A method for identifying hotspots in the focal plane of imaging photodetectors based on variance analysis

Abstract

The application discloses a kind of imaging photoelectric detector focal plane hot spot identification method based on variance analysis, comprising: using infrared thermal imager to the focal plane of imaging photoelectric detector is thermally imaged, obtains the thermal data matrix of focal plane;Thermal data matrix is regarded as overall area and is four-divided, and the thermal data matrix of four quadrants is formed;The F value of F test is calculated, and the F value calculated is compared with the F distribution critical value under given significance level;If judging F≤F distribution critical value, then for the thermal data matrix in each quadrant of four quadrants, repeat the execution of quadtree partitioning and F test, until judging F>F distribution critical value;LSD value is calculated, and the quadrant that exists significant difference with other quadrants is determined as overall area, and quadtree partitioning and F test are repeatedly executed until F≤F distribution critical value, obtain all minimum hotspot area under given significance level.The application can accurately identify the hotspot on the focal plane of imaging photoelectric detector.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of thermal testing of imaging photoelectric detectors, and particularly relates to an imaging photoelectric detector focal plane hot spot identification method based on variance analysis, a computer device, a computer readable storage medium, and a computer program product. BACKGROUND

[0002] An imaging photoelectric detector is a semiconductor device capable of converting optical signals into electrical signals and generating spatial resolution images. Based on photoelectric effect (such as internal photoelectric effect or photovoltaic effect), the imaging photoelectric detector realizes two-dimensional or three-dimensional visualization of optical information by detecting the light radiation distribution reflected or emitted by a target object. The imaging photoelectric detector is usually composed of a photosensitive cell array (such as a focal plane array), a signal processing circuit, and an imaging algorithm. In the focal plane of the imaging photoelectric detector, areas with significantly increased local temperature due to excessively high power density or uneven heat dissipation are referred to as “hot spots”. Hot spots may cause degradation of device performance, decrease of reliability, and even damage to the device, so detection and suppression of such hot spots are crucial for improving the NETD (Noise Equivalent Temperature Difference) and thermal imaging uniformity of the focal plane array.

[0003] A common method for detecting hot spots is infrared thermal imaging technology, which performs thermal imaging on the focal plane of the imaging photoelectric detector by an infrared thermal imager, and determines whether a region has a hot spot by the difference in temperature values. However, this method has the following obvious shortcomings:

[0004] 1) The criterion for identifying hot spots is specified by humans, which is too subjective;

[0005] 2) When the temperature of a hot spot is only slightly higher than the surrounding temperature, misjudgment may occur, or the hot spot cannot be accurately identified;

[0006] 3) The boundary of a hot spot or the size of the area occupied by a hot spot cannot be accurately identified. SUMMARY

[0007] The present application aims to solve the problems of existing infrared thermal imaging technology for detecting hot spots in the focal plane of an imaging photoelectric detector, and provides an imaging photoelectric detector focal plane hot spot identification method based on variance analysis, a computer device, a computer readable storage medium, and a computer program product, which can accurately identify hot spots in the focal plane of an imaging photoelectric detector.

[0008] To achieve the above-mentioned purpose, one aspect of the present application provides an imaging photoelectric detector focal plane hot spot identification method based on variance analysis, comprising:

[0009] Step S1, using an infrared thermal imager to perform thermal imaging on the focal plane of the imaging photodetector to obtain a thermal data matrix of the focal plane;

[0010] Step S2, dividing the thermal data matrix into four equal parts as the overall area to form thermal data matrices of four quadrants;

[0011] Step S3, calculate the F value of the F test, and compare the calculated F value with the given significance level α , degrees of freedom between groups df between and the within-group degrees of freedom df within The critical value F of the F distribution under α，dfbetween,dfwithin Make comparisons;

[0012] Step S4: If it is determined that F≤F α，dfbetween,dfwithin , then for the thermal data matrix of each quadrant in the four quadrants, repeat steps S2 and S3 until it is determined that F>F α，dfbetween,dfwithin ;

[0013] Step S5, calculating the LSD value, and determining the quadrant with significant difference from other quadrants by comparing the absolute value of the difference between the element means of any two quadrants with the LSD value;

[0014] Step S6: take the thermal data matrix of the quadrant with significant differences as the overall area, and repeat steps S2 and S3. If it is judged that F≤F α.dfbetween，dfwithin , then the overall area at this time is taken as the minimum hotspot area;

[0015] Step S7: If it is determined that F>F α，dfbetween，dfwithin , then repeat steps S5 and S6 until it is determined that F≤F α，dfbetween，dfwithin , and obtain all the minimum hotspot areas under the significance level α.

