基于张量低秩分解和稀疏表示的红外微小气体泄漏检测

隋中山; 李俊山; 张姣; 隋晓斐

doi:10.3788/OPE.20162411.2855

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基于张量低秩分解和稀疏表示的红外微小气体泄漏检测

信息科学 | 更新时间：2020-07-07

- 基于张量低秩分解和稀疏表示的红外微小气体泄漏检测
- Micro gas leakage detection based on tensor low rank decomposition and sparse representation from infrared images
- 光学精密工程 2016年24卷第11期页码：2855-2862
- 作者机构：
  
  1.火箭军工程大学, 信息工程系, 陕西西安 710025
  2.96618部队, 北京 100085
- 作者简介：
  
  隋中山(1985-),男,山东平度人,博士研究生,2007年、2009年于第二炮兵工程学院分别获得学士、硕士学位,主要从事图像目标识别方面的研究. E-mail:zclszs@163.com. E-mail:zclszs@163.com.
  [ "李俊山(1956-),男,陕西白水人,教授,博士生导师,1981于国防科技大学获得学士学位,1988年于第二炮兵工程学院获得硕士学位,2001年于西安微电子技术研究所获得博士学位,主要从事智能图像处理与目标感知识别,电子对抗模拟与仿真等方面的研究。E-mail:lijunshan403@163.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(No.61175120)
- DOI：10.3788/OPE.20162411.2855
  中图分类号： TP391.4;TN215
- 收稿日期：2016-07-11，
  
  录用日期：2016-8-12，
  
  纸质出版日期：2016-11-25
- 稿件说明：
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隋中山, 李俊山, 张姣, 等. 基于张量低秩分解和稀疏表示的红外微小气体泄漏检测[J]. 光学精密工程, 2016,24(11):2855-2862.

Zhong-shan SUI, Jun-shan LI, jiao ZHANG, et al. Micro gas leakage detection based on tensor low rank decomposition and sparse representation from infrared images[J]. Editorial office of optics and precision engineeri, 2016, 24(11): 2855-2862.
隋中山, 李俊山, 张姣, 等. 基于张量低秩分解和稀疏表示的红外微小气体泄漏检测[J]. 光学精密工程, 2016,24(11):2855-2862. DOI： 10.3788/OPE.20162411.2855.

Zhong-shan SUI, Jun-shan LI, jiao ZHANG, et al. Micro gas leakage detection based on tensor low rank decomposition and sparse representation from infrared images[J]. Editorial office of optics and precision engineeri, 2016, 24(11): 2855-2862. DOI： 10.3788/OPE.20162411.2855.

摘要

为了检测石化工业生产过程中微小气体的泄漏，提出了一种应用红外成像技术的单帧红外小目标检测方法。研究了低秩稀疏分解理论和稀疏表示理论，并提出了一种新的基于张量低秩分解和稀疏表示的小目标检测方法。该方法基于张量分解的形式充分发掘背景矩阵所包含的信息；利用先验知识构造微小气体泄漏的目标字典；同时利用背景的低秩约束和小目标的稀疏表示约束分解出微小气体的泄漏目标。最后基于非精确增广拉格朗日乘子法（IALM），对本文算法进行最优化求解，并通过实验分析比较了本文方法和已有方法的优缺点。结果表明：本文方法的检测效果优于其他已有方法，并且具有较好的ROC（受试者工作特征）曲线，可以满足工业生产中对微小气体泄漏检测的要求。

Abstract

To detect the micro gas leakage in petrochemical production

a single-frame small target detection method was proposed by using infrared images. The low-rank sparse decomposition theory and sparse representation theory were researched and an innovative method to detect a micro-target was proposed based on tensor low-rank decomposition and sparse representation. The tensor decomposition form was employed in exploiting the information contained in background matrices

The priori knowledge was used to construct a micro gas leakage target dictionary

meanwhile

the micro-gas leakage targets were decomposed by low-rank constraint in the background and sparse representation in the micro-target. Finally

the algorithm was solved optimally by using Inexact Augmented Lagrange Multiplier(IALM) method and its merits were compared with that of common methods. The results indicate that the proposed algorithm has better detection efficiency than that of common methods and it shows better ROC (Receiver Operating Characteristics)curves. It concludes that these results meet the requirements of micro gas leakage detection during industrial productions.

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references

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