论文标题
通过深度学习从视频中提取全场子像素结构位移
Extracting full-field subpixel structural displacements from videos via deep learning
论文作者
论文摘要
本文基于卷积神经网络（CNN）开发了一个深度学习框架，该框架可以实时提取视频中的全场子像素结构位移。特别是，在由基于阶段的运动提取方法生成的数据集中设计和训练了两个新的CNN体系结构，该数据集是从一个动态结构的单个实验室记录的高速视频中生成的。由于仅在具有足够纹理对比的区域中位移是可靠的，因此通过网络体系结构设计和损失函数定义，考虑了纹理掩模引起的运动场的稀疏性。结果表明，在全面和稀疏运动场的监督下，训练有素的网络能够识别具有足够纹理对比度以及子像素运动的像素。在其他结构的各种视频中测试了训练有素的网络的性能，以提取全场运动（例如，位移时间历史），这表明训练有素的网络具有准确提取具有足够纹理相比的像素的全场微妙位移。
This paper develops a deep learning framework based on convolutional neural networks (CNNs) that enable real-time extraction of full-field subpixel structural displacements from videos. In particular, two new CNN architectures are designed and trained on a dataset generated by the phase-based motion extraction method from a single lab-recorded high-speed video of a dynamic structure. As displacement is only reliable in the regions with sufficient texture contrast, the sparsity of motion field induced by the texture mask is considered via the network architecture design and loss function definition. Results show that, with the supervision of full and sparse motion field, the trained network is capable of identifying the pixels with sufficient texture contrast as well as their subpixel motions. The performance of the trained networks is tested on various videos of other structures to extract the full-field motion (e.g., displacement time histories), which indicates that the trained networks have generalizability to accurately extract full-field subtle displacements for pixels with sufficient texture contrast.
