CN115541036B - Real-time calibration method for infrared movement system - Google Patents
Real-time calibration method for infrared movement system Download PDFInfo
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Abstract
The application belongs to the technical field of infrared cameras. The method comprises the steps of controlling the temperature of a temperature control baffle to be a first temperature and controlling the temperature control baffle to be in a closed state: acquiring a plurality of images by utilizing each infrared movement in the system respectively, and acquiring an airspace pixel average value of the plurality of images; determining an inter-station ratio value of the system according to the airspace pixel average value; and calibrating the integration time of each infrared movement according to the inter-table proportional value. Based on the technical scheme provided by the application, the parameters of the infrared movement system can be calibrated in real time, so that the accuracy of various data measured by the system is improved.
Description
Technical Field
The invention relates to the technical field of infrared cameras, in particular to a real-time calibration method of an infrared movement system.
Background
In recent years, the need for industrial safety monitoring technology has been increasing. The leakage of gas medium in the chemical production field frequently occurs, which causes serious consequences such as explosion, fire and the like.
Currently, for the problem of gas medium leakage in the chemical generation field, a wide spectrum infrared movement is generally used for gas medium leakage monitoring. In general, a jet of black gas may be observed as the gaseous medium leaks, and the risk of the leak event may be determined by monitoring the temperature at each location. However, this method can only detect the presence of a gaseous medium leak, without distinguishing the type of gaseous medium leaking. In order to identify the type of leaking gas medium, the prior art uses more rotating wheel type schemes, namely: a detector is adopted, and the filter plates with different wave bands are switched in different time periods by sacrificing the time resolution to obtain the corresponding spectrum curve, so that the type of the gaseous medium is distinguished; however, the outdoor leaked gaseous medium is extremely susceptible to air flow, and thus, this scheme has a defect in discrimination accuracy. In another prior art, a multi-infrared movement mode is adopted to monitor leakage of a gas medium at the same time, however, differences exist between different infrared movements, so that accurate spectrum curves are difficult to obtain.
Currently, for infrared temperature measurement, each parameter of an infrared camera is generally corrected by pre-storing and calibrating a single infrared camera, however, the method is extremely easy to be interfered by environmental factors, so that a measured temperature has a large error.
Disclosure of Invention
Therefore, the invention provides a real-time calibration method of an infrared movement system, which solves the problem that an accurate spectrum curve is difficult to obtain by correcting the system in real time, and not only can the temperature measurement be more accurate according to the system parameters updated by real-time correction, but also the identification of the type of the leaked gas can be more accurate.
To achieve the above object, a first aspect of the present application provides a method for calibrating an infrared movement system in real time, including: controlling the temperature of the temperature control baffle to be a first temperature and controlling the temperature control baffle to be in a closing state: acquiring a plurality of images by utilizing each infrared movement in the system respectively, and acquiring an airspace pixel average value of the plurality of images; determining an inter-station ratio value of the system according to the airspace pixel average value; the inter-platform proportion value is used for representing the proportion of the average value of the airspace pixels of each infrared machine core relative to the average value of the airspace pixels of the main infrared machine core; the main infrared machine core acquires spectrum signals within a first frequency range; each infrared separating machine core collects spectrum signals in a second frequency range; and calibrating the integration time of each infrared movement according to the inter-table proportional value.
By calculating the inter-table proportion value of each sub-infrared machine core relative to the main infrared machine core in the system, the integration time of the sub-infrared machine core is calibrated based on the inter-table proportion value, and the accuracy of measuring various data by the system is improved due to different sensitivity caused by inter-table difference.
As an optional implementation manner of the first aspect, the determining an inter-station scale value of the system according to the spatial pixel average value includes: taking the airspace pixel average value of the plurality of images obtained by the main infrared movement as a reference airspace pixel average value; and comparing the spatial pixel average value of the plurality of images obtained by each infrared separating movement with the reference spatial pixel average value to obtain the inter-platform proportion value.
By taking the main infrared machine core as a reference, the inter-stage ratio value of each sub-infrared machine core relative to the main infrared machine core is obtained, and the obtained spectrum curve is more accurate through the inter-stage ratio value.
As an optional implementation manner of the first aspect, the calibrating the integration time of the respective infrared cores according to the inter-station scale value includes: taking the theoretical signal quantity of the main infrared movement as a reference theoretical signal quantity; the theoretical signal quantity of each sub-infrared machine core is compared with the reference theoretical signal quantity, so that the theoretical proportionality coefficient of each sub-infrared machine core relative to the main infrared machine core is obtained; determining the difference coefficient of each sub-infrared machine core according to the theoretical proportion coefficient of each sub-infrared machine core relative to the main infrared machine core and the inter-platform proportion value; and calibrating the integration time of each sub-infrared machine core by using the difference coefficient of each sub-infrared machine core.
By the method, the integration time of each infrared machine core is calibrated through the inter-platform proportional value, and real-time correction of the integration time of each infrared machine core is realized.
As an optional implementation manner of the first aspect, the determining of the theoretical semaphore includes: calculating a planck curve of the first temperature; and determining theoretical signal quantities of the main infrared machine core and each sub infrared machine core according to the Planck curve.
As an optional implementation manner of the first aspect, the method further includes: acquiring time sequence pixel values of each image when acquiring the airspace pixel average value; and calibrating the gain of each sub-infrared movement according to the time sequence pixel value of each image.
