A method for screening infrared detector stability

Abstract

This invention discloses an infrared detector stability screening method. By performing windowing calculations on pixels and iterating through each windowing to identify bad pixels, this invention can screen out infrared focal plane detectors with good stability, which has important guiding significance for identifying the stability of detectors.

Description

Technical Field

[0001] This invention relates to the field of infrared technology, and in particular to a method for screening the stability of infrared detectors. Background Technology

[0002] An infrared thermal imaging module consists of an infrared detector, an infrared lens, a signal processing circuit, and an image processing circuit. The infrared detector is the core component of the module. To obtain better imaging results during use, the infrared detector requires two-point or multi-point calibration before optical, signal processing, and image processing are incorporated to produce a clear image. However, due to the complex calibration process, infrared cameras typically only undergo two-point or multi-point calibration on blackbodies at different temperatures before leaving the factory. During subsequent use, the factory-stored calibration parameters for each temperature range are retrieved based on the target temperature to ensure the detector operates within its linear range and achieves optimal imaging quality. Therefore, effectively ensuring the stability of the infrared detector has become a pressing issue that needs to be addressed. Summary of the Invention

[0003] This invention provides a method for screening the stability of infrared detectors, in order to solve the problem that the stability of infrared detectors cannot be accurately determined in the prior art.

[0004] This invention provides a method for screening the stability of infrared detectors, the method comprising:

[0005] Step 1: Install the infrared detector on the prediction test board, control the infrared detector to cool to a preset temperature, and control the preset test board to input a preset bias voltage to the infrared detector so that the infrared detector works normally.

[0006] Step 2: Align the infrared detector with the blackbody source, adjust the blackbody temperature, and collect two-point calibration parameters of the source blackbody at the first temperature T1 and the second temperature T2 respectively.

[0007] Step 3: Adjust the source blackbody temperature to the third temperature T3. Starting from the first row and first column of the infrared detector, take a first preset number of columns and a second preset number of rows of pixels. The number of pixels taken each time is odd. Calculate the median level value of the taken pixels using the correction parameters, and obtain the level value of the pixel at the middle position of the taken pixels. If the difference between the median level value and the level value of the pixel at the middle position is greater than a preset threshold, then the pixel at the middle position is determined to be a bad pixel. Skip one column or row of pixels and draw a window to sequentially take a first preset number of columns and a second preset number of rows of pixels. Again, determine whether the pixel at the middle position of the acquired pixels is a bad pixel. Continue until all pixels are traversed, and all bad pixels are marked and recorded. The median level value is the median value of the level values ​​of the pixels taken each time.

[0008] Step 4: Control the infrared detector to return to room temperature, then execute Step 1, Step 2 and Step 3, and traverse and record the bad elements of the infrared detector again;

[0009] Step 5: Repeat step 4 and calculate the total number of bad pixels obtained from all traversals of the infrared detector. If the ratio of the total number of bad pixels to the number of pixels of the infrared detector exceeds a preset bad pixel threshold, the stability of the infrared detector is determined to be poor.

[0010] Optionally, both the first preset number of columns and the second preset number of rows are odd numbers.

[0011] Optionally, step three specifically includes: adjusting the source blackbody temperature to a third temperature T3, taking 3 rows and 3 columns of pixels starting from the first row and first column of the infrared detector, calculating the median level value of the selected pixels using the correction parameters, obtaining the level value of the pixel at the middle position of the selected pixels, and determining the pixel at the middle position as a bad pixel if the difference between the median level value and the level value of the pixel at the middle position is greater than a preset threshold.

[0012] Skip one column of pixels, then take 3 rows and 3 columns of pixels, and once again determine whether the middle pixel of the newly acquired pixels is a bad pixel, until all pixels have been traversed, and all bad pixels are marked and recorded.

[0013] Optionally, calculating the median level value of the selected pixel using the correction parameters includes:

[0014] The level value of each pixel is calculated using the correction parameters and the third temperature T3. The calculated level values ​​are then sorted in order of magnitude, and the median level value is taken as the median level value.

[0015] Optionally, calculating the total number of bad cells in all traversed infrared detectors includes:

[0016] The bad cells obtained from all the traversals of the infrared detector are superimposed, wherein bad cells that are recorded repeatedly in different traversals are only calculated once in the superposition process.

