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

Commercialization and industrial deployment of optical feedback interferometry or self-mixing interferometry (SMI)-based displacement instruments are 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 2-D 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 that 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 learned to detect SMI fringes belonging to weak- and very strong-optical feedback regimes, 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 when compared to this established method. Our novel augmentation scheme is generic and can be applied to other interferometric signals. We have released our dataset and implementation, with the hope that this will assist the community in accelerating the commercialization of optical feedback interferometry leveraging the full potential of deep learning.