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笔者设计了一款差分式光谱滤波气体泄漏红外成像检测系统(如图21所示)。系统采用烟台艾睿宽波段非制冷IRFPA (LA6110-PL16113 S00)和低照度彩色或黑白CMOS成像组件(Crevis MV-BX30,像元规模1024×768,像元尺寸4.65 μm,8 bit Camera Link输出)。在红外物镜(如图22所示,焦距40 mm,F数1.0)后截距中安装六孔转轮(如图23所示,BP、LP和SP分别表示带通、高通和低通滤光片),实现最高100 r/min的光谱调制。红外和可见光视频图像通过FPGA图像处理板(如图24所示)进行预处理和融合,同时控制滤光转轮旋转及定位、视频图像采集、数字视频图像传输、人机交互等。
图 21 差分式光谱滤波的气体泄漏红外成像检测
Figure 21. Infrared imaging detection of gas leakage by differential spectral filtering
研制的样机系统如图25所示,六个滤光片的组合可检测的典型工业气体种类如表1所示。图26给出了采用长通滤光片获得的气体泄漏热图像及渲染效果。可以看出:采用长通滤光片获得的图像信噪比较高,气体云团的检测效率也更佳。
表 1 六个滤光片组合可检测的气体种类
Table 1. Gas type detectable by 6 filter combination
Filter combination Spectral band Gas type 1-2 6-7.49 Ethylene, Butane, Toluene, Carbon disulfide 2-3 7.49-8.11 Methane, Nitrous Oxide, Ethylene oxide,
Ethyl mercaptan3-4 8.11-9 Difluoroethane, phenol,
2-propionic acid4-5 9-10 Ozone, Ammonia, Cyclopropane 6-1+5 10-11 Sulfur hexafluoride, Ethylene, Acrylonitrile 1-5 11-12.5 Ethylene oxide, Phosgene, Carbon oxysulfide VIS+IR Water vapor, heat convection -
图像差分是光谱滤波式红外成像模式最大的特点之一,其采用两个截止波长不同的高通滤光片分别进行成像,并由两者的差异解算出两个截止波长差之间辐射差异图像,相对于直接采用窄波段滤波成像,能够获得更多的入射辐射量。因此,成像信噪比较高,且后续处理属于典型的计算成像范畴,利用现代数字图像处理技术可望获得更好的成像检测效果、更弱的泄漏气体痕迹或更远的气体泄漏检测距离。
除常规的红外焦平面成像的图像处理方法外,需特别关注以下几个环节:
(1) 单通道成像的非均匀性校正
各成像通道的通光波段变化将带来非均匀性校正系数矩阵的变化,因此,需要进行六个通道的辐射定标。考虑到检测环境及气体目标温度范围,采用常温黑体进行二点定标模型,以确定每一个通道的非均匀性校正系数增益矩阵
$ {a_i}(i,j) $ 和偏置矩阵$ {b_i}(i,j) $ ,并在实际使用过程中采用挡板或基于场景的自适应非均匀性校正方法完成校正系数矩阵的修正。该类方法已有较多的研究,这里主要进行算法的引用。(2) 不同波段图像之间的均衡化处理
由于系统采用不同高通滤波成像之差构成气体泄漏检测图像,各通道虽然已进行了各自的非均匀性校正,但各通道的响应(即增益与偏置)并不一致,因此,需要对各通道图像进行均衡化处理。
若通道1和2的两点校正模型分别为:
$$ \begin{gathered} {y_1}(i,j) = {a_1}(i,j){x_1}(i,j) + {b_1}(i,j) \\ {y_2}(i,j) = {a_2}(i,j){x_2}(i,j) + {b_2}(i,j) \\ \end{gathered} $$ (1) 式中:
$ {x_1}(i,j) $ 和$ {x_{\text{2}}}(i,j) $ 、$ {y_1}(i,j) $ 和$ {y_2}(i,j) $ 分别为通道1和通道2探测器输出的原始数据以及经两点校正的数据;$ {a_1}(i,j) $ 和$ {a_2}(i,j) $ 、$ {b_1}(i,j) $ 和$ {b_2}(i,j) $ 分别为通道1和通道2的增益和偏置矩阵。为使通道2的响应与通道1一致,需再进行通道间的线性变换—二次均衡校正:以通道1为参考基准,使通道2校正模型与通道1一致。设对通道2进行二次均衡后的输出数据为
$ {y_3}(i,j) $ ,对应的增益和偏振矩阵分别为$ {a_3}(i,j) $ 和$ {b_3}(i,j) $ 。若使其输出数据与通道1校正后的响应一致,则有:$$ \begin{split} {y_3}(i,j) =& {a_3}(i,j){y_2}(i,j) + {b_3}(i,j)= \\ & {a_1}(i,j){x_2}(i,j) + {b_1}(i,j) \end{split} $$ (2) 将公式(1)与公式(2)对比,可得到:
$$ \left\{ {\begin{array}{*{20}{c}} {{a_3}(i,j) = \dfrac{{{a_1}(i,j)}}{{{a_2}(i,j)}}{\text{ }}} \\ {{b_3}(i,j) = {b_1}(i,j) - \dfrac{{{a_1}(i,j)}}{{{a_2}(i,j)}}{b_2}(i,j)} \end{array}} \right. $$ (3) 故经二次均衡校正后,两个通道输出相减构成的带通滤波输出为:
$$ \begin{split} \Delta y(i,j) =& {y_1}(i,j) - {y_3}(i,j) =\\ & {a_1}(i,j)\{ {x_1}(i,j) - {x_2}(i,j)\} \end{split} $$ (4) (3) 泄漏气体痕迹提取方法
由于实际石油天然气设施环景中往往存在人员走动、随风飘动的树木花草和运动车辆等复杂运动背景,对泄漏气体的判断会造成明显干扰。为此,采用帧间差分图像(带通滤波模式)或差分光谱图像(差分光谱滤波模式),利用支持向量机(Support Vector Machine,SVM)和深度学习等智能处理方法有效提取气体痕迹(如图27所示),准确率可达92.5%[51]。
图 27 采集样本。 (a)~(c) 泄漏气体;(d)~(f) 人、树木、抽油机等干扰
Figure 27. Samples collected. (a)~(c) Leaking gas; (d)~(f) Interference from people, trees, pumping units, etc.