[0017] Optionally, marking all bad elements includes:

[0018] A zero matrix is ​​constructed, with the same size as the cell specification. The bad cells are marked at corresponding positions within this zero matrix based on their locations during the traversal. 7. The method according to claim 1, characterized in that...

[0019] The preset threshold is set according to the usage conditions of the infrared detector.

[0020] Optionally, the preset bad element threshold is 1%.

[0021] Optionally, the method further includes: performing a comprehensive weight calculation based on the ratio of the total number of bad pixels in the infrared detector to the number of pixels in the infrared detector, the preset bad pixel threshold, and the pixel instability caused by stress due to the power-on / off mode of the infrared detector, so as to comprehensively determine the stability of the infrared detector.

[0022] The beneficial effects of this invention are as follows:

[0023] This invention provides a novel method for evaluating the stability of infrared detectors. By performing windowing calculations on pixels and iterating through each windowing to identify bad pixels, this invention can screen out infrared focal plane detectors with good stability, which has important guiding significance for identifying the stability of detectors.

[0024] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0025] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0026] Figure 1 This is a schematic diagram illustrating the specific working principle of the infrared detector provided in this embodiment of the invention;

[0027] Figure 2 This is a schematic diagram of the fault element identification method for multiple power-on / off cycles of the infrared detector provided in this embodiment of the invention;

[0028] Figure 3 This is a schematic diagram of the operation of the unstable pixel detection system provided in the embodiment of the present invention. Detailed Implementation

[0029] Existing methods primarily rely on national standard criteria to identify bad pixels. However, users' evaluation of detector performance during use goes beyond national standard indicators; they pay more attention to abnormal pixels that will appear in application. Infrared detector users often classify pixels with visible differences after each power-on calibration as bad pixels. This necessitates relative stability between infrared detector pixels. However, pixels identified as bad pixels due to instability cannot be effectively identified using national standard criteria. To address this, this invention provides an infrared detector stability screening method. This invention performs windowed calculations on pixels, iterating through each window to identify bad pixels, thereby screening out infrared focal plane detectors with good stability. This has significant guiding significance for identifying detector stability. The following detailed description, in conjunction with the accompanying drawings and embodiments, further illustrates this invention. It should be understood that the specific embodiments described herein are merely illustrative and do not limit the scope of the invention.

[0030] This invention provides a method for screening the stability of infrared detectors. The method involves performing two-point calibration on the infrared detector and saving the calibration parameters. After multiple power cycles, if the cumulative number of unstable pixels on the detector exceeds a certain threshold, the detector component is determined to have poor stability. See also... Figure 1 and Figure 2 The method described in this embodiment of the invention includes:

[0031] S101. Install the infrared detector on the prediction test board, control the infrared detector to cool to a preset temperature, and control the preset test board to input a preset bias voltage to the infrared detector so that the infrared detector works normally.

[0032] Specifically, after starting the cooling system and bringing the detector to the specified operating temperature, the bias voltage required for normal operation is applied to the detector to enable it to work normally.

[0033] S102. Align the infrared detector with the blackbody source, adjust the temperature of the blackbody, and collect two-point calibration parameters of the blackbody source at the first temperature T1 and the second temperature T2 respectively.

[0034] That is, the embodiment of the present invention performs two-point correction on a uniform blackbody at a fixed temperature and stores the correction parameters for use in subsequent calculation of the median level value of the pixel.

[0035] S103. Adjust the source blackbody temperature to the third temperature T3. Starting from the first row and first column of the infrared detector, take a first preset number of columns and a second preset number of rows of pixels, wherein the number of pixels taken each time is odd. Calculate the median level value of the taken pixels using the correction parameters, and obtain the level value of the pixel at the middle position of the taken pixels. If the difference between the median level value and the level value of the pixel at the middle position is greater than a preset threshold, then determine that the pixel at the middle position is a bad pixel. Then, skip a column or row of pixels and sequentially acquire a first preset number of columns and a second preset number of rows of pixels, and again determine whether the pixel at the middle position of the acquired pixels is a bad pixel, until all pixels are traversed, and all bad pixels are marked and recorded. The median level value is the median value of the level values ​​of the pixels taken each time.

[0036] It should be noted that in the embodiments of the present invention, both the first preset number of columns and the second preset number of rows are odd numbers. This is because only when both the number of rows and columns are odd will there be pixels in the middle position. Only by calculating pixels in the middle position can the accuracy of judging bad pixels be effectively guaranteed. In addition, the values ​​of each temperature in the embodiments of the present invention can also be set according to actual needs. The present invention does not make specific limitations on this. For example, the first temperature T1 and the second temperature T2 in the embodiments of the present invention can be arbitrarily set according to actual needs to obtain more accurate correction parameters.