As an optional implementation manner of the first aspect, the calibrating the gain of each sub-infrared core according to the time sequence pixel value of each image includes: determining an internal feedback value of the main infrared machine core according to a time sequence pixel value of an image acquired by the main infrared machine core; determining the internal feedback value of each sub-infrared machine core according to the time sequence pixel value of the image acquired by each sub-infrared machine core; determining gain correction coefficients of the infrared cores according to the internal feedback values of the main infrared cores and the internal feedback values of the infrared cores; and correcting the gain of each sub-infrared machine core by using the gain correction coefficient of each sub-infrared machine core.
By the scheme, the gain of each sub-infrared movement is corrected through the time sequence pixel values, and the gain can be corrected in real time through the scheme, so that the accuracy of the system is improved.
As an optional implementation manner of the first aspect, the infrared movement system includes: the temperature control baffle, a plurality of cascaded infrared cores and a plurality of beam splitters which are in one-to-one correspondence with the infrared cores; wherein the plurality of infrared machine cores comprise a main infrared machine core and a plurality of sub-infrared machine cores; the main infrared machine core acquires spectrum signals within a first frequency range; each infrared separating machine core collects spectrum signals in a second frequency range; the beam splitter corresponding to the main infrared machine core can transmit part of spectrum signals in a first frequency range, can reflect part of spectrum signals in the first frequency range and can transmit all spectrum signals in other frequency ranges; the beam splitter corresponding to the infrared movement can reflect all spectrum signals in the second frequency range and transmit all spectrum signals in other frequency ranges.
By the above, due to the adoption of the beam splitter scheme, the infrared machine cores can keep the visual fields consistent without alignment, and the time resolution and the spatial resolution of the infrared machine cores are ensured to the greatest extent.
A second aspect of the present application provides a method for measuring temperature using the system calibrated by the calibration method according to any one of the first aspect, comprising: determining the temperature estimated value of each infrared machine core according to the airspace pixel average value of a plurality of images obtained by each infrared machine core; determining the temperature weight of each infrared machine core according to the gray value of the image obtained by each infrared machine core; and determining the measured temperature of the system according to the temperature estimated value of each infrared core and the temperature weight of each infrared core.
By the temperature measuring method, the accuracy of temperature measurement can be improved.
A third aspect of the present application provides a method for identifying a kind of leakage gas by using the system calibrated by the calibration method according to any one of the first aspect, including: determining signal quantity obtained by each infrared machine core for the gas according to the transmittance of an infrared lens of each infrared machine core in the system, the response value of an infrared detector in the system and the transmittance of the gas; determining the response quantity of each infrared machine core to the gas according to the signal quantity obtained by each infrared machine core to the gas; determining a gas database matrix according to the response quantity of each infrared machine core to the gas; determining gas spectrum data according to the average signal value of each infrared movement gas area; determining a gas similarity matrix according to the gas database matrix and the gas spectrum data; and taking the gas corresponding to the minimum value selected from the gas similarity matrix as leakage gas.
By the temperature measuring method, the accuracy of gas identification can be improved.
A fourth aspect of the present application provides a computing device comprising: a processor, and a memory; the memory having stored thereon program instructions that, when executed by the processor, cause the processor to perform the method of real-time calibration of an infrared movement system of any of the first aspects above.
These and other aspects of the application will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.
Drawings
The individual technical features of the present application and their relationships are further described below with reference to the accompanying drawings. The drawings are exemplary, some technical features are not shown in actual proportion, and some drawings may omit technical features that are conventional in the art to which the present application pertains and are not essential to understanding and realizing the present application, or additionally show technical features that are not essential to understanding and realizing the present application, that is, combinations of the technical features shown in the drawings are not limiting the present application. In addition, throughout this application, like reference numerals refer to like elements. The specific drawings are as follows:
Fig. 1 is a schematic structural diagram of a cascaded infrared movement system provided in the related art;
FIG. 2 is a flow chart of calibration of integration time for each infrared movement provided in an embodiment of the present application;
FIG. 3 is a flowchart of calibration of integration time of each infrared movement according to an embodiment of the present application;
FIG. 4 is a flow chart of a method for measuring temperature according to an embodiment of the present application;
FIG. 5 is a flow chart of a method for identifying a type of leaking gas according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a real-time calibration device of an infrared movement system according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a computing device according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of another computing device according to an embodiment of the present application.
Detailed Description
The terms first, second, third, etc. or module a, module B, module C, etc. in the description and in the claims, etc. are used solely for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order, as may be appreciated, if permitted, to interchange particular orders or precedence orders to enable embodiments of the present application described herein to be implemented in orders other than those illustrated or described herein.
In the following description, reference numerals indicating steps such as S110, S120, … …, etc. do not necessarily indicate that the steps are performed in this order, and the order of the steps may be interchanged or performed simultaneously as allowed.
The term "comprising" as used in the description and claims should not be interpreted as being limited to what is listed thereafter; it does not exclude other elements or steps. Thus, it should be interpreted as specifying the presence of the stated features, integers, steps or components as referred to, but does not preclude the presence or addition of one or more other features, integers, steps or components, or groups thereof. Thus, the expression "a device comprising means a and B" should not be limited to a device consisting of only components a and B.
Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Thus, appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments as would be apparent to one of ordinary skill in the art from this disclosure.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. If there is a discrepancy, the meaning described in the present specification or the meaning obtained from the content described in the present specification is used. In addition, the terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the present application.
Before describing the embodiments of the present application in further detail, the terms and belongs to the embodiments of the present application, and the corresponding uses/actions/functions and the like in the present application are described, where the terms and terms are applicable to the following explanation:
1) An infrared movement: is a core component of an infrared camera and is used for detecting radiation signals of infrared wave bands.
