基于图像处理的光纤预警系统模式识别

孙茜; 封皓; 曾周末

doi:10.3788/OPE.20152302.0334

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基于图像处理的光纤预警系统模式识别

现代应用光学 | 更新时间：2020-08-13

- 基于图像处理的光纤预警系统模式识别
- Recognition of optical fiber pre-warning system based on image processing
- 光学精密工程 2015年23卷第2期页码：334-341
- 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：
  
  国家自然科学基金青年基金资助项目(No.61304244)()
- DOI：10.3788/OPE.20152302.0334
  中图分类号： TN973;TP391
- 收稿日期：2014-08-19，
  
  修回日期：2014-09-29，
  
  纸质出版日期：2015-02-25
- 稿件说明：
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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

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