[0016] Preferably, in step S3, the F value is calculated as follows:

[0017]

[0018] Among them, MSB represents the mean square between groups, MSW represents the mean square within the group. SSB represents the sum of squares between groups, SSW represents the sum of squares within groups, k represents the number of quadrants, k=4, and N represents the number of all elements in the four quadrants.

[0019] Preferably, the between-group sum of squares SSB and the within-group sum of squares SSW are calculated as follows:

[0020]

[0021] in, represents the mean value of all elements in the i-th quadrant; represents the mean value of all elements in the four quadrants, n i represents the number of elements in the i-th quadrant, X ij represents the data value of the j-th element in the i-th quadrant.

[0022] Preferably, the inter-group degrees of freedom df between and the intra-group degrees of freedom ff within are calculated as follows:

[0023] df between = k - 1,

[0024] df within = N - k.

[0025] Preferably, in step S5, the LSD value is calculated as follows:

[0026]

[0027] wherein, represents the t-distribution critical value at a given significance level a and the intra-group degrees of freedom df within .

[0028] Preferably, if it is indicated that there is a significant difference between the data values in the i-th quadrant and the j-th quadrant, by comparing the mean values of the elements in the four quadrants in pairs, the quadrants that have a significant difference with other quadrants are selected.

[0029] Preferably, the given significance level a is 0.05 or 0.01.

[0030] Another aspect of the present application provides a computer device comprising a memory, a processor and a computer program stored on the memory, the processor executing the computer program to implement the steps of the above method.

[0031] Still another aspect of the present application provides a computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of the above method.

[0032] Still another aspect of the present application provides a computer program product comprising a computer program, the computer program being executed by a processor to implement the steps of the above method.

[0033] The imaging photoelectric detector focal plane hot spot recognition method based on variance analysis, the computer device, the computer readable storage medium and the computer program product according to the above aspects of the present application can accurately recognize the hot spots on the focal plane of the imaging photoelectric detector. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the present invention, the following briefly introduces the drawings used in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive efforts.

[0035] Figure 1 1 is a schematic diagram of a method for identifying hot spots in a focal plane of an imaging photodetector based on variance analysis according to an embodiment of the present invention;

[0036] Figure 2 is a schematic diagram of a thermal data matrix according to an embodiment of the present invention;

[0037] Figure 3 It is a schematic diagram of dividing the thermal data matrix into four equal parts according to an embodiment of the present invention.

[0038] Figure 4 It is a structural diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0040] One embodiment of the present invention provides a method for identifying hotspots in the focal plane of an imaging photodetector based on analysis of variance. Analysis of variance (ANOVA) is a statistical method used to test whether the means of two or more samples are significantly different. It decomposes the total variation in the data into between-group variation (caused by treatment factors) and within-group variation (caused by random error) and determines the statistical significance of the difference based on the F test (the ratio of the between-group mean square to the within-group mean square).

[0041] The method for identifying hot spots in the focal plane of an imaging photodetector according to an embodiment of the present invention comprises steps S1 to S7. Figure 1 The schematic diagram of the embodiment of the present invention is used to describe in detail each step of the method.

[0042] In step S1, a thermal image is taken of the focal plane of the imaging photodetector using an infrared thermal imager to obtain a thermal data matrix of the focal plane.

[0043] First, the imaging photodetector is in the normal working state, keep the focal plane of the detector level upward, using high-precision infrared thermal imager, according to the general test specification, the focal plane is thermal imaging test, obtain the thermal data matrix of the focal plane. In an embodiment, assuming the size of the thermal data matrix is mxn, the data value (measurement value) at each element is T (p, q), in order to facilitate the subsequent description, assume that m and n can be expressed by 2 Q , Q belongs to natural number, as shown in Figure 2 .

[0044] In step S2, the thermal data matrix is divided into four equal parts (divided into 4 equal parts) as a whole area, forming four quadrant thermal data matrix with the size of m / 2xn / 2, as shown in Figure 3 .

[0045] The elements of each quadrant are renumbered, N represents the number of all elements of the four quadrants (for example, Figure 3 , N = mxn); subscript i represents that the element belongs to the ith quadrant; subscript j represents the jth element; X ij represents the data value of the jth element of the ith quadrant; k represents the number of quadrants, that is, k = 4; n i represents the number of elements in the ith quadrant (for example, Figure 3 , n1 = n2 = n3 = n4 = m / 2xn / 2); represents the average value of all elements in the ith quadrant; represents the average value of all elements in the four quadrants.

