Augmentation-Assisted Robust Fringe Detection on Unseen Experimental Signals Applied to Optical Feedback Interferometry Using a Deep Network

Abstract

Commercialization and industrial deployment of optical feedback interferometry or self-mixing interferometry (SMI) based displacement instruments is held back due to inaccurate fringe detection under different optical feedback or speckle induced by operating conditions. In this work, we propose using deep neural 2D networks, which have renowned generalization performance on unseen data. Nonetheless, training deep neural networks requires very large data which in our application would imply acquiring large datasets of experimental signals by operating such interferometers under as many operating conditions as possible. To circumvent this time- and resource-consuming process, we propose a novel data augmentation scheme which increases the amount of training data needed by a deep network for fringe detection/classification in SMI signals. Interestingly, this has enabled the trained deep network to acquire excellent generalization capability where it has learnt to detect SMI fringes belonging to weak-, and very strong-optical feedback regime, even when it was only trained on moderate-, and strong-feedback regime signals. Consequently, our trained model has shown robust performance for simulated weak-, moderate-, and strong-optical feedback regime SMI signals affected by additive noise. Various experimental SMI signals, acquired under different sensor- and optical-conditions, have also been successfully processed. We also implemented an established fringe detection method for comparison. Our work presents very good generalization capability as compared to this established method. Our novel augmentation scheme is generic and can be applied for other interferometric signals. We have released our dataset and implementation, with the hope that this will assist community in accelerating the commercialization of optical feedback interferometry leveraging the full potential of deep learning.