(4) 可见光+红外通道融合图像处理
工业气体泄漏红外成像检测系统通常包含红外和可见光通道,除可提供人们熟悉的可见光监视场景外,同时提供不可探测的泄漏气体动态痕迹红外图像和融合图像,以及采用伪彩色渲染气体痕迹表示不同浓度范围。另外,可见光与红外图像融合还可提高对气体痕迹与水蒸气等的检测能力(如图28所示)。
图 28 包含水蒸气的可见光与泄漏气体痕迹红外图像的融合
Figure 28. Fusion of visible light containing water vapor and infrared image of leaking gas traces
(5) 泄漏气体识别与浓度的预测方法
不同的光谱滤光片可用于不同泄漏气体的探测和识别,并根据泄漏气体痕迹的标定实现从痕迹图像灰度到气体浓度的估计。
目前已进行了甲烷、二氟乙烷、六氟化硫、汽油(如图29所示)、乙烯等气体的有效探测,并可通过假彩色渲染气体的浓度,但具体的浓度水平估计依赖于实际标定方法与过程。
Research on infrared imaging detection and differential spectrum filtering detection methods for industrial gas leakage
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摘要: 以石油天然气为代表的工业气体已深入到人们生活和生产过程中,工业气体泄漏成为当前工业生产、交通运输等领域发生重大灾害的现象之一,同时甲烷排放也成为我国“碳排放”达标战略目标的主要指标,快速有效的气体泄漏检测技术和仪器成为当今国内外研究的重点。随着近年来非制冷红外焦平面探测器性能的提高,其低成本、长寿命和高可靠性能满足工业气体泄漏红外成像检测昼夜连续工作的应用需要,并发展出多种气体泄漏红外成像检测模式。在分析不同工业气体泄漏红外成像检测模式的基础上,设计并研制了基于差分光谱滤波的工业气体泄漏红外成像检测实验系统,分析提出了五个需要重点研究的视频图像处理方法,给出了相关的处理模型或典型处理示例。试验结果表明该成像检测模式具有较高的灵敏度,是一种有效的气体泄漏红外成像检测技术。Abstract: Industrial gases, represented by oil and natural gas, have penetrated people's lives and production processes. Gas leakage has become one of the major disasters in current industrial production, transportation and other fields. Meanwhile, methane emissions have become the main target of China’s "carbon emissions" strategic goal. Rapid and effective gas leakage detection technology and instruments have become the focus of research at home and abroad. In response to the improvement of the performance of the uncooled infrared focal plane array (IRFPA) in recent years, its low cost, long life and high reliability can adapt to industrial gas leakage of infrared imaging detection requirements of continuous work day and night, and a variety of gas leakage infrared imaging detection modes have been developed. Based on the analysis of different infrared imaging detection modes of industrial gas leakage, this paper designs and develops an infrared imaging detection experimental system of industrial gas leakage based on differential spectral filtering, analyses and puts forward five video image processing methods that need to be studied, and gives relevant processing models or typical processing examples. The results show that the imaging detection mode has the characteristics of high sensitivity and is an effective infrared imaging detection technology for gas leakage.
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表 1 六个滤光片组合可检测的气体种类
Table 1. Gas type detectable by 6 filter combination
Filter combination Spectral band Gas type 1-2 6-7.49 Ethylene, Butane, Toluene, Carbon disulfide 2-3 7.49-8.11 Methane, Nitrous Oxide, Ethylene oxide,
Ethyl mercaptan3-4 8.11-9 Difluoroethane, phenol,
2-propionic acid4-5 9-10 Ozone, Ammonia, Cyclopropane 6-1+5 10-11 Sulfur hexafluoride, Ethylene, Acrylonitrile 1-5 11-12.5 Ethylene oxide, Phosgene, Carbon oxysulfide VIS+IR Water vapor, heat convection -
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