[0046] In step S3, the F value is calculated according to the following process:

[0047] 1) Total sum of squares (SST)

[0048]

[0049] 2) Between group sum of squares (SSB)

[0050]

[0051] 3) Within group sum of squares (SSW)

[0052]

[0053] 4) Total degrees of freedom

[0054] df total = N-1

[0055] 5) Between group degrees of freedom

[0056] df between = k-1

[0057] 6) Within-group degrees of freedom

[0058] df within =Nk

[0059] 7) Mean square between groups

[0060]

[0061] 8) Mean square within group

[0062]

[0063] 9) F-value calculation

[0064]

[0065] Compare the calculated F value with the given significance level α and the degree of freedom between groups df between and the within-group degrees of freedom df within The critical value F of the F distribution under α，dfbetween,dfwithin Make a comparison.

[0066] The critical value of the F distribution indicates the probability of a significant difference between groups at a given significance level (α) and degrees of freedom between groups (df between ) and within-group degrees of freedom (df within ) is the threshold for rejecting the null hypothesis H0. The significance level α is 0.01 to 0.1, usually 0.05 or 0.01. The null hypothesis H0 is that there is no significant difference between the data values ​​in the four quadrants at the significance level α.

[0067] The specific value of the F distribution critical value can be found in the "F distribution table". The "F distribution table" can be obtained through conventional channels such as the Internet. The obtained F distribution critical value can be expressed as F α，dfbetween,dfwithin .

[0068] In step S4, if it is determined that F≤F α，dfbetween,dfwithin , it means that the original hypothesis H0 is not rejected, indicating that there is no significant difference in the data values ​​in the four quadrants at the significance level α.

[0069] This means that it is impossible to identify whether there is a hot spot under the current quadrant size. Then, the current four quadrants are regarded as four populations, and each sub-population is divided into four quadrants according to step S2. Steps S2 and S3 are repeated until it is determined that F>F α，dfbetween,dfwithin .

[0070] If we judge that F>F α，dfbetween,dfwithin , then the original hypothesis H0 is rejected, indicating that the data values ​​in the four quadrants are significantly different at the significance level α.

[0071] This indicates that under the current quadrant size, at least one quadrant area contains a hot spot, so step S5 is executed to perform multiple comparisons.

[0072] In step S5, multiple comparisons (LSD method: Least Significant Difference) are performed. The LSD method is used to further determine which groups have significant differences in means after rejecting the H0 null hypothesis (i.e., assuming that there is no significant difference in means between groups).

[0073] The criterion LSD value can be expressed as:

[0074]

[0075] Among them, MSW is the mean square within the group obtained in step S3, n i Represents the number of elements in the i-th quadrant. The number of elements in the four quadrants is the same. Indicates the given significance level α and within-group degrees of freedom df within The critical value of the t distribution under . It can be obtained by consulting the t-distribution table, which can be easily found on the Internet or in statistics-related books.

[0076] Compare the means of the elements in the four quadrants pairwise. If

[0077]

[0078] This indicates that there is a significant difference between the element values ​​in the i-th quadrant and the j-th quadrant.

[0079] By comparing the four quadrants pairwise, we can select the quadrants (regions) that are significantly different from the other quadrants.

[0080] In step S6, the thermal data matrix of the quadrant with significant differences is taken as the overall area, and steps S2 and S3 are repeatedly performed to perform judgment.

[0081] If we judge that F≤F α.dfbetween.dfwithin , indicating that after the region with significant differences is divided into four equal parts, there are no significant differences in the four inner quadrants. This means that the overall region is now a hotspot.

[0082] In step S7, if it is determined that F>F α，dfbetween，dfwithin , indicating that after the area with significant differences is divided into four equal parts, there are significant differences in the four inner quadrants. This means that the hotspot area is smaller than the overall area at this time, and then repeat steps S5 and S6 until it is determined that F≤F α，dfbetween，dfwithin , and obtain the minimum hotspot area.

[0083] Through the above steps and repeated calculations, all minimum hotspot areas under the significance level α can be obtained.

[0084] In summary, the imaging photodetector focal plane hotspot identification method based on variance analysis in an embodiment of the present invention combines variance analysis with quadtree recursive segmentation, drives hot zone detection by dynamically calculating the sub-region F value, breaks through the sensitivity limitation of the traditional global threshold method, and can adjust the adaptive significance level (α=0.01~0.1) based on the noise intensity, and relax the segmentation conditions under low signal-to-noise ratio; the recursive termination layer embeds LSD multiple comparisons to eliminate pseudo hotspots (|ΔT|<0.5℃), and can achieve sub-pixel positioning (error ±3 pixels) and millisecond real-time processing (256×256 matrix takes <0.8s), which is suitable for high-noise complex scenes.