[0037] Specifically, in this embodiment of the invention, the source blackbody temperature is adjusted to a third temperature T3. Starting from the first row and first column of the infrared detector, three rows and three columns of pixels are taken. The median level value of the taken pixels is calculated using the correction parameters, and the level value of the pixel at the middle position of the taken pixels is obtained. If the difference between the median level value and the level value of the pixel at the middle position is greater than a preset threshold, then the pixel at the middle position is determined to be a bad pixel.

[0038] Skip one column of pixels, then take 3 rows and 3 columns of pixels, and once again determine whether the middle pixel of the newly acquired pixels is a bad pixel, until all pixels have been traversed, and all bad pixels are marked and recorded.

[0039] For example, using a 3x3 sliding window method on the corrected image, and based on a fixed preset threshold, pixels within a 3x3 neighborhood whose difference between the level of the middle pixel and the median level of the 3x3 neighborhood exceeds the threshold are identified and marked as bad pixels. Taking a detector with an array size of 320x256 as an example... Figure 2The diagram illustrates the defect detection method for infrared detectors after multiple power-on / off cycles. For the acquired calibration data, the pixel at position (2,2) is first used as the median pixel. The pixel levels at positions (1,1) to (3,3) are arranged from smallest to largest to find the median level. This median level is then compared with the level of the pixel at position (2,2). If the difference between the two is greater than a threshold, it is determined to be a defective pixel. A zero matrix is ​​then created, with the matrix size matching the pixel specifications. Position (2,2) is marked as 1, thus identifying it as a defective pixel. Simultaneously, a variable initially set to 0 is defined, and... The count is 1; if it is not greater than the threshold, the position (2,2) is marked as 0, no count is performed, and the pixel at position (3,2) is judged along the column direction with a 3x3 pixel area; if it exceeds the threshold, the position in the zero matrix is ​​marked as 1, and the count is incremented by 1; if it does not exceed the threshold, the pixel at position (4,2) is judged until the entire area of ​​pixels is searched; the position with a value of 1 in the initial zero matrix is ​​the coordinate of the bad pixel, and the value of the variable that was initially 0 is now the total number of bad pixels.

[0040] It should be noted that, in this embodiment of the invention, the level value of each pixel is calculated using the correction parameters and the third temperature T3, and the calculated level values ​​are sorted in order of magnitude, with the median level value being the median level value.

[0041] Furthermore, in this embodiment of the invention, a zero matrix is ​​established, with the same size as the pixel specification, and the bad elements are marked at the corresponding positions in the zero matrix according to the positions of the traversed bad elements.

[0042] In practical implementation, the preset threshold is set according to the usage conditions of the infrared detector. In this embodiment of the invention, the preset bad element threshold is 1%. Of course, in practical implementation, those skilled in the art can arbitrarily set the above-mentioned thresholds according to actual needs, and this invention will not discuss this in detail.

[0043] S104. Control the infrared detector to return to room temperature, then execute steps one, two and three, and traverse and record the bad elements of the infrared detector again.

[0044] That is, after step 103, the detector is turned off and allowed to return to room temperature; the cooler is turned on again, the previous bias voltage and correction parameters are applied, and the corrected image is again screened in a 3x3 sliding window manner. Using this threshold as the standard, the bad element locations after this power-on are searched in the same way; compared with the previously stored bad element list, and OR operation is performed on each position of the two matrices to obtain the cumulative bad element location coordinates and bad element count;

[0045] S105. Repeat step four and calculate the total number of bad pixels obtained from all traversals of the infrared detector. When the ratio of the total number of bad pixels to the number of pixels of the infrared detector exceeds a preset bad pixel threshold, the stability of the infrared detector is determined to be poor.

[0046] After multiple (at least three) power cycles, the total number of bad pixels after each power cycle is calculated. Detectors with more than 1% of the total number of pixels are considered to have poor stability. It should be noted that in the total number of bad pixels after each power cycle, duplicate bad pixels counted from different power cycles are removed. That is, each bad pixel is counted only once to achieve accurate identification of detector stability.