2) Temperature control baffle plate: and the device is used for realizing light path shielding, and the temperature of the device is controllable.
3) Beam splitter: beam splitters are a common optical element that partially transmit and partially reflect an incident beam.
The following describes in detail a method for calibrating an infrared movement system in real time according to an embodiment of the present application with reference to the drawings.
Fig. 1 is a schematic structural diagram of an infrared movement system according to an embodiment of the present application.
Referring to fig. 1, the system includes a temperature control barrier 10, a plurality of cascaded infrared cores (infrared cores 0-N), and a plurality of beam splitters (beam splitters 0-N). Wherein, beam splitter 0-beam splitter N corresponds to infrared core 0-infrared core N one by one. The infrared movement should include at least an infrared lens and an infrared detector. The infrared lens is used for focusing an infrared radiation image, and the infrared detector is used for realizing thermoelectric conversion.
The infrared core 0 is a main core, and the main core collects spectrum signals in a first frequency range, for example: the first frequency range may be a band of 6 μm to 14 μm.
The infrared core 1-the infrared core N are sub cores, each sub core collects spectrum signals in a corresponding second frequency range, and it is understood that the frequency range values of the collected second frequency ranges can be different for different sub cores.
As an implementation, the sum of the frequency ranges collected by the infrared core 1-the infrared core N covers (i.e. is not smaller than) the range of the first frequency range collected by the main core, for example: the infrared machine core 1 collects the wave band of 6-7 mu m, the infrared machine core 2 collects the wave band of 7-8 mu m, and the infrared machine core N collects the wave band of 13-14 mu m. Namely, the wave band range which can be acquired by the infrared machine core 1-the infrared machine core N is 6-14 mu m. In this case the sum of the frequency ranges acquired by the infrared cores 1-N is equal to the first frequency range.
As yet another implementation, for example: the infrared machine core 1 collects the wave band of 5-6 mu m, the infrared machine core 2 collects the wave band of 6-7 mu m, the infrared machine core N collects the wave band of 14-15 mu m. Namely, the wave band range which can be acquired by the infrared machine core 1-the infrared machine core N is 5 mu m-15 mu m. In this case, the sum of the frequency ranges acquired by the infrared cores 1 and N is larger than the first frequency range.
As yet another implementation, the sum of the frequency band ranges collected by the infrared cores 1-N may be smaller than the range of the first frequency band collected by the main core, for example: the system comprises 2 sub cores: the infrared machine core 1 collects the wave bands of 7-9 mu m, and the infrared machine core 2 collects the wave bands of 9-11 mu m. Namely, the range of the wave bands which can be acquired by the infrared machine core 1-the infrared machine core N is 7 mu m-11 mu m. In this case, the sum of the frequency ranges acquired by the infrared cores 1 to N is smaller than the first frequency range.
It should be understood that the frequency ranges collected by the infrared cores 1-N are all exemplary, and in other embodiments, the frequency ranges collected by the infrared cores may be other ranges.
The beam splitter 0 is a beam splitter corresponding to the infrared machine core 0 (main machine core). The beam splitter 0 adopts a semi-transparent and semi-reflective design in a first frequency range of 6-14 mu m, namely 50% of signals in the frequency range pass through the beam splitter 0, 50% of signals are reflected back to the infrared machine core 0, and the beam splitter 0 is of a full-transparent design in other frequency ranges outside the first frequency range. It should be understood that the ratio of the signal transmitted through the beam splitter 0 and reflected back to the infrared core 0 in the first frequency range can be adjusted according to the actual needs, and the transmittance of 50% in this embodiment is merely illustrative.
The beam splitter 1-beam splitter N is the beam splitter corresponding to the infrared machine core 1-infrared machine core N. All beam splitters adopt full-reflection design in the second frequency band of the corresponding infrared machine core, namely: and the signals are totally reflected back to the corresponding infrared movement in the specific second frequency range. In addition, the other frequency band ranges of the beam splitter 1-beam splitter N outside the second frequency band range are full-transparent designs. For example, if the second frequency band corresponding to the infrared machine core 1 is a 6-7 μm wave band, signals of the beam splitter 1 in the 6-7 μm wave band are totally reflected back to the infrared machine core 1, and signals of the 6-7 μm wave band in the wave band can totally pass through the beam splitter 1; for example, if the second frequency band corresponding to the infrared machine core 2 is 7 μm-8 μm, the signals of the beam splitter 2 in the 7 μm-8 μm wave band are totally reflected back to the infrared machine core 2, and the signals of the 7 μm-8 μm wave band in the wave band can be totally transmitted from the beam splitter 2.
The temperature control baffle 10 is a temperature-controllable device for realizing light path shielding, so that the temperature of the temperature control baffle can be adjusted according to the use scene of the infrared movement system, images are acquired by controlling the closing of the temperature control baffle 10 at different temperatures, then the temperature control baffle 10 is controlled to be opened, the next temperature is switched to be controlled, the closing of the temperature control baffle 10 is controlled to acquire the images, and the updating of the parameters of the infrared movement system is carried out repeatedly in sequence.
In this embodiment, since the infrared movement system adopts the beam splitter, each infrared movement can keep the field of view consistent without performing alignment operation, so as to ensure the time resolution and the spatial resolution of the infrared movement to the greatest extent.
The method for calibrating the infrared movement system in real time is described in detail below.
