Multispectral infrared imaging gas detection method based on microlens and optical filter array

Abstract

The invention discloses a method for detecting industrial hazardous gas leakage based on a multispectral infrared imaging system formed by a micro-lens array and a partition optical filter, which is used for solving the problem that the current infrared imaging equipment cannot identify various leaked gases, and belongs to the technical field of infrared imaging. The multispectral infrared imaging system is constructed on the basis of the microlens array and the partition optical filter, multispectral band design is carried out on common gas in the current petrochemical industry field, and the types of common leaked gas are identified by means of spectral characteristics of the leaked gas in an infrared multispectral channel. Firstly, a snapshot type infrared imaging system is constructed based on a micro-lens array and a partition optical filter, and the system comprises a plurality of spectral intervals which respectively correspond to optical absorption peaks of specific gas; imaging the hazardous gas based on a multispectral infrared imaging system to obtain a multispectral infrared image of a hazardous gas leakage scene; and finally, based on a foreground detection method, extracting gray information of the gas cloud target, constructing a spectral absorption normalization curve of the gas cloud target in each channel, and comparing the spectral absorption normalization curve with the spectral absorption curve in the database to judge the gas type of the current gas cloud target. The invention provides a reliable multispectral imaging scheme for a hazardous gas imaging system based on infrared imaging, gas types can be effectively distinguished during actual detection, interference of safe gases such as carbon dioxide and water vapor is eliminated, and the detection capability and practicability of an infrared imaging hazardous gas detection system are improved.

Description

Technical Field

[0001] The present invention relates to a multi-spectral infrared imaging gas detection method based on a microlens and a filter array, and in particular to a multi-spectral infrared imaging system technology, which is suitable for multi-spectral infrared gas detection of industrial hazardous gas leakage, and belongs to the field of infrared imaging and gas detection technology. Background Art

[0002] Industrial hazardous gas leak detection technology is a technology widely used in the industrial field. It is usually used to detect leaked gas based on the physical or chemical properties of the leaked gas. Traditional gas detection sensors use contact measurement methods, but they need to be deployed in large numbers at various potential leak points, and the leak points cannot be located intuitively. The infrared imaging gas detection technology developed in recent years is based on the infrared spectral absorption characteristics of leaked gas. Detectors are designed for detection in specific infrared bands absorbed by the gas spectrum. It can intuitively display traces of leaked gas, making it easier to determine the scale of the leak and locate the leak point, and the method of use is more intuitive.

[0003] Currently, the main application is the gas detection equipment based on the single-band infrared imaging system. Although it can observe the tail of dangerous gas leakage that the visible light band imaging system cannot capture, there is usually more than one type of leaking gas in actual industrial scenarios, and it includes safe gases such as water vapor and carbon dioxide, which will interfere with the infrared image and make the system unable to distinguish between dangerous gases and safe gases, posing a challenge to the existing single-spectrum infrared imaging gas detection technology.

[0004] Multi-spectral infrared imaging technology usually has several to dozens of infrared imaging spectra, which can be divided into several independent bands within the 3-14μm band for imaging, and targeted imaging is performed for the characteristics of a certain gas with multiple infrared absorption peaks within the 3-14μm band. For example, methane gas usually has absorption in the 3.2-3.4μm and 6-8μm bands, so by configuring a multi-spectral infrared imaging system to divide the 3-14μm band into multiple bands with equal width, the current gas can be identified as methane through the gas cloud traces of methane gas in the 3-4μm and 6-7μm bands.

[0005] Hazardous gas detection technology based on multispectral infrared imaging technology allows equipment users to effectively distinguish between various types of leaked gases and eliminate interference from common gases such as water vapor and carbon dioxide, effectively improving the detection capabilities of current infrared imaging gas detection technology. In order to improve the level of automated monitoring of hazardous gases, it is also necessary to design a solution for automated identification of leaked gas types based on a multispectral infrared imaging system and by utilizing the information differences in multispectral infrared images, so as to facilitate the implementation of automatic alarm technology for equipment in actual use scenarios and improve the automation level of hazardous gas monitoring systems. Summary of the invention

[0006] The purpose of the present invention is to provide a multi-spectral infrared imaging gas detection method based on a microlens array and a partition filter, which is used for gas cloud target detection of dangerous gas leakage.