[0085] The imaging photodetector focal plane hotspot identification method based on variance analysis of the embodiment of the present invention has the following beneficial effects: through the recursive segmentation algorithm driven by variance analysis, multi-dimensional performance improvement of focal plane hotspot detection is achieved: detection sensitivity is significantly enhanced, positioning accuracy reaches ±3 pixels, anti-noise ability is outstanding, and computational efficiency is improved by more than 4 times (256×256 matrix processing takes less than 0.8 seconds); the hotspot criterion is determined by the significance level in the statistical parameters. Compared with traditional methods, it can reduce the judgment of hotspots by human subjective factors and give the specific size of the hotspot; through the coordinated optimization of F-value dynamic threshold screening and quadtree segmentation, the traditional global threshold method solves the problems of missed detection and misjudgment of hotspots with small temperature differences, noise interference and irregular boundaries. It can be widely used in industrial non-destructive testing (such as semiconductor wafer thermal defect identification) and medical thermal imaging analysis (such as precise positioning of tumor edges), and has significant technical universality and cost advantages.

[0086] The embodiment of the present invention further provides a computer device, which may be a server, and its internal structure diagram may be as shown in FIG. Figure 4 As shown. The computer device includes a processor, a memory and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store operating parameter data of each framework. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps of the method of the embodiment of the present invention are implemented.

[0087] Those skilled in the art will understand that Figure 4The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0088] Embodiments of the present application also provide a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the steps of the method of the embodiments of the present application.

[0089] Embodiments of the present application also provide a computer program product, comprising a computer program which, when executed by a processor, implements the steps of the method of the embodiments of the present application.

[0090] The above only describes certain exemplary embodiments of the present application by way of illustration, and it is needless to say that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present application. Therefore, the above figures and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present application.

Claims

1. A method for identifying hot spots in the focal plane of an imaging photodetector based on variance analysis, characterized in that: include: Step S1, using an infrared thermal imager to perform thermal imaging on the focal plane of the imaging photodetector to obtain a thermal data matrix of the focal plane; Step S2, dividing the thermal data matrix into four equal parts as the overall area to form thermal data matrices of four quadrants; Step S3, calculate the F value of the F test, and compare the calculated F value with the given significance level α and the degree of freedom between groups df between and the within-group degrees of freedom df within The critical value F of the F distribution under α，dfbetween，dfwithin Make comparisons; Step S4: If it is determined that F≤F α，dfbetween，dfwithin , then for the thermal data matrix of each quadrant in the four quadrants, repeat steps S2 and S3 until it is determined that F>F α，dfbetween，dfwithin ; Step S5, calculating the LSD value, and determining the quadrant with significant difference from other quadrants by comparing the absolute value of the difference between the element means of any two quadrants with the LSD value; Step S6: take the thermal data matrix of the quadrant with significant differences as the overall area, and repeat steps S2 and S3. If it is judged that F≤F α，dfbetween，dfwithin , then the overall area at this time is taken as the minimum hotspot area; Step S7, if it is determined that F>F α，dfbetween，dfwithin , then repeat steps S5 and S6 until it is determined that F≤F α，dfbetween，dfwithin , and obtain all the minimum hotspot areas under the significance level α.

2. The method according to claim 1, wherein In step S3, the F value is calculated as follows: Among them, MSB represents the mean square between groups, MSW represents the mean square within the group. SSB represents the sum of squares between groups, SSW represents the sum of squares within groups, k represents the number of quadrants, k=4, and N represents the number of all elements in the four quadrants.

3. The method according to claim 2, wherein The between-group sum of squares SSB and within-group sum of squares SSW are calculated as follows: in, represents the mean of all elements in the i-th quadrant; Represents the mean of all elements in the four quadrants, n i Indicates the number of elements in the i-th quadrant, X ij Represents the data value of the jth element in the i-th quadrant.

4. The method according to claim 3, wherein The degrees of freedom between groups df are calculated as follows between and the within-group degrees of freedom df within : df between =k-1, df within =N-k。 5. The method according to claim 3 or 4, wherein: In step S5, the LSD value is calculated as follows: in, Indicates the significance level α and the degree of freedom df within the group within The critical value of the t distribution under .

6. The method according to claim 5, wherein like This indicates that there is a significant difference between the data values ​​in the i-th quadrant and the j-th quadrant. By comparing the element means of the four quadrants pairwise, the quadrant that is significantly different from the other quadrants is selected.

7. The method according to any one of claims 1 to 4, wherein The significance level α was set to 0.05 or 0.

01.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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