A flow chart of the calibration method is shown in fig. 2. The embodiment is mainly described by taking the integration time of each sub-infrared movement and the gain of each sub-infrared movement as examples. The calibration of the integration time of each sub-infrared movement is first described below, and the calibration process mainly includes steps S110-S130, and each step is described in detail in turn below.
S110: and respectively acquiring a plurality of images by utilizing each infrared movement in the system, and acquiring the airspace pixel average value of the plurality of images.
When an infrared camera is used for acquiring an image, the temperature control baffle needs to be controlled to be closed, after the image is acquired by shooting, the temperature control baffle is opened to switch the temperature of the temperature control baffle, and the next round of image acquisition is performed again. Wherein, the closing of the temperature control baffle sheet indicates that the temperature control baffle sheet completely shields the imaging light path, and the opening of the temperature control baffle sheet indicates that the temperature control baffle sheet does not shield the imaging light path.
For example, when the temperature of the temperature control baffle is m-level, the temperature control baffle is closed, and K images are obtained by using the infrared machine core n, namely an Image is obtained m_n_1 、Image m_n_2 、Image m_n_3 ……Image m_n_K Wherein the subscripts m_n_K Respectively representing the mth-stage temperature, the nth infrared movement and the kth Image, then Image m_n_K Indicating that the temperature control baffle is the m-th grade temperatureAnd under the condition, the time sequence pixel value of the K-th image obtained by the nth infrared movement. Image m_n_1 、Image m_n_2 、Image m_n_3 ……Image m_n_K Averaging over space domain can obtain the space domain pixel average value Val m_n Wherein the subscripts m_n Respectively representing the mth-stage temperature and the nth infrared movement, val is calculated m_n The mean value of the spatial pixels of the nth infrared movement at the mth level temperature is shown.
S120: determining an inter-station ratio value of the system according to the airspace pixel average value; the inter-platform proportion value is used for representing the proportion of the average value of the airspace pixels of each infrared machine core relative to the average value of the airspace pixels of the main infrared machine core; the main infrared machine core acquires spectrum signals within a first frequency range; the infrared cores collect spectrum signals in a second frequency range, and the sum of the second frequency range of each infrared core is not smaller than the first frequency range.
Specific: taking the airspace pixel average value of a plurality of images obtained by the main infrared machine core as a reference airspace pixel average value, namely an airspace pixel average value Val corresponding to the main infrared machine core 0 in the embodiment m_0 As a reference spatial pixel average.
And comparing the spatial pixel average value of the plurality of images obtained by each infrared machine core with the reference spatial pixel average value to obtain the inter-platform ratio value of each infrared machine core and the main infrared machine core. Specifically, the ratio value between each sub-infrared core and the main infrared core can be determined according to the following formula:
wherein,table ratio value representing the sub-infrared movement 1 and the main infrared movement (infrared movement 0 in this embodiment,)>The ratio value between the tables of the sub infrared machine core 2 and the main infrared machine core is shown,representing the ratio value between the sub-infrared machine core n and the main infrared machine core, +.>Indicating the spatial pixel average value of the sub-infrared movement 1 (the infrared movement 1 in this embodiment) at the mth level temperature, +.>Indicating the spatial pixel mean value of the infrared movement 2 (infrared movement 2 in this embodiment) at the mth level temperature, +.>Representing the spatial pixel mean value of infrared movement n (infrared movement n in this embodiment) at the mth level temperature, +.>The spatial pixel average of the main infrared core (infrared core 0 in this embodiment) at the mth level temperature is shown.
S130: and calibrating the integration time of each infrared movement according to the inter-table proportional value.
Specific: first, the theoretical signal quantity of the main infrared movement is used as a reference theoretical signal quantity. In the present embodiment, the theoretical signal amount of the infrared core 0 is regarded as the reference theoretical signal amount.
And then, respectively comparing the theoretical signal quantity of each infrared movement with the reference theoretical signal quantity, thereby obtaining the theoretical proportionality coefficient. Specifically, the theoretical scaling factor may be calculated as follows:
wherein,is the theoretical proportionality coefficient of the infrared movement 1, < ->Is the theoretical proportionality coefficient of the infrared movement 2, < ->Is the theoretical proportionality coefficient of the infrared movement N, < ->For dividing the theoretical signal quantity of the infrared movement 1, < >>For dividing the theoretical signal quantity of the infrared movement 2, < >>Is the theoretical signal quantity of the infrared movement N, < ->Is the theoretical signal quantity of the infrared movement 0 (main infrared movement). Note that the calculation process of the theoretical scaling factor of each infrared core will be described in detail below.
Next, the difference coefficient of each sub-infrared core is determined according to the theoretical proportion coefficient of each sub-infrared core relative to the main infrared core and the inter-station proportion value. Specifically, the difference coefficient of each infrared movement can be determined according to the following formula:
wherein,for dividing the difference coefficient of the infrared movement 1, < - >For dividing the difference coefficient of the infrared movement 2, < ->Is the difference coefficient of the infrared movement n, < ->The ratio value between the tables of the sub infrared machine core 1 and the main infrared machine core is shown,indicating the ratio value between the infrared machine core 2 and the main infrared machine core, +.>Representing the ratio value between the sub-infrared machine core n and the main infrared machine core, +.>Is the theoretical proportionality coefficient of the infrared movement 1, < ->Is the theoretical proportionality coefficient of the infrared movement 2, < ->Is the theoretical proportionality coefficient of the infrared movement N.
