A Hybrid Perspective of Vision-Based Methods for Estimating Structural Displacements Based On Mask R-CNN

Chuanchang Xu, Cass Wai Gwan Lai, Yangchun Wang, Jiale Hou, Zhufeng Shao, Enjian Cai, Xingjian Yang
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Abstract

Vision-based methods have shown great potential in vibration-based structural health monitoring (SHM), which can be classified as target-based and target-free methods. However, target-based methods cannot achieve sub-pixel accuracy, and target-free methods are sensitive to environmental effects. To this end, this paper proposed a hybrid perspective of vision-based methods for estimating structural displacements, based on Mask region-based convolutional neural networks (Mask R-CNN). In proposed methods, Mask R-CNN is used to first locate the target region, and then target-free vision-based methods are used to estimate structural displacements from the located target. The performances of proposed methods were validated in a shaking table test of a cold-formed steel (CFS) wall system. It can seen that Mask R-CNN can significantly improve the accuracy of feature point matching results of the target-free method. The comparisons of estimated structural displacements using proposed methods are conducted and detailed into accuracy, stability, and computation burden, to guide the selection of the proper proposed method for the specific problem in vibration-based SHM. Proposed methods can also achieve even 1/15 pixel level accuracy. Moreover, different image denoising methods in different lighting conditions are compared.
基于掩模 R-CNN 的结构位移估算视觉方法的混合视角
基于视觉的方法在基于振动的结构健康监测(SHM)中显示出巨大的潜力,这些方法可分为基于目标的方法和无目标方法。然而,基于目标的方法无法达到亚像素精度,而无目标方法对环境影响敏感。为此,本文基于基于掩膜区域的卷积神经网络(Mask R-CNN),提出了一种基于视觉的结构位移估算混合方法。在所提出的方法中,首先使用掩膜 R-CNN 定位目标区域,然后使用基于视觉的无目标方法来估计已定位目标的结构位移。在冷弯型钢(CFS)墙体系统的振动台试验中验证了所提方法的性能。可以看出,Mask R-CNN 可以显著提高无目标方法的特征点匹配结果的准确性。通过对所提方法估算的结构位移进行比较,详细分析了精度、稳定性和计算负担,以指导在基于振动的 SHM 中针对具体问题选择合适的所提方法。建议的方法甚至可以达到 1/15 像素级的精度。此外,还比较了不同光照条件下的不同图像去噪方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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