The leakage of toxic or hazardous gases not only pollutes the environment, but also threatens people's lives and property safety. Many countries attach great importance to the rapid and effective gas leak detection technology and instrument development. However, the gas leak imaging detection systems currently existing are generally limited to a narrow-band in Medium Wavelength Infrared (MWIR) or Long Wavelength Infrared (LWIR) cooled focal plane imaging, which is difficult to detect the common kinds of the leaking gases. Besides the costly cooled focal plane array is utilized, the application promotion is severely limited. To address this issue, a wide-band gas leak IR imaging detection system using Uncooled Focal Plane Array (UFPA) detector is proposed, which is composed of wide-band IR optical lens, sub-band filters and switching device, wide-band UFPA detector, video processing and system control circuit. A wide-band (3µm~12µm) UFPA detector is obtained by replacing the protection window and optimizing the structural parameters of the detector. A large relative aperture (F#=0.75) wide-band (3μm~12μm) multispectral IR lens is developed by using the focus compensation method, which combining the thickness of the narrow-band filters. The gas leak IR image quality and the detection sensitivity are improved by using the IR image Non-Uniformity Correction (NUC) technology and Digital Detail Enhancement (DDE) technology. The wide-band gas leak IR imaging detection system using UFPA detector takes full advantage of the wide-band (MWIR&LWIR) response characteristic of the UFPA detector and the digital image processing technology to provide the resulting gas leak video easy to be observed for the human eyes. Many kinds of gases, which are not visible to the naked eyes, can be sensitively detected and visualized. The designed system has many commendable advantages, such as scanning a wide range simultaneously, locating the leaking source quickly, visualizing the gas plume intuitively and so on. The simulation experiment shows that the gas IR imaging detection has great advantages and widely promotion space compared with the traditional techniques, such as point-contact or line-contactless detection.
Color fusion technology, one of the typical technologies, has been emphasized all over the world. Multiband images are
fused into a color image. Some effective visible and thermal infrared color fusion algorithms have been proposed now.
We have successfully run a real-time natural sense of visible/infrared color fusion algorithm in DSP and FPGA hardware
processing platforms. However, according to different needs, gray image fusion technology has its own unique
Based on the natural sense of color image fusion algorithm of the visible and infrared, we have proposed a visible /
infrared gray image fusion algorithm. Firstly we do a YUV color fusion. Then we output the brightness of the fusion as
gray fusion images. This algorithm for image fusion is compared with typical fusion algorithms: the weighted average,
the Laplace Pyramid and the Haar basis wavelet. Several objective evaluation indicators are selected. The results of
objective and subjective comparison show that the algorithm has most advantages. It shows that multiband gray image
fusion in the color space is available.
The algorithm is implemented on a DSP hardware image processing platform real-time with the TI's chip as the kernel
processor. It makes natural sense of color fusion and gray fusion for visible light (low level light) and thermal imaging
integrated. Users are convenient to choose model of the natural sense of color fusion or gray fusion for real-time video
As to visualize the leaking gas cloud which is not visible to the naked eyes, three categories of techniques have emerged,
Backscatter Absorption Gas Imaging, Passive Thermal Imaging, and Imaging Spectrometer. Among these systems,
Signal to Noise Ratio (SNR) is generally used to deduce gas leakage detection limit and leads to several performance
evaluation parameters, such as Noise-Equivalent Spectral Radiance and Noise-Equivalent Concentration-Path Length.
However, in most cases, measuring the SNR accurately is not accessible and usually needs auxiliary instruments.
Therefore, we focus on researching a gas leakage detection model according to the general parameter of a thermal imager,
Noise Equivalent Temperature Difference (NETD). Firstly, the Gas Equivalent Blackbody Temperature Difference
(GEBTD) is obtained by calculating the attenuated radiation of the On-plume path and that of the Off-plume path
respectively. A simplified form of GEBTD was derived by our previous paper, assuming that the work range was short
and the affection of atmospheric transmission was omitted. But in this paper, more factors are considered to establish a
more realistic and accurate detectivity model. The radiation of the gas cloud and the attenuation of the atmosphere are
taken into account as well as the size of the leakage spot which inevitably affects the concentration path length. Secondly,
the NETD and the GEBTD are compared to determine the detection capability. At last, an experiment is designed to
verify the accuracy and reliability of this model on the basis of the gas cloud concentration cone distribution model.
