spotted hyena algorithm, were introduced to conduct performance comparison experiments using particle swarm optimization and backpropagation network algorithms. The experiment shows that the three-dimensional error of the fusion algorithm gradually stabilizes within the range of [–0.5 %, 0.5 %] over time, while the two-dimensional error generally hovers around 0 value. Its performance is significantly superior to other algorithms, so the binocular stereo vision of this fusion algorithm can achieve good measurement results.
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来源期刊
Journal of Measurements in Engineering
Journal of Measurements in Engineering ENGINEERING, MECHANICAL-
CiteScore
2.00
自引率
6.20%
发文量
16
审稿时长
16 weeks
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