Deep Learning-Based Fiber Bending Recognition for Sensor Applications

Date

2023-01-01

Author

Bender, Deniz
Cakir, Ugur
Yüce, Emre

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The sensitivity of multimode fibers (MMFs) to mechanical deformations has led to their widespread use in various fields, such as structural monitoring and healthcare. However, traditional optical fiber sensing techniques often involve complex equipment and analysis procedures. In this work, we demonstrate the use of Deep Learning (DL) to accurately detect both the curvature and location of a bent MMF under external force. The DL model is trained using intensity-only speckle images as input, which correspond to the bending curvature and location. Our results show that the network can detect the bending location with an accuracy of 1.39 cm and the curvature with an accuracy of 0.158 m-1.

Subject Keywords

Convolutional neural networks, Curvature sensor, Deep learning, Multimode fiber specklegram sensing, ResNet

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149407408&origin=inward
https://hdl.handle.net/11511/102675

Journal

IEEE Sensors Journal

DOI

https://doi.org/10.1109/jsen.2023.3249049

Collections

Department of Physics, Article

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Citation Formats

D. Bender, U. Cakir, and E. Yüce, “Deep Learning-Based Fiber Bending Recognition for Sensor Applications,” IEEE Sensors Journal, pp. 0–0, 2023, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149407408&origin=inward.