And finally, calibrating the integration time of each sub-infrared machine core by using the difference coefficient of each sub-infrared machine core. Specifically, the integration time of each infrared movement can be calibrated as follows:
wherein,for dividing infrared movement 1Integration time after quasi +.>Integration time after calibration for the infrared movement 2, +.>Integration time after calibration of the infrared movement n, < >>To divide the integration time before the infrared movement 1 is calibrated,for dividing the integration time before the calibration of the infrared movement 2, < >>For dividing the integration time before the calibration of the infrared movement n,/->For a preset time micro-variable, +.>For dividing the difference coefficient of the infrared movement 1, < ->For dividing the difference coefficient of the infrared movement 2, < ->Is the difference coefficient of the infrared movement n.
The integration time calibration of the cascaded infrared movement system can be realized through the description of the steps.
The calculation process of the theoretical signal quantity of each infrared movement is described below.
The temperature of the preset temperature control baffle is、/>、……、/>、……、/>。
Calculating the temperature of the temperature control baffle plate asPlanck curve of (c). Specifically, the temperature can be calculated as +.>Is>：
Wherein,for wavelength, < >>The value of light speed is +.>；/>Is Planck constant, the value isThe method comprises the steps of carrying out a first treatment on the surface of the K is Boltzmann constant, the value is +.>。
According to the temperature ofIs>And determining the theoretical signal quantity of each infrared movement. Specifically, the theoretical signal quantity of the main infrared movement (infrared movement 0 in this embodiment) can be determined as follows>：
Wherein,the theoretical signal quantity of the infrared core 0 is p which is the minimum band value of the infrared core filter in the system and p=6μm in the embodiment, q which is the maximum band value of the infrared core filter in the system and q=14μm in the embodiment; />Transmittance of infrared lens for infrared movement 0,>is the response value of the infrared detector of infrared movement 0,>is a temperature +.>Planck curve of (c).
The theoretical signal quantity of the infrared machine core n can be determined according to the following formula：
Wherein,is the minimum band value which can be transmitted by the n filter of the infrared machine core>Is the maximum band value which can be transmitted by the n filter of the infrared machine core >Transmittance of infrared lens for infrared movement n,>is the response value of the infrared detector of infrared movement n,>is a temperature +.>Planck curve of (c).
Next, calibration of the integration time of each infrared movement is described, and as shown in fig. 3, the calibration process mainly includes steps S210 to S220, and each step is described in detail in turn.
S210: and when the airspace pixel average value is acquired, acquiring time sequence pixel values of each image.
The process of acquiring the time-series pixel values of each Image in this step can refer to the Image in step S110 m_n_K Image is obtained through the acquisition process of (a) m_n_K Indicating that the temperature control baffle is the mthAt the stage temperature, the time sequence pixel value of the K-th image obtained by the nth infrared movement.
S220: and calibrating the gain of each sub-infrared movement according to the time sequence pixel value of each image.
In this step, firstly, determining an internal feedback value of the main infrared core according to a time sequence pixel value of an image acquired by the main infrared core, and specifically, the following formula can be referred to:
wherein,k is the K-th Image acquired by the infrared machine core 0, K is the total number of images acquired by the infrared machine core 0, R is a row iteration variable, R is a row total number, C is a column iteration variable, C is a column total number, and Image m_0_k The time sequence pixel value of the kth image obtained by the infrared machine core 0 at the mth stage temperature is obtained.
Then, determining the internal feedback value of each infrared movement according to the time sequence pixel value of the image acquired by each infrared movement, wherein the following formula can be seen specifically:
wherein,for the internal feedback value of the infrared machine core n, K is the kth Image acquired by the infrared machine core n, K is the total number of images acquired by the infrared machine core 0, R is the row iteration variable, R is the row total number, C is the column iteration variable, C is the column total number, and Image m_n_k The time sequence pixel value of the kth image is obtained for the infrared movement n at the mth stage temperature.
Next, the internal feedback values of the infrared cores are respectively fed backAnd the internal feedback value of the main infrared movement +.>The gain correction coefficients of the respective sub-infrared cores are obtained by comparison, for example, the gain correction coefficient of the sub-infrared core n may be determined as follows:
wherein,gain correction factor for IR core n, < ->Is the internal feedback value of the infrared movement n, < >>Is the internal feedback value of the main infrared movement.
And finally, correcting the gain of each sub-infrared machine core by using the gain correction coefficient of each sub-infrared machine core. For example, the gain of the sub-infrared core n may be corrected using the gain correction coefficient of the sub-infrared core n as follows:
Wherein,gain after calibration for infrared movement n, < >>For the gain of the infrared movement n before calibration,the gain correction coefficient of the infrared movement n is divided.
Another embodiment of the present application provides a method for measuring a temperature by using the cascaded infrared movement system calibrated by the above calibration method.
The method for measuring temperature mainly includes steps S310-S330, as shown in fig. 4, and each step is described in detail in sequence.
S310: and determining the temperature estimated value of each infrared movement according to the airspace pixel average value of a plurality of images obtained by each infrared movement.
For example, the temperature estimate for each infrared core may be determined as follows:
wherein,gray value of the position of the object to be observed in the image obtained for the infrared movement n,/-, is obtained>For the mean value of the spatial pixels of the nth infrared movement at the 1 st temperature, +.>For the mean value of the spatial pixels of the nth infrared movement at the temperature of level 2>For the mean value of the spatial pixels of the nth infrared movement at the temperature of level 3, +.>For the spatial domain pixel average value of the nth infrared movement at the temperature of the M-2 th level,for the mean value of the spatial pixels of the nth infrared movement at the temperature of the M-1 st stage,/ >The temperature of the temperature control baffle is +.>，/>The temperature of the temperature control baffle is +.>，/>The temperature of the temperature control baffle is +.>，/>The temperature of the temperature control baffle is +.>，/>The temperature of the temperature control baffle is +.>，/>The temperature of the temperature control baffle is +.>，/>For the mean value of the spatial pixels of the nth infrared movement at the 4 th temperature, +.>The average value of the spatial pixels of the nth infrared movement at the temperature of the Mth stage is obtained.