[0007] The objective of the present invention is achieved through the following technical solutions:

[0008] A multi-spectral infrared imaging gas detection method using a microlens array and a partition filter is provided, comprising the following steps:

[0009] S1. Design and build a multi-spectral infrared imaging system based on microlens array and partition filter. The core components of the system include an uncooled infrared focal plane detector, a microlens array corresponding to the focal plane array, and a partition filter. The microlens array refers to a microlens array corresponding to the image plane of the infrared detector. It corresponds to the imaging area of ​​the infrared detector. M×N lens groups are designed to form an array to divide the scene radiation into multiple channels for imaging. The partition filter refers to a filter array corresponding to the microlens array. Through partitioning or filter splicing, it corresponds to M×N lens groups to provide band selection for the imaging light path of a single lens group. The advantage of building a multi-spectral infrared imaging system based on microlens array and partition filter is that each band is imaged on the same detector. When analyzing the spectral absorption characteristics of gas cloud targets in each band in the subsequent steps, the imaging characteristics of multi-band images are consistent, and the spectral characteristics of gas cloud targets can be accurately analyzed according to the image grayscale.

[0010] A single lens group in the microlens array is composed of one or more infrared lenses. The lens parameters of the single lens group are optimized according to the transmitted wavelength to achieve the best imaging effect. A single filter is set in front of the single lens group to select the wavelength of the light input to the lens group, realizing spectral imaging combined with the lens group and the filter.

[0011] S2, based on the microlens array multispectral infrared imaging system, the image of dangerous gas leakage is collected, and the multispectral image is registered according to the characteristics of the scene; then the multi-channel image is preprocessed to eliminate the stripe noise and Gaussian noise in the image. Then the M×N channel images are spliced ​​to obtain the complete image Y containing each channel.

[0012] S3, use the foreground detection method based on Gaussian Mixture Model (GMM) to statistically model the image Y, obtain the static background image X, and then use the current frame Y of the image t With static background X t Subtract and obtain the grayscale image G of the gas cloud target in each channel t Based on the grayscale image G of the gas cloud target t , select the gas cloud area, and for each pixel G in the gas cloud areat (i, j) to find the average value and obtain the average grayscale P of the gas cloud absorption in the current spectral channel k ;

[0013] In order to obtain the average grayscale absorption of each channel gas cloud {P1, P2, P3···P k}, the spectral absorbance of each channel is normalized according to the grayscale to obtain the spectral absorbance curve Q of each band.

[0014] S4, match the gas type through the gas cloud target spectral absorbance curve. Based on the gas cloud absorbance normalization curve R obtained in step S3, the spectral correlation coefficient is calculated with the gas standard spectral database, and the gas with the highest correlation coefficient in the database is taken as the gas type in the current imaging system. In order to use the gas standard database, it is necessary to modify the standard database to a dedicated database suitable for the multi-spectral imaging system proposed in step S1. The specific steps are as follows: based on the public or self-collected gas standard database, the high-resolution spectral curve T of a certain gas A is matched. o According to the channel bands of the multi-spectral imaging system proposed in step S1, high-resolution spectral curves T o The discrete spectral transmittance values ​​are averaged to obtain the average gas absorptivity of the corresponding band. Based on the average absorptivity of each band, a dedicated absorptivity normalization curve T is obtained for comparison with the gas cloud absorptivity normalization curve R obtained in step S3.

[0015] The beneficial effects of the present invention are:

[0016] The multi-spectral infrared imaging gas detection method based on microlens array and partition filter is based on a single uncooled infrared focal plane detector and a microlens array and a partition filter array to form a multi-spectral infrared imaging system. It effectively utilizes the ability of a single detector to unify the response characteristics, and is convenient for calculating the normalized absorption rate curve of each band spectrum and comparing it with a dedicated gas spectrum normalized absorption rate curve database to determine the type of current gas. When applied to the field of gas detection, it can solve the problem that the existing infrared imaging system cannot effectively distinguish the type of gas, effectively improve the gas detection capability of the infrared imaging system, and the proposed method can perform automatic monitoring of gas leaks, improving the automatic detection level of the gas cloud imaging system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is an imaging structure diagram of a multi-spectral infrared imaging gas detection method based on a microlens array and a partition filter according to an embodiment of the present invention;

[0018] Figure 2This is a schematic diagram of imaging for industrial scene gas leakage detection based on a multi-spectral infrared imaging gas detection system based on a microlens array and a partition filter proposed in an embodiment of the present invention;

[0019] Figure 3 This is a multispectral image of difluoroethane leakage collected by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to better illustrate the purpose and advantages of the present invention, the invention is further described below with reference to the accompanying drawings and examples.

[0021] Embodiment 1:

[0022] like Figure 1 As shown, this embodiment discloses a multi-spectral infrared imaging gas detection method based on a microlens array and a partition filter, and the specific implementation steps are as follows:

[0023] S1. Design and build a multi-spectral infrared imaging system based on microlens array and partition filter. The core components of the system include an uncooled infrared focal plane detector, a microlens array corresponding to the focal plane array, and a partition filter. The imaging optical path is as follows: Figure 2 As shown; wherein the microlens array refers to the microlens array corresponding to the image plane of the infrared detector, corresponding to the imaging area of ​​the infrared detector, and 3×2 groups of lens groups are designed to form an array for dividing the scene radiation into multiple channels for imaging; wherein the partition filter refers to the filter array corresponding to the microlens array, and 3×2 groups of filters are designed by partitioning or filter splicing, corresponding to 3×2 groups of lens groups, to provide band selection for the imaging light path of a single lens group.