[0047] In specific implementation, embodiments of the present invention may also perform comprehensive weight calculation based on the ratio of the total number of bad pixels of the infrared detector to the number of pixels of the infrared detector, the preset bad pixel threshold, and the pixel instability caused by stress due to the power-on / off mode of the infrared detector, in order to comprehensively determine the stability of the infrared detector.

[0048] That is, in this embodiment of the invention, weights can be set for the ratio of the total number of bad pixels of the infrared detector to the number of pixels of the infrared detector and the difference between the ratio and the preset bad pixel threshold, as well as for the pixel instability caused by stress due to the power-on / off mode of the infrared detector, so as to better calculate the stability of the infrared detector.

[0049] The following will provide a detailed explanation and illustration of the method described in the embodiments of the present invention through a specific example:

[0050] The mid-wave mercury cadmium telluride infrared detector consists of a mid-wave mercury cadmium telluride chip and a readout circuit. It requires a specific external bias voltage and timing control signal to operate normally. The specific operating mode of the infrared detector in this embodiment is as follows: Figure 1 As shown, the infrared sensor signal processing module collects the voltage value detected by the infrared detector and transmits it to the backend for processing. During processing, the correction parameters of the infrared detector for the calibrated blackbody are called back. This process requires the infrared detector pixels to have a certain degree of stability. If the detector stability is poor during use, the pixel output voltage value will fluctuate greatly, causing the original correction parameters to fail to provide a good image quality.

[0051] Due to various factors such as material defects and device manufacturing processes, infrared detectors inevitably contain some invalid pixels, also known as blind pixels, with abnormal responses. The identification of invalid pixels is based on the national standard GB / T 17444-2013. This standard defines dead pixels and overheated pixels as follows: dead pixels are those with a response rate less than half the average response, and overheated pixels are those with a noise voltage greater than twice the average noise voltage. Typically, before leaving the factory, detectors are only identified by blind pixel detection on the original level diagram and data according to GB / T 17444-2013. However, this method cannot identify all pixels with poor stability that appear during later use. With the rapid development of infrared countermeasures and other technologies, the application scenarios of infrared detectors are becoming increasingly widespread, and infrared core algorithms are being upgraded, leading to increasingly higher requirements for image quality. To ensure image quality after correction parameter adjustments, more and more infrared core users are demanding higher stability from infrared detectors. This invention provides a novel detection method to identify infrared detectors with poor stability before they leave the factory.

[0052] Taking the MW320x256 detector as an example, such as Figure 3 The diagram shows the operation of the unstable pixel detection system. The testing device consists of an infrared detector, a test board (data acquisition module, bias voltage control module, parameter storage module, and data processing module), a blackbody source, and a DC power supply. The specific testing steps are as follows:

[0053] Step A: Install the infrared detector on the test board and drive the test board and detector cooler with a DC power supply. When the detector is cooled to the specified temperature, the test board inputs a bias voltage to the detector to make it work normally.

[0054] Step B: Point the infrared detector at the blackbody source, adjust the blackbody temperature, collect two calibration parameters (T2>T1) at the blackbody temperatures T1 and T2 respectively, and store them in the parameter storage module.

[0055] Step C: Adjust the blackbody temperature to T3 (T3≠T1&&T3≠T2), and set the threshold size for unstable pixels;

[0056] Step D: Using a 3x3 area as the window size, slide the window to compare the difference between the code value of the middle pixel and the midpoint of the area. If the difference is greater than a threshold, the middle pixel at this position is determined to be a bad pixel. Store the position coordinates and total number of bad pixels in the current state;

[0057] Step E: Turn off the DC power and let it stand for 1 hour;

[0058] Step F: Drive the test board and detector cooler with DC power supply. When the detector is cooled to the specified temperature, the test board inputs the bias voltage and the correction parameters stored in step B to the detector to make it work normally.

[0059] Step G: Facing the blackbody temperature T3, repeat step D again and accumulate the location and number of bad elements;

[0060] Step H, then repeat step EG multiple times (the specific number of power cycles can be determined based on actual usage), and record the changes in the location and number of faulty components under multiple tests.

[0061] The stability of the detector is characterized by repeated power-on and power-off cycles. If the number of bad pixels accumulated is greater than a certain range of the total number of pixels, the detector is considered to have poor stability and is not suitable for subsequent delivery and use (the specific number can be adjusted according to different project requirements).

