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Fringe Detection and Displacement Sensing for Variable Optical Feedback-Based Self-Mixing Interferometry by Using

Asra Abid Siddiqui1, Usman Zabit1, Olivier D Bernal2

  • 1School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

Deep neural networks enhance laser feedback self-mixing interferometry (SMI) for displacement sensing. This overcomes challenges from variable optical feedback and speckle, improving accuracy for remote targets.

Keywords:
artificial intelligencedeep learninglaser sensingself-mixing interferometryspecklevariable optical feedback

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Area of Science:

  • Optics and Photonics
  • Machine Learning Applications
  • Metrology and Measurement Science

Background:

  • Self-mixing interferometry (SMI) using laser feedback offers potential for precise displacement sensing.
  • Commercialization is hindered by performance degradation under variable optical feedback, particularly with non-cooperative targets and speckle.
  • Existing methods struggle with reliable fringe detection in noisy, speckle-affected SMI signals.

Purpose of the Study:

  • To develop and evaluate deep neural networks for robust interferometric fringe detection and displacement measurement in SMI systems.
  • To address the challenge of variable optical feedback and speckle noise in laser feedback-based sensors.
  • To propose an automated method for labeling SMI fringes to create large training datasets for machine learning models.

Main Methods:

  • Training deep neural network models (Yolov5 and EfficientDet) on experimental SMI signals with variable optical feedback.
  • Implementing an automatic fringe labeling technique to generate extensive training data.
  • Quantifying network performance using metrics such as fringe detection accuracy, signal-to-noise ratio, depth of modulation, and execution time.

Main Results:

  • Demonstrated successful fringe detection and displacement measurement using deep learning models under challenging optical feedback conditions.
  • Achieved reliable performance across different laser-diode sensors and varying noise/speckle environments.
  • Validated the effectiveness of the automatic fringe labeling method for dataset generation.

Conclusions:

  • Deep neural networks, specifically Yolov5 and EfficientDet, significantly improve the robustness and accuracy of self-mixing interferometry displacement sensing.
  • The proposed automated labeling method facilitates the creation of large datasets, crucial for training effective machine learning models in this domain.
  • This work paves the way for wider commercial adoption of SMI sensors in applications involving non-cooperative targets and variable optical feedback.