Enhanced fiber optic bending senor based on convolutional neural network

Zhong-wei TAN; Jing-ya YANG; Yan LIU; Shun LU; Li-wei ZHANG; Hui NIU

doi:10.37188/OPE.20202807.1454

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Enhanced fiber optic bending senor based on convolutional neural network

Modern Applied Optics | 更新时间：2020-08-13

- Enhanced fiber optic bending senor based on convolutional neural network
- Optics and Precision Engineering Vol. 28, Issue 7, Pages: 1454-1461(2020)
- 作者机构：
  
  北京交通大学, 北京 100044
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20202807.1454
  CLC： TP212;TN253
- Received：23 December 2019，
  
  Revised：25 February 2020，
  
  Accepted：25 February 2020，
  
  Published：15 July 2020
- 稿件说明：
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Zhong-wei TAN, Jing-ya YANG, Yan LIU, et al. Enhanced fiber optic bending senor based on convolutional neural network[J]. Optics and precision engineering, 2020, 28(7): 1454-1461.
DOI：

Zhong-wei TAN, Jing-ya YANG, Yan LIU, et al. Enhanced fiber optic bending senor based on convolutional neural network[J]. Optics and precision engineering, 2020, 28(7): 1454-1461. DOI： 10.37188/OPE.20202807.1454.

摘要

为了提高光纤弯曲传感器的灵敏度，增大线性范围，降低成本，本文提出一种基于深度神经网络分类塑料光纤弯曲角度及方向的方法。使用进行侧抛增敏处理的塑料光纤，在光纤输出端采集不同弯曲角度的散斑图。制作了包含五类弯曲角度的数据集一以及包含七类弯曲角度的数据集二，在预处理图像数据之后，利用多层卷积神经网络对散斑图像进行卷积和池化处理，得到散斑图像的特征图，softmax分类器用来得到分类准确率，最后对两种不同卷积神经网络模型的分类效果进行对比。结果显示：数据集一光纤弯曲的角度间隔为5°时分类准确率达到了96%，理论和实际分析结果表明该方案识别率较高，基于该方法有望实现一种简单、高效的光纤弯曲传感器。

Abstract

To improve the sensitivity and cost-efficiency of a fiber bending sensor and to increase its linear range

a method based on a deep neural network was proposed to classify different bending angles and directions of plastic fiber. Plastic fiber with side throw sensitization processing was used to collect speckle images of different bending angles at the output end of the fiber. Data set one was made with five types of bending angle and data set two contained seven types of bending angle. After the pretreatment of image data

a multilayer convolution neural network was used to analyze the speckle image. The convolution and pooling provided speckle image features. A softmax classification was used for classification accuracy. Finally

the effect of two different convolutions on the classification of the neural network model was compared. The results show that the classification accuracy reaches 96% when the angle interval of fiber bending in the data set one is 5°. The theoretical and practical analysis results show that the scheme has a high recognition rate. Moreover

the realization of this method is expected to provide a new type of simple and efficient fiber bending sensor.

关键词

Keywords

references

K T V GRATTAN , T SUN . Fiber optic sensor technology:an overview . Sensors and Actuators A(Physical) , 2000 . 82 ( 1/3 ): 40 - 61 . http://d.old.wanfangdata.com.cn/Periodical/shdxxb201401003 http://d.old.wanfangdata.com.cn/Periodical/shdxxb201401003 .

H L ZHANG , Z F WU , P P SHUM , 等 . Fiber Bragg gratings in heterogeneous multicore fiber for directional bending sensing . Journal of Optics , 2016 . 18 ( 8 ): 085705 DOI: 10.1088/2040-8978/18/8/085705 http://doi.org/10.1088/2040-8978/18/8/085705 .

K YANG , J HE , C LIAO , 等 . Femtosecond laser inscription of fiber Bragg grating in twin-core few-mode fiber for directional bend sensing . Journal of Lightwave Technology , 2017 . 35 ( 21 ): 4670 - 4676 . DOI: 10.1109/JLT.2017.2750407 http://doi.org/10.1109/JLT.2017.2750407 .

孙广开 , 曲道明 , 闫光 , 等 . 软体气动驱动器弯曲变形光纤传感与形状重构 . 光学精密工程 , 2019 . 27 ( 5 ): 1052 - 1059 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201905007 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201905007 .

