Recognition of optical fiber pre-warning system based on image processing

SUN Qian; FENG Hao; ZENG Zhou-mo

doi:10.3788/OPE.20152302.0334

您当前的位置：

首页 >

文章列表页 >

Recognition of optical fiber pre-warning system based on image processing

更新时间：2020-08-13

- Recognition of optical fiber pre-warning system based on image processing
- Optics and Precision Engineering Vol. 23, Issue 2, Pages: 334-341(2015)
- 作者机构：
  
  天津大学精密测试技术与仪器国家重点实验室天津,300072
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152302.0334
  CLC： TN973;TP391
- Received：19 August 2014，
  
  Revised：29 September 2014，
  
  Published：25 February 2015
- 稿件说明：
移动端阅览
孙茜, 封皓*, 曾周末. 基于图像处理的光纤预警系统模式识别[J]. 光学精密工程, 2015,23(2): 334-341

SUN Qian, FENG Hao*, ZENG Zhou-mo. Recognition of optical fiber pre-warning system based on image processing[J]. Editorial Office of Optics and Precision Engineering, 2015,23(2): 334-341
孙茜, 封皓*, 曾周末. 基于图像处理的光纤预警系统模式识别[J]. 光学精密工程, 2015,23(2): 334-341 DOI： 10.3788/OPE.20152302.0334.

SUN Qian, FENG Hao*, ZENG Zhou-mo. Recognition of optical fiber pre-warning system based on image processing[J]. Editorial Office of Optics and Precision Engineering, 2015,23(2): 334-341 DOI： 10.3788/OPE.20152302.0334.

摘要

针对相位敏感光时域反射计(-OTDR)光纤预警系统对一维信号进行模式识别产生的误报和较低的识别效率

提出基于形态学方法提取时空二维信号特征

并利用相关向量机(RVM)分类器对事件进行分类识别的方法。首先

将-OTDR采集到的时空二维信号当作图像

根据信号在图像上的特征采用图像处理的方法对不同入侵事件信号进行阈值分割。然后

基于本文提出的特征提取方法

利用不同事件区域在幅值、面积、形状以及区域间隔上的差别提取不同信号特征。最后

利用相关向量机分类器对不同事件信号进行识别并采用"一对一"的多分类策略。对3种管道安全事件进行了实验。实验结果表明

本文提出方法的识别精度能够达到97.8%

而算法时间不到1 s。与传统模式识别方法相比

提出的算法大幅度地改善了系统性能

且简便易行

能够满足-OTDR光纤预警系统在线实时监测的要求。

Abstract

To reduce the time-consuming and misinformation of one dimensional signal recognition by the pre-warning system in a Phase-sensitivity Optical Time-domain Reflectometer(-OTDR)

a new method to acquire two dimension signals by the -OTDR pre-warning system and to recognize events based on Relative Vector Machine(RVM) classifier was proposed. Firstly

the spatial and temporal two dimension signal was taken as an image and the image processing method was used for the threshold segmentation of different events according to the image characteristics. Then

the proposed feature extraction method based on morphology was used to extract different signal features by using the amplitude

area

shape and internal of region as feature vectors. Finally

the RVM classifiers and "one to one" strategy were used for multi-class recognition. The experiments on three pipeline safety events show that the feature extraction method proposed in this paper greatly improves the recognition accuracy with less computation time

the accuracy has been reached to 97.8% and the computing time is less than 1 s. As compared with traditional methods

the algorithm has better performance

thus is very suitable for the pre-warning system online monitoring of -OTDRs.

关键词

Keywords

references

KUANG K S C, AKMALUDDIN,CANTWELL W J. Crack detection and vertical deflection monitoring in concrete beams, using plastic optical fibre sensors[J]. Measurement Science & Technology, 2013, 14(2): 205-216.

李川,刘江,庄君刚,等. 基于背向Brillouin散射监测混凝土应变[J]. 光学精密工程,2014, 22(2):325-330.

LI CH, LIU J, ZHUANG J G, et al.. Strain detection of concrete structures based on Brillouin backscattering[J]. Opt. Precision Eng., 2014, 22(2):325-330.(in Chinese)

HAO J Z, DONG B, VARGHESE P. An armored-cable-based fiber Bragg grating sensor array for perimeter fence intrusion detection[J]. SPIE, 2011,8332:83320B-1-9.