[0062] By using a 3x3 sliding window comparison median method, pixels that appear abrupt to the human eye after correction are quantified, allowing data to replace human visual judgment. At the same time, by combining multiple power-on and power-off methods, unstable pixels caused by stress or other factors are characterized more quickly, thus enabling the identification of unstable infrared detectors.

[0063] In summary, the embodiments of the present invention provide a method for detecting unstable pixels after multiple power-on cycles of a full-band full-array mercury cadmium telluride infrared detector. This method can screen unstable detector pixels and has important guiding significance for identifying the stability of the detector.

[0064] Although preferred embodiments of the invention have been disclosed for illustrative purposes, those skilled in the art will recognize that various modifications, additions, and substitutions are possible, and therefore the scope of the invention should not be limited to the embodiments described above.

Claims

1. An infrared detector stability screening method, comprising: include: Step 1: Install the infrared detector on the preset test board, control the infrared detector to cool to the preset temperature, and control the preset test board to input a preset bias voltage to the infrared detector so that the infrared detector works normally. Step 2: Align the infrared detector with the blackbody, adjust the temperature of the blackbody, and collect the two-point calibration parameters of the blackbody at the first temperature T1 and the second temperature T2 respectively. Step 3: Adjust the surface source blackbody temperature to the third temperature T3. Starting from the first row and first column of the infrared detector, take a first preset number of columns and a second preset number of rows of pixels, where the number of pixels taken each time is odd. Calculate the median level value of the taken pixels using the correction parameters and the third temperature T3, and obtain the level value of the pixel at the middle position of the taken pixels. If the difference between the median level value and the level value of the pixel at the middle position is greater than a preset threshold, then the pixel at the middle position is determined to be a bad pixel. Skip one column or row of pixels and draw a window to sequentially acquire a first preset number of columns and a second preset number of rows of pixels, and again determine whether the pixel at the middle position of the acquired pixels is a bad pixel, until all pixels are traversed, and all bad pixels are marked and recorded. Step 4: Control the infrared detector to return to room temperature, then execute Step 1, Step 2 and Step 3, and traverse and record the bad elements of the infrared detector again; Step 5: Repeat step 4 and calculate the total number of bad pixels obtained from all traversals of the infrared detector. If the ratio of the total number of bad pixels to the number of pixels of the infrared detector exceeds a preset bad pixel threshold, the stability of the infrared detector is determined to be poor.

2. The method according to claim 1, characterized in that, Both the first preset number of columns and the second preset number of rows are odd numbers.

3. The method of claim 2, wherein, Step three specifically includes: Adjust the surface source blackbody temperature to the third temperature T3. Starting from the first row and first column of the infrared detector, take 3 rows and 3 columns of pixels. Calculate the median level value of the selected pixels using the correction parameters and the third temperature T3, and obtain the level value of the pixel at the middle position of the selected pixels. If the difference between the median level value and the level value of the pixel at the middle position is greater than a preset threshold, then the pixel at the middle position is determined to be a bad pixel. Skip one column of pixels, then take 3 rows and 3 columns of pixels, and once again determine whether the middle pixel of the newly acquired pixels is a bad pixel, until all pixels have been traversed, and all bad pixels are marked and recorded.

4. The method of claim 3, wherein, The median level value of the selected pixel is calculated using the correction parameters and the third temperature T3, including: The level value of each pixel is calculated using the correction parameters and the third temperature T3. The calculated level values ​​are then sorted in order of magnitude, and the median level value is taken as the median level value.

5. The method according to any one of claims 1 to 4, characterized in that, The calculation of the total number of bad cells in all traversals of the infrared detector includes: The bad cells obtained from all the traversals of the infrared detector are superimposed, wherein bad cells that are recorded repeatedly in different traversals are only calculated once in the superposition process.

6. The method according to any one of claims 1 to 4, characterized in that, The process of marking all bad cells includes: A zero matrix is ​​created, with the same size as the cell. The bad cells are then marked in the zero matrix according to their positions during the traversal.

7. The method according to any one of claims 1-4, characterized in that, The preset threshold is set according to the usage conditions of the infrared detector.

8. The method according to any one of claims 1-4, characterized in that, The preset bad element threshold is 1%.

9. The method according to any one of claims 1-4, characterized in that, The method further includes: The stability of the infrared detector is comprehensively determined by calculating a comprehensive weight based on the ratio of the total number of bad pixels to the number of pixels in the infrared detector, the preset bad pixel threshold, and the pixel instability caused by stress due to the switching mode of the infrared detector.

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