用AI模型从少量图像中实时提取结构变形数据，替代传统耗时的测量方法。
Deep learning-based Visual Measurement Extraction within an Adaptive Digital Twin Framework from Limited Data Using Transfer Learning
- 用CNN结合合成与真实散斑图，实现快速变形分析
- 支持二维到三维立体图像的泛化应用，提升建模精度
- 适合需要实时监测的工程结构健康诊断场景
数字孪生技术通过融合模型与实时数据，正在革新科学决策。针对传统结构健康监测依赖计算密集型数字图像相关（DIC）且难以实现实时数据整合的问题，本文提出一种基于深度学习的新方法。利用卷积神经网络（CNN）分析结构行为，将散斑图案图像与形变场进行关联。研究初期聚焦二维散斑图，后扩展至使用双目立体图像实现三维形变分析。通过混合合成与真实散斑图像训练模型，克服了计算瓶颈，提升了模型鲁棒性与泛化能力。该方法为传统测量手段提供了高效替代方案，推动三维建模在实时仿真与分析中的应用发展。
原文摘要 · Abstract (English)
Digital Twins technology is revolutionizing decision-making in scientific research by integrating models and simulations with real-time data. Unlike traditional Structural Health Monitoring methods, which rely on computationally intensive Digital Image Correlation and have limitations in real-time data integration, this research proposes a novel approach using Artificial Intelligence. Specifically, Convolutional Neural Networks are employed to analyze structural behaviors in real-time by correlating Digital Image Correlation speckle pattern images with deformation fields. Initially focusing on two-dimensional speckle patterns, the research extends to three-dimensional applications using stereo-paired images for comprehensive deformation analysis. This method overcomes computational challenges by utilizing a mix of synthetically generated and authentic speckle pattern images for training the Convolutional Neural Networks. The models are designed to be robust and versatile, offering a promising alternative to traditional measurement techniques and paving the way for advanced applications in three-dimensional modeling. This advancement signifies a shift towards more efficient and dynamic structural health monitoring by leveraging the power of Artificial Intelligence for real-time simulation and analysis.
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