Learning-assisted specklegram analysis for recognition of simultaneous weights on multimode optical fiber

Abstract

Abstract A deep learning-based recognition of multimode fiber (MMF) specklegrams for various simultaneous weights is presented in this work. Five different random locations have been considered along the length of MMF and the specklegram images are recorded corresponding to seven different combinations of random simultaneous weights applied at these locations. A popular deep learning convolutional neural network (CNN) model, VGG-16 is employed on these images for the recognition of these seven weight combinations. The impact of acoustic vibrations, laser power, external temperature, and image sizes on the recognition accuracy is examined. A 100% recognition accuracy is attained and a negligible accuracy variation of ∼1.9% for acoustic vibrations as well as for changing laser power is observed, whereas a drastic fall in accuracy is observed in case of change in image sizes less than 80 × 80 pixels. Also, a negligible variation of ∼2% is observed for the applied external temperature. The heart of our work lies in the accumulation of a diverse, large volume of specklegram dataset by virtue of conducting brute force experiments that take care of eradication of model overfitting. The proposed proof-of-concept scheme might be useful for low-cost, efficacious, self-assisted multi-weight analysis in structural health monitoring.