[0024] A single lens group in the microlens array consists of two infrared lenses. The lens parameters of a single lens group are optimized according to the transmitted band design to achieve the best imaging effect. A single filter is set in front of a single lens group to select the band of light input to the lens group, realizing spectral imaging combined with lens group and filter. The 3×2 groups of filters are designed with bands of 3-5μm (channel 1), 6-8μm (channel 2), 7-9μm (channel 3), 9-11μm (channel 4), 10-12μm (channel 5), and 12-14μm (channel 6). 。

[0025] S2, based on the microlens array multispectral infrared imaging system, the image of dangerous gas leakage is collected, and the multispectral image is registered according to the characteristics of the scene; then the multi-channel image is preprocessed to eliminate the stripe noise and Gaussian noise in the image. Then the 3×2 channel images are spliced ​​to obtain a complete image containing each channel, such as Figure 3 shown.

[0026] S3, use the foreground detection method based on Gaussian Mixture Model (GMM) to statistically model the image Y, obtain the static background image X, and then use the current frame Y of the image t With static background X t Subtract and obtain the grayscale image G of the gas cloud target in each channel t Based on the grayscale image G of the gas cloud target t , select the gas cloud area, and for each pixel G in the gas cloud area t (i, j) to find the average value and obtain the average grayscale P of the gas cloud absorption in the current spectral channel k ;

[0027] In order to obtain the average grayscale absorption of each channel gas cloud {P1, P2, P3···P k}, the spectral absorbance of each channel is normalized according to the grayscale to obtain the spectral absorbance curve Q of each band.

[0028] S4, match the gas type through the gas cloud target spectral absorbance curve. Based on the gas cloud absorbance normalization curve R obtained in step S3, the spectral correlation coefficient is calculated with the gas standard spectral database, and the gas with the highest correlation coefficient in the database is taken as the gas type in the current imaging system. In order to use the gas standard database, it is necessary to modify the standard database to a dedicated database suitable for the multi-spectral imaging system proposed in step S1. The specific steps are as follows: based on the public or self-collected gas standard database, the high-resolution spectral curve T of a certain gas A is matched. o According to the channel bands of the multi-spectral imaging system proposed in step S1, high-resolution spectral curves T o The discrete spectral transmittance values ​​are averaged to obtain the average gas absorptivity of the corresponding band. Based on the average absorptivity of each band, a dedicated absorptivity normalization curve T is obtained for comparison with the gas cloud absorptivity normalization curve R obtained in step S3.

[0029] The specific description above further illustrates the purpose, technical solutions and beneficial effects of the invention in detail. It should be understood that the above is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-spectral infrared imaging gas detection method using a microlens array and a partition filter, characterized in that: The following steps are included: S1. Design and build a multi-spectral infrared imaging system based on a microlens array and a partition filter. The core components of the system include an uncooled infrared focal plane detector, a microlens array corresponding to the focal plane array, and a partition filter. The microlens array refers to an array of micro lenses corresponding to the image plane of the infrared detector. M×N groups of lenses are designed to form an array corresponding to the imaging area of ​​the infrared detector, which is used to divide the scene radiation into multiple channels for imaging. The partition filter refers to the filter array corresponding to the microlens array, which corresponds to the M×N lens group through partitioning or filter splicing, and provides band selection for the imaging light path of a single lens group. The advantage of building a multi-spectral infrared imaging system based on microlens array and partition filter is that each band is imaged on the same detector. When analyzing the spectral absorption characteristics of gas cloud targets in each band in the subsequent steps, the imaging characteristics of the multi-band images are consistent, and the spectral characteristics of the gas cloud targets can be accurately analyzed according to the image grayscale. A single lens group in the microlens array is composed of one or more infrared lenses. The lens parameters of the single lens group are optimized according to the transmitted wavelength to achieve the best imaging effect. A single filter is set in front of the single lens group to select the wavelength of the light input to the lens group, realizing spectral imaging combined with the lens group and the filter. S2, based on the microlens array multispectral infrared imaging system, the image of dangerous gas leakage is collected, and the multispectral image is registered according to the characteristics of the scene; then the multi-channel image is preprocessed to eliminate the stripe noise and Gaussian noise in the image. Then the M×N channel images are spliced ​​to obtain the complete image Y containing each channel. S3, use the foreground detection method based on Gaussian Mixture Model (GMM) to statistically model the image Y, obtain the static background image X, and then use the current frame Y of the image t With static background X t Subtract and obtain the grayscale image G of the gas cloud target in each channel t Based on the grayscale image G of the gas cloud target t , select the gas cloud area, and for each pixel G in the gas cloud area t (i, j) to find the average value and obtain the average grayscale P of the gas cloud absorption in the current spectral channel k ; In order to obtain the average grayscale absorption of each channel gas cloud {P1, P2, P3···P k }, the spectral absorbance of each channel is normalized according to the grayscale to obtain the spectral absorbance curve Q of each band. S4, match the gas type through the gas cloud target spectral absorbance curve. Based on the gas cloud absorbance normalization curve R obtained in step S3, the spectral correlation coefficient is calculated with the gas standard spectral database, and the gas with the highest correlation coefficient in the database is taken as the gas type in the current imaging system. In order to use the gas standard database, it is necessary to modify the standard database to a dedicated database suitable for the multi-spectral imaging system proposed in step S1. The specific steps are as follows: based on the public or self-collected gas standard database, the high-resolution spectral curve T of a certain gas A is matched. o According to the channel bands of the multi-spectral imaging system proposed in step S1, high-resolution spectral curves T o The discrete spectral transmittance values ​​are averaged to obtain the average gas absorptivity of the corresponding band. Based on the average absorptivity of each band, a dedicated absorptivity normalization curve T is obtained for comparison with the gas cloud absorptivity normalization curve R obtained in step S3.