S320: and determining the temperature weight of each infrared machine core according to the gray value of the image obtained by each infrared machine core.
For example, the temperature weights of the individual infrared cores may be determined as follows:
wherein,temperature weight of infrared movement n, < ->Gray value of the position of the object to be observed in the image obtained for the infrared movement n,/-, is obtained>Gray value of the position of the object to be observed in the image obtained for the infrared movement 1,/->Gray value of the position of the object to be observed in the image obtained for the infrared movement 2,/->The gray value of the position of the target to be observed in the image obtained by the infrared machine cores N is obtained, wherein N infrared machine cores are arranged in the system.
S330: and determining the measured temperature of the system according to the temperature estimated value of each infrared core and the temperature weight of each infrared core.
The measured temperature of the system can be determined as follows:
wherein,for measuring the temperature of the system, +.>Is the temperature estimation value of infrared movement 0 (main infrared movement)>For dividing the temperature weight of the infrared machine core, < ->Is the temperature estimated value of the infrared movement.
Another embodiment of the present application provides a method for identifying a kind of a leaking gas by using the cascaded infrared movement system calibrated by the above calibration method. When the system is used for identifying the gas types, the relative response value of the infrared machine core to the gas types possibly existing in each detection scene is required to be pre-stored. It is assumed that there are Q gases in the pre-stored gas species library.
Fig. 5 is a flowchart of the method for identifying the type of the leakage gas, which mainly includes steps S410-S460, and each step is described in detail in sequence.
S410: and determining the signal quantity obtained by each infrared machine core for the gas according to the transmittance of the infrared lens of each infrared machine core in the system, the response value of the infrared detector in the system and the transmittance of the gas.
In this step, the signal quantity available to infrared movement n for gas 1 can be determined as follows:
/>
wherein,for the signal quantity available to infrared movement n for gas 1, [ -A ] >，/>]Is the transparent wave band range of the infrared machine core n filter plate,>is the lower limit of the range of the permeable wave band, +.>Is the upper limit of the range of the permeable wave band, +.>Transmittance of infrared lens for infrared movement n,>is the response value of the infrared detector of infrared movement n,>is the transmittance of the gas 1.
S420: and determining the response quantity of each infrared machine core to the gas according to the signal quantity obtained by each infrared machine core to the gas.
In this step, the response of infrared movement n to said gas 1 can be determined as follows:
wherein,for the response of the infrared movement n to said gas 1,for the signal quantity available to infrared movement n for gas 1, +.>For the signal quantity available to the infrared movement 1 for the gas 1,/for the infrared movement 1>For the signal quantity available to the infrared movement 2 for the gas 1,/for the infrared movement>Is the signal quantity available to the infrared movement N for the gas 1.
S430: and determining a gas database matrix according to the response quantity of each infrared machine core to the gas.
In this step, the gas database matrix may be determined as follows:
wherein,for a gas database matrix, +.>For the response of the infrared movement 1 to said gas 1, +.>For the response of the infrared movement 2 to said gas 1, For the response of the infrared movement N to said gas 1, +.>For the response of the infrared movement 1 to the gas 2, +.>For the response of the infrared movement 2 to said gas 2,for the response of the infrared movement N to said gas 2, +.>For the response of the infrared movement 1 to the gas Q +.>For the response of the infrared movement 2 to said gas Q,is the response of the infrared movement N to the gas Q.
S440: and determining gas spectrum data according to the average signal value of the gas area of each infrared movement.
In this step, when gas leakage is detected, the average signal of the gas area in each infrared movement is read, wherein the method for detecting the gas leakage includes, but is not limited to, motion detection, background modeling, deep learning and the like.
In this step, the gas spectral data can be determined as follows:
wherein,for gas spectral data, +.>Is the average signal of the infrared movement 1 gas zone, < >>Is the average signal of the infrared movement 2 gas zone,/->Is the average signal of the infrared movement N gas area.
S450: and determining a gas similarity matrix according to the gas database matrix and the gas spectrum data.
The gas similarity matrix may be determined as follows:
Wherein,is a gas similarity matrix>As a result of the spectral data of the gas,for a gas database matrix, +.>To detect the probability that the gas is gas 1,for the probability of detecting gas as gas 2, +.>To detect the probability that the gas is gas Q.
S460: and taking the gas corresponding to the minimum value selected from the gas similarity matrix as leakage gas.
In this step, i.e. from a gas similarity matrixAnd (3) selecting the maximum value, wherein the medium gas is the detected leaked gas type.
By the embodiment of the application, the real-time calibration of related parameters in the cascaded infrared core system can be realized, so that an accurate measurement result can be obtained when the system is used for measurement. In addition, the embodiment of the application also provides an example of measuring temperature and identifying the type of the leaked gas by using the system, so that the measured temperature and the identification accuracy of the type of the leaked gas are improved.
Another embodiment of the present application provides a real-time calibration device for an infrared movement system, where the device may be implemented by a software system, or may be implemented by a hardware device, or may be implemented by a combination of the software system and the hardware device.