G K SUN , D M QU , G YAN , 等 . Bending deformation of optical fiber sensing and shape reconstruction of soft pneumatic driver . Opt. Precision Eng , 2019 . 27 ( 5 ): 1052 - 1059 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201905007 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201905007 .

何彦霖 , 张旭 , 孙广开 , 等 . 复合基底柔性光纤曲率传感器设计 . 光学精密工程 , 2019 . 27 ( 6 ): 1270 - 1276 . http://www.cqvip.com/QK/92835X/201906/7002462031.html http://www.cqvip.com/QK/92835X/201906/7002462031.html .

Y L HE , X ZHANG , G K SUN , 等 . Flexible curvature sensor based on composite substrate . Opt. Precision Eng , 2019 . 27 ( 6 ): 1270 - 1276 . http://www.cqvip.com/QK/92835X/201906/7002462031.html http://www.cqvip.com/QK/92835X/201906/7002462031.html .

Q WANG , Y LIU . Optical fiber curvature sensor based on MMF-SCF-MMF structur . Optical Fiber Technology , 2018 . 43 ( 10 ): 1 - 5 . https://www.sciencedirect.com/science/article/pii/S1068520018300531 https://www.sciencedirect.com/science/article/pii/S1068520018300531 .

L A DANISCH . Bend enhanced fiber optic sensors . SPIE , 1993 . 1795 204 - 214 . http://d.old.wanfangdata.com.cn/NSTLHY/NSTL_HYCC025739658/ http://d.old.wanfangdata.com.cn/NSTLHY/NSTL_HYCC025739658/ .

DANISCH L A. Fiber optic bending and positioning sensor including a light emission surface formed on a portion of a light guide, United States[P]. 5321257, 1994-6-14.

申亚萍.曲率光纤传感器关键技术的研究[D].北京: 北京交通大学, 2019.

SHEN Y P. Research on the Key Technology of Curvature Fiber Optic Sensor[D].Beijing: Beijing Jiaotong University, 2019. (in Chinese)

H T DI , Y T LI , K Y LIU , 等 . Hand gesture monitoring using fiber-optic curvature sensors . Applied Optics , 2019 . 58 ( 29 ): 7935 - 7942 . DOI: 10.1364/AO.58.007935 http://doi.org/10.1364/AO.58.007935 .

R FLORENTIN , V KERMENE , J BENOIST , 等 . Shaping the light amplified in a multimode fiber . Light:Science & Applications , 2016 . 6 ( 2 ): e16208 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gkxyyy-e201701014 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gkxyyy-e201701014 .

L C YI , BENGIO Y , HINTON G. . Deep learning . Nature , 2015 . 521 ( 7553 ): 436 - 444 . DOI: 10.1038/nature14539 http://doi.org/10.1038/nature14539 .

J SCHMIDHUBER . Deep learning in neural networks:An overview . Neural Networks , 2015 . 61 85 - 117 . DOI: 10.1016/j.neunet.2014.09.003 http://doi.org/10.1016/j.neunet.2014.09.003 .

KRIZHEVSKY A, SUTSKEVER I, HINTON G.E. Image Net classification with deep convolutional neural networks[C]. International Conference on Neural Information Processing Systems. NY USA: Advances in neural information processing systems, 2012: 1097-1105.

P WANG , J DI . Deep learning-based object classification through multimode fiber via a CNN-architecture SpeckleNet . Applied Optics , 2018 . 57 ( 28 ): 8258 - 8263 . DOI: 10.1364/AO.57.008258 http://doi.org/10.1364/AO.57.008258 DOI: 10.1364/AO.57.008258 http://doi.org/10.1364/AO.57.008258 .

N BOTHANI , E KAKKAVA , M CHRISTOPHE , 等 . Learning to see through multimode fibers . Optical , 2018 . 5 ( 8 ): 960 - 966 . https://arxiv.org/pdf/1805.05614 https://arxiv.org/pdf/1805.05614 .

DI H T, SUN S, YU J, et al. Novel optical fiber sensor for deformation measurement[C].5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment. Washington: Proceeding of SPIE, 2010: 7656, 76561C-1-76561C-7.

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