Micro gas leakage detection based on tensor low rank decomposition and sparse representation from infrared images

Zhong-shan SUI; Jun-shan LI; jiao ZHANG; Xiao-fei SUI

doi:10.3788/OPE.20162411.2855

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Micro gas leakage detection based on tensor low rank decomposition and sparse representation from infrared images

Information science | 更新时间：2020-07-07

- Micro gas leakage detection based on tensor low rank decomposition and sparse representation from infrared images
- Editorial Office of Optics and Precision Engineeri Vol. 24, Issue 11, Pages: 2855-2862(2016)
- 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：
- 基金信息：
- DOI：10.3788/OPE.20162411.2855
  CLC： TP391.4;TN215
- Received：11 July 2016，
  
  Accepted：12 August 2016，
  
  Published：25 November 2016
- 稿件说明：
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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：

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

何玉杰,李敏,张金利,等. 基于低秩三分解的红外图像杂波抑制[J]. 光学精密工程, 2015,23(7):2069-2078.

HE Y J, LI M, ZHANG J L, et al.. Clutter suppression of infrared image based on there-component low-rank matrix decomposition[J].Opt. Precision Eng., 2015,23(7):2069-2078.(in Chinese)

赵爱罡,王宏力,杨小冈,等. 基于结构低秩编码的复杂环境红外弱小目标检测算法[J]. 中国惯性技术学报,2015,23(5):662-669.

ZHAO A G, WANG H L, YANG X G, et al.. Infrared dim small target detection algorithm based on structural low-rank coding under complex environment[J]. Journal of Chinese Inertial Technology,2015,23(5):662-669.(in Chinese)

TOM V T, PELI T, LEUNG M, et al.. Morphology-based algorithm for point target detection in infrared backgrounds[C]. Optical Engineering and Photonics in Aerospace Sensing. International Society for Optics and Photonics,1993:2-11.

DESHPANDE S D, MENG H E, VENKATESWARLU R, et al.. Max-mean and max-median filters for detection of small targets[C].SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999:74-83.

HAN J H, MA Y, ZHOU B, et al.. A robust infrared small target detection algorithm based on human visual system[J]. Geoscience and Remote Sensing Letters, 2014,11(12):2168-2172.

QI S X, MA J, TAO C, et al.. A robust directional saliency-based method for infrared small target detection under various complex backgrounds[J].IEEE Geoscience and Remote Sensing Letters, 2013,10(3):495-499.

YANG C W, LIU H P,LIAO S Y, et al.. Small target detection in infrared video sequence using robust dictionary learning[J]. Infrared Physics & Technology,2015, 68:1-9.

ZHENG C Y, LI H. Small infrared target detection based on low-rank and sparse matrix decomposition[J]. Applied Mechanics and Materials,2013, 239:214-218.

HE Y J, LI M, ZHANG J L, et al.. Small infrared target detection based on low-rank and sparse representation[J].Infrared Physics & Technology, 2015, 68:98-109.

LIU X, ZHAO G Y. Background subtraction based on low-rank and structured sparse decomposition[J].IEEE Transactions on Image Processing DRAFT,2015.

YAO J, LIU X, QI C. Foreground detection using low rank and structured sparsity[C]. Proc. IEEE Int. Conf. Multimed.Expo., 2014:1-6.

BOUWMANS T, ZAHZAH E H. Robust PCA via principal component pursuit:A review for a comparative evaluation in video surveillance[J]. Comput. Vis. Image Underst., 2014, 122:22-34.

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