It should be understood that fig. 6 is merely a schematic structural diagram illustrating a real-time calibration device 60 of an infrared core system, and the present application is not limited to the division of functional modules in the real-time calibration device 60 of an infrared core system. As shown in fig. 6, the real-time calibration device 60 of the infrared movement system may be logically divided into a plurality of modules, each of which may have different functions, and the functions of each module are implemented by a processor in an electronic device that reads and executes instructions in a memory. Illustratively, the real-time calibration device 60 of the infrared movement system includes a first acquisition module 610, a first determination module 620, and a first calibration module 630.
In an alternative implementation, the real-time calibration device 60 of the infrared movement system is used to perform what is described in steps S110-S130 shown in fig. 2. Specifically, it may be: the first acquiring module 610 is configured to acquire a plurality of images by using each infrared movement in the system, and acquire an average value of spatial pixels of the plurality of images. The first determining module 620 is configured to determine an inter-station scale value of the system according to the spatial pixel average value; the inter-platform proportion value is used for representing the proportion of the average value of the airspace pixels of each infrared machine core relative to the average value of the airspace pixels of the main infrared machine core; the main infrared machine core acquires spectrum signals within a first frequency range; and each infrared separating machine core collects spectrum signals in a second frequency range. The first calibration module 630 is configured to calibrate the integration time of each of the infrared cores according to the inter-station scale value.
In an alternative implementation, the real-time calibration device 60 of the infrared movement system further includes a second acquisition module 640 and a second calibration module 650. The second obtaining module 640 is configured to obtain a time-sequence pixel value of each image when obtaining the spatial pixel average value. The second calibration module 650 is configured to calibrate the gain of each of the infrared cores according to the time sequence pixel values of each of the images.
Embodiments of the present application also provide a computing device including a processor, and a memory. The memory has stored thereon program instructions that, when executed by the processor, cause the processor to perform the method of the corresponding embodiment of fig. 2, or alternative embodiments thereof.
Fig. 7 is a schematic diagram of a computing device 900 provided by an embodiment of the present application. The computing device 900 includes: processor 910, memory 920.
It should be appreciated that the computing device 900 shown in fig. 7 may also include a communication interface 930 therein that may be used to communicate with other devices.
Wherein the processor 910 may be coupled to a memory 920. The memory 920 may be used to store the program codes and data. Accordingly, the memory 920 may be a storage unit internal to the processor 910, an external storage unit independent of the processor 910, or a component including a storage unit internal to the processor 910 and an external storage unit independent of the processor 910.
Optionally, computing device 900 may also include a bus. The memory 920 and the communication interface 930 may be connected to the processor 910 through a bus. The bus may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, or the like. The buses may be classified as address buses, data buses, control buses, etc.
It should be appreciated that in embodiments of the present application, the processor 910 may employ a central processing unit (Central Processing Unit, CPU). The processor may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. Or the processor 910 may employ one or more integrated circuits for executing associated programs to perform the techniques provided in the embodiments of the present application.
The memory 920 may include read only memory and random access memory and provide instructions and data to the processor 910. A portion of the processor 910 may also include nonvolatile random access memory. For example, the processor 910 may also store information of the device type.
When the computing device 900 is running, the processor 910 executes computer-executable instructions in the memory 920 to perform the operational steps of the methods described above.
It should be understood that the computing device 900 according to the embodiments of the present application may correspond to a respective subject performing the methods according to the embodiments of the present application, and that the foregoing and other operations and/or functions of the respective modules in the computing device 900 are respectively for implementing the respective flows of the methods of the embodiments, and are not described herein for brevity.
The present application also provides another computing device, as shown in fig. 8, which is a schematic structural diagram of another computing device 1000 provided by the embodiment, including: a processor 1010, and an interface circuit 1020, wherein the processor 1010 accesses a memory through the interface circuit 1020, the memory storing program instructions that, when executed by the processor, cause the processor to perform the method of the corresponding embodiment of fig. 2. In addition, the computing device may further include a communication interface, a bus, etc., and may be specifically referred to the description in the embodiment shown in fig. 7, which is not repeated. The interface circuit 1020 may be, for example, a CAN bus or a LIN bus.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, is configured to perform a method for real-time calibration of an infrared movement system, the method comprising at least one of the aspects described in the embodiments above.
Any combination of one or more computer readable media may be employed as the computer storage media of the embodiments herein. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only the preferred embodiments of the present application and the technical principles applied. Those skilled in the art will appreciate that the present application is not limited to the particular embodiments described herein, but is capable of numerous obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the present application. Thus, while the present application has been described in terms of the foregoing embodiments, the present application is not limited to the foregoing embodiments, but may include many other equivalent embodiments without departing from the spirit of the present application, all of which fall within the scope of the present application.
Claims (8)
1. A method for real-time calibration of an infrared movement system, comprising: controlling the temperature of the temperature control baffle to be a first temperature and controlling the temperature control baffle to be in a closing state:
acquiring a plurality of images by utilizing each infrared movement in the system respectively, and acquiring an airspace pixel average value of the plurality of images;
determining an inter-station ratio value of the system according to the airspace pixel average value; the inter-platform proportion value is used for representing the proportion of the average value of the airspace pixels of each infrared machine core relative to the average value of the airspace pixels of the main infrared machine core; the main infrared machine core acquires spectrum signals within a first frequency range; each infrared separating machine core collects spectrum signals in a second frequency range;
calibrating the integration time of each infrared movement according to the inter-table proportional value;
the step of calibrating the integration time of each sub-infrared movement according to the inter-station proportional value comprises the following steps:
taking the theoretical signal quantity of the main infrared movement as a reference theoretical signal quantity;
the theoretical signal quantity of each sub-infrared machine core is compared with the reference theoretical signal quantity, so that the theoretical proportionality coefficient of each sub-infrared machine core relative to the main infrared machine core is obtained;
Determining the difference coefficient of each sub-infrared machine core according to the theoretical proportion coefficient of each sub-infrared machine core relative to the main infrared machine core and the inter-platform proportion value;
calibrating the integration time of each sub-infrared machine core by using the difference coefficient of each sub-infrared machine core;
the theoretical semaphore determination process includes:
calculating a planck curve of the first temperature;
and determining theoretical signal quantities of the main infrared machine core and each sub infrared machine core according to the Planck curve.