Passive infrared gas imaging systems have been utilized in the equipment leak detection and repair in chemical
manufacturers and petroleum refineries. The detection performance mainly relates to the sensitivity of infrared detector,
optical depth of gas, atmospheric transmission, wind speed, and so on. Based on our knowledge, the spatial concentration
distribution of continuously leaking gas plays an important part in leak detection. Several computational model of gas
diffusion were proposed by researchers, such as Gaussian model, BM model, Sutton model and FEM3 model. But these
models focus on calculating a large scale gas concentration distribution for a great amount of gas leaks above over 100-
meter height, and not applicable to assess detection limit of a gas imaging system in short range. In this paper, a wind
tunnel experiment is designed. Under different leaking rate and wind speed, concentration in different spatial positions is
measured by portable gas detectors. Through analyzing the experimental data, the two parameters σ<sub>y</sub>(x) and σ<sub>z</sub> (x) that
determine the plume dispersion in Gaussian model are adjusted to produce the best curve fit to the gas concentration
data. Then a concentration distribution model for small mount gas leakage in short range is established. Various gases,
ethylene and methane are used to testify this model.
The fingerprint region of most gases is within 3 to 14μm. A mid-wave or long-wave infrared thermal imager is therefore
commonly applied in gas detection. With further influence of low gas concentration and heterogeneity of infrared focal
plane arrays, the image has numerous drawbacks. These include loud noise, weak gas signal, gridding, and dead points,
all of which are particularly evident in sequential images. In order to solve these problems, we take into account the
characteristics of the leaking gas image and propose an enhancement method based on adaptive time-domain filtering
with morphology. The adaptive time-domain filtering which operates on time sequence images is a hybrid method
combining the recursive filtering and mean filtering. It segments gas and background according to a selected threshold;
removes speckle noise according to the median; and removes background domain using weighted difference image. The
morphology method can not only dilate the gas region along the direction of gas diffusion to greatly enhance the
visibility of the leakage area, but also effectively remove the noise, and smooth the contour. Finally, the false color is
added to the gas domain. Results show that the gas infrared region is effectively enhanced.
Leakage of dangerous gases will not only pollute the environment, but also seriously threat public safety. Thermal infrared
imaging has been proved to be an efficient method to qualitatively detect the gas leakage. But some problems are remained,
especially when monitoring the leakage in a passive way. For example, the signal is weak and the edge of gas cloud in the
infrared image is not obvious enough. However, we notice some important characteristics of the gas plume and therefore
propose a gas cloud infrared image enhancement method based on anisotropic diffusion. As the gas plume presents a large
gas cloud in the image and the gray value is even inside the cloud, strong forward diffusion will be used to reduce the noise
and to expand the range of the gas cloud. Frames subtraction and K-means cluttering pop out the gas cloud area.
Forward-and-Backward diffusion is to protect background details. Additionally, the best iteration times and the time step
parameters are researched. Results show that the gas cloud can be marked correctly and enhanced by black or false color,
and so potentially increase the possibility of gas leakage detection.
Standoff detection of gas leakage is a fundamental need in petrochemical and power industries. The passive gas imaging
system using thermal imager has been proven to be efficient to visualize leaking gas which is not visible to the naked
eye. The detection probability of gas leakage is the basis for designing a gas imaging system. Supposing the performance
parameters of the thermal imager are known, the detectivity based on electromagnetic radiation transfer model to image
gas leakage is analyzed. This model takes into consideration a physical analysis of the gas plume spread in the
atmosphere-the interaction processes between the gas and its surrounding environment, the temperature of the gas and
the background, the background surface emissivity, and also gas concentration, etc. Under a certain environmental
conditions, through calculating the radiation reaching to the detector from the camera's optical field of view, we obtain
an entity "Gas Equivalent Blackbody Temperature Difference (GEBTD)" which is the radiation difference between the
on-plume and off-plume regions. Comparing the GEBTD with the Noise Equivalent Temperature Difference (NETD) of
the thermal imager, we can know whether the system can image the gas leakage. At last, an example of detecting CO<sub>2</sub> gas by JADE MWIR thermal imager with a narrow band-pass filter is presented.