Deep learning for highly efficient curvature recognition using fiber scattering speckles
Xinliang Gao, Yonghui Li, Jixuan Wu, Binbin Song, Haifeng Liu, Xiao Liu, Hanchao Sun
Abstract
A flexible fiber-optic sensor enabled by deep learning is proposed and experimentally demonstrated for highly efficient curvature sensing application. This sensing modulation system combines a deep optical neural network based on a small training dataset, aiming to simplify speckle data capture and sensor model evaluation. The multimode fiber concatenated with a section of single stress-applying fiber serves as a sensing unit as well as an image transport medium. A type of hybrid scattering speckle images is collected and employed to provide more freedom to identify the bending curvature with and without external disturbances. In a perturbed environment, the trained optical classification model is suitable for the speckle dataset recognition with high accuracy rate of 98.3%. Moreover, the deep-learning-enabled fiber curvature sensor shows great potential for practical applications in real-time structural safety test, including studies on health monitoring of infrastructure equipment and aerospace wings.