TANIMOLA F, HILL D. Distributed fibre optic sensors for pipeline protection[J]. Journal of Natural Gas Science and Engineering,2009, 1(4-5): 134-143.

徐国权, 熊代余. 光纤光栅传感技术在工程中的应用[J]. 中国光学, 2013,6(3): 306-317.

XU G Q, XIONG D Y. Applications of fiber Bragg grating sensing technology in engineering[J]. Chinese Optics, 2013,6(3): 306-317.(in Chinese)

安阳,封浩,冯欣,等.基于双光束干涉的相位敏感光时域反射计[J]. 光学学报,2013,33(7):0706005.

AN Y, FENG H, FENG X,et al.. Phase sensitive optical time domain reflectometer based on two-beam interference[J]. Acta Optica Sinica, 2013, 33(7): 0706005.(in Chinese)

吴晶, 吴晗平, 黄俊斌,等. 光纤光栅传感信号解调技术研究进展[J]. 中国光学, 2014,7(4): 519-531.

WU J, WU H P, HUANG J B, et al.. Research progress in signal demodulation technology of fiber Bragg grating sensors[J]. Chinese Optics, 2014,7(4):519-531.(in Chinese)

朱明, 杨航, 贺柏根,等. 联合梯度预测与导引滤波的图像运动模糊复原[J]. 中国光学, 2013,6(6): 850-855.

ZHU M, YANG H, HE B G, et al.. Image motion blurring restoration of joint gradient prediction and guided filter[J]. Chinese Optics, 2013,6(6): 850-855.(in Chinese)

ANYAEGBUMAM A J. Complete stresses and displacements in a cross-anisotropic half-space caused by a surface vertical point load[J]. International Journal of Geomechanics,2014, 14(2):171-181.

韩延祥,张志胜,郝飞,等.灰度序列图像中基于纹理特征的移动阴影检测[J]. 光学精密工程,2013, 21(11): 2931-2942.

HAN Y X, ZHANG ZH S, HAO F, et al..Shadow detection based on texture feature in gray sequence images[J]. Opt. Precision Eng., 2013, 21(11):2931-2942.(in Chinese)

TIPPING M E. Sparse bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research, 2001, 1(3): 211-244.

ZIO E, MAIO F D. Fatigue crack growth estimation by relevance vector machine[J]. Expert Systems with Application, 2012, 39(12): 10681-10692.

董超,田联房.最速上升关联向量机高光谱影像分类[J]. 光学精密工程,2012, 20(6):1398-1405.

DONG CH, TIAN L F. Hyperspectral image classification by steepest ascent relevance vector machine[J]. Opt. Precision Eng., 2012, 20(6): 1398-1405.(in Chinese)

ZHONG S P, CHEN D Y, XU Q F. Optimizing the Gaussian kernel function with the formulated kernel target alignment criterion for two-class pattern classification[J]. Pattern Recognition, 2013, 46(7): 2045-2054.

SCHWENKER F. Hierarchical support vector machines for multi-class pattern recognition[C]. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies, Brighton: DBLP,2000, 2: 561-565.

冯欣,曾周末,封浩,等.基于小波信息熵的分布式振动传感系统的扰动评价方法[J]. 光学学报,2013, 33(11): 116005.

FENG X, ZENG ZH M, FENG H, et al.. A method for evaluating the disturbance in distributed vibration sensor based on wavelet information entropy[J]. Acta Optica Sinica,2013, 33(11): 116005.(in Chinese)

Views

361

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Automatic identification of vulnerable plaques based on intravascular ultrasound images

Hyperspectral images feature extraction and classification based on fractional differentiation

Lightweight deep global-local knowledge distillation network for hyperspectral image scene classification

Dense pedestrian detection algorithm in multi-branch non-anchor frame network

Application of SENet generative adversarial network in image semantics description

Related Author

ZHANG Qi

WANG Yuan-yuan

MA Jian-ying

QIAN Ju-ying

SHI Jun

YAN Zhuang-zhi

LIU Jing

LI Yang

Related Institution

School of communication and information Engineering, Shanghai University

Department of Electronic Engineering, Fudan University

Department of Cardiology, Zhongshan Hospital of Fudan University

School of Electronic Engineering， Xi’an University of Posts and Telecommunications

School of Electronic Engineering， Xidian University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