Deep learning based optical curvature sensor through specklegram detection of multimode fiber

An optical fiber curvature sensor based on the detection of specklegrams from the facet of multimode fiber (MMF) is realized by using a deep learning regression model. Since the specklegrams result from mode interference in the MMF, they can be used to characterize the status of the MMF. In the experiment, light from a semiconductor laser source was injected into a section of 10-cm-long step-indexed MMF with a core diameter of 50 mu m. A large number of specklegrams at the facet of the MMF were automatically detected when different curvatures were introduced for the MMF by a controllable moving translation stage. The output specklegrams of the MMF under different curvatures were then fed into a convolutional neural network (CNN) for training, validation and testing. Experimental results demonstrate that after the CNN was well-trained by the speckle grams with specified curvatures, the CNN can effectively predict the curvature from any of the specklegrams obtained from the MMF with a curvature in the trained range. In the experiment, the CNN trained by speckle grams with 20 curvatures successfully predicted the curvatures corresponding to the specklegrams from the MMF under 57 curvatures in the range of 1.55-6.93 m(-1). The prediction error for 94.7% of the specklegrams is within an error range of +/- 0.3m(-1), confirming the feasibility of curvature sensing based on the analysis of specklegrams by CNN.