2. A multi-spectral infrared imaging gas detection method using a microlens array and a partition filter as claimed in claim 1, characterized in that: In step S1, Design and build a multi-spectral infrared imaging system based on microlens array and partition filter. The core components of the system include an uncooled infrared focal plane detector, a microlens array corresponding to the focal plane array, and a partition filter. The microlens array refers to an array of micro lenses corresponding to the image plane of the infrared detector. M×N groups of lenses are designed to form an array corresponding to the imaging area of ​​the infrared detector, which is used to divide the scene radiation into multiple channels for imaging. The partition filter refers to the filter array corresponding to the microlens array, which corresponds to the M×N lens group through partitioning or filter splicing, and provides band selection for the imaging light path of a single lens group. The advantage of building a multi-spectral infrared imaging system based on microlens array and partition filter is that each band is imaged on the same detector. When analyzing the spectral absorption characteristics of gas cloud targets in each band in the subsequent steps, the imaging characteristics of the multi-band images are consistent, and the spectral characteristics of the gas cloud targets can be accurately analyzed according to the image grayscale. A single lens group in the microlens array is composed of one or more infrared lenses. The lens parameters of the single lens group are optimized according to the transmitted wavelength to achieve the best imaging effect. A single filter is set in front of the single lens group to select the wavelength of the light input to the lens group, realizing spectral imaging combined with the lens group and the filter.

3. A multi-spectral infrared imaging gas detection method using a microlens array and a partition filter as claimed in claim 2, characterized in that: Use the foreground detection method based on Gaussian Mixture Model (GMM) to statistically model the image Y, obtain the static background image X, and then use the current frame Y t With static background X t Subtract and obtain the grayscale image G of the gas cloud target in each channel t Based on the grayscale image G of the gas cloud target t , select the gas cloud area, and for each pixel G in the gas cloud area t (i, j) to find the average value and obtain the average grayscale P of the gas cloud absorption in the current spectral channel k ; In order to obtain the average grayscale absorption of each channel gas cloud {P1, P2, P3···P k }, the spectral absorbance of each channel is normalized according to the grayscale to obtain the spectral absorbance curve Q of each band.

4. A multi-spectral infrared imaging gas detection method using a microlens array and a partition filter as claimed in claim 3, characterized in that: Match the gas type through the gas cloud target spectral absorbance curve. Based on the gas cloud absorbance normalization curve R obtained in step S3, the spectral correlation coefficient is calculated with the gas standard spectral database, and the gas with the highest correlation coefficient in the database is taken as the gas type in the current imaging system. In order to use the gas standard database, it is necessary to modify the standard database to a dedicated database suitable for the multi-spectral imaging system proposed in step S1. The specific steps are as follows: based on the public or self-collected gas standard database, the high-resolution spectral curve T of a certain gas A is matched. o According to the channel bands of the multi-spectral imaging system proposed in step S1, high-resolution spectral curves T o The discrete spectral transmittance values ​​are averaged to obtain the average gas absorptivity of the corresponding band. Based on the average absorptivity of each band, a dedicated absorptivity normalization curve T is obtained for comparison with the gas cloud absorptivity normalization curve R obtained in step S3.

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