Demodulation of Fiber Specklegram Curvature Sensor Using Deep Learning

Abstract

In this paper, a learning-based fiber specklegram sensor for bending recognition is proposed and demonstrated. Specifically, since the curvature-induced variations of mode interference in optical fibers can be characterized by speckle patterns, Resnet18, a classification model based on convolutional neural network architecture with excellent performance, is used to identify the bending state and disturbed position simultaneously according to the speckle patterns collected from the distal end of the multimode fiber. The feasibility of the proposed scheme is verified by rigorous experiments, and the test results indicate that the proposed sensing system is effective and robust. The accuracy of the trained model is 99.13%, and the prediction speed can reach 4.75 ms per frame. The scheme proposed in this work has the advantages of low cost, easy implementation, and a simple measurement system and is expected to find applications in distributed sensing and bending identification in complex environments.

Keywords

• fiber specklegram sensor
• deep learning
• convolutional neural networks
• bending measurement