2. The method of claim 1, wherein said determining an inter-station scale value for the system from the spatial pixel average comprises:
taking the airspace pixel average value of the plurality of images obtained by the main infrared movement as a reference airspace pixel average value;
and comparing the spatial pixel average value of the plurality of images obtained by each infrared separating movement with the reference spatial pixel average value to obtain the inter-platform proportion value.
3. The method as recited in claim 1, further comprising:
acquiring time sequence pixel values of each image when acquiring the airspace pixel average value;
And calibrating the gain of each sub-infrared movement according to the time sequence pixel value of each image.
4. A method according to claim 3, wherein said calibrating the gain of each of said sub-infrared cores based on the time-sequential pixel values of each of said images comprises:
determining an internal feedback value of the main infrared machine core according to a time sequence pixel value of an image acquired by the main infrared machine core;
determining the internal feedback value of each sub-infrared machine core according to the time sequence pixel value of the image acquired by each sub-infrared machine core;
determining gain correction coefficients of the infrared cores according to the internal feedback values of the main infrared cores and the internal feedback values of the infrared cores;
and correcting the gain of each sub-infrared machine core by using the gain correction coefficient of each sub-infrared machine core.
5. The method of any one of claims 1-4, wherein the infrared movement system comprises:
the temperature control baffle, a plurality of cascaded infrared cores and a plurality of beam splitters which are in one-to-one correspondence with the infrared cores; wherein the plurality of infrared machine cores comprise a main infrared machine core and a plurality of sub-infrared machine cores; the main infrared machine core acquires spectrum signals within a first frequency range; each infrared separating machine core collects spectrum signals in a second frequency range;
The beam splitter corresponding to the main infrared machine core can transmit part of spectrum signals in a first frequency range, can reflect part of spectrum signals in the first frequency range and can transmit all spectrum signals in other frequency ranges;
the beam splitter corresponding to the infrared movement can reflect all spectrum signals in the second frequency range and transmit all spectrum signals in other frequency ranges.
6. A method of measuring temperature using the calibrated system of any one of claims 1-5, comprising:
determining the temperature estimated value of each infrared machine core according to the airspace pixel average value of a plurality of images obtained by each infrared machine core;
determining the temperature weight of each infrared machine core according to the gray value of the image obtained by each infrared machine core;
and determining the measured temperature of the system according to the temperature estimated value of each infrared core and the temperature weight of each infrared core.
7. A method for identifying a type of leaking gas using the system calibrated by the calibration method of any one of claims 1-5, comprising:
determining signal quantity obtained by each infrared machine core for the gas according to the transmittance of an infrared lens of each infrared machine core in the system, the response value of an infrared detector in the system and the transmittance of the gas;
Determining the response quantity of each infrared machine core to the gas according to the signal quantity obtained by each infrared machine core to the gas;
determining a gas database matrix according to the response quantity of each infrared machine core to the gas;
determining gas spectrum data according to the average signal value of each infrared movement gas area;
determining a gas similarity matrix according to the gas database matrix and the gas spectrum data;
and taking the gas corresponding to the minimum value selected from the gas similarity matrix as leakage gas.
8. A computing device, comprising:
a processor, and a memory;
the memory having stored thereon program instructions that, when executed by the processor, cause the processor to perform the method of real-time calibration of an infrared movement system of any of claims 1-5.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2782178A1 (en) * | 2010-01-18 | 2011-07-21 | Telops Inc. | Radiometric calibration method for infrared detectors |
| EP3386180A1 (en) * | 2017-04-05 | 2018-10-10 | Tecnobit S.L. | Method and system for calibrating an infrared camera |
| CN111595462A (en) * | 2020-07-23 | 2020-08-28 | 南京智谱科技有限公司 | Infrared imaging temperature measurement system calibration method and device, computing equipment and storage medium |
| CN111780876A (en) * | 2020-06-30 | 2020-10-16 | 平安国际智慧城市科技股份有限公司 | Temperature measurement method, temperature measurement device, electronic equipment and computer readable storage medium |
-
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- 2022-10-24 CN CN202211300014.6A patent/CN115541036B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2782178A1 (en) * | 2010-01-18 | 2011-07-21 | Telops Inc. | Radiometric calibration method for infrared detectors |
| EP3386180A1 (en) * | 2017-04-05 | 2018-10-10 | Tecnobit S.L. | Method and system for calibrating an infrared camera |
| CN111780876A (en) * | 2020-06-30 | 2020-10-16 | 平安国际智慧城市科技股份有限公司 | Temperature measurement method, temperature measurement device, electronic equipment and computer readable storage medium |
| CN111595462A (en) * | 2020-07-23 | 2020-08-28 | 南京智谱科技有限公司 | Infrared imaging temperature measurement system calibration method and device, computing equipment and storage medium |
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