Infrared nondestructive testing sequence image processing method and processing system

Abstract

The present invention discloses an infrared nondestructive testing sequence image processing method and processing system, the processing method comprises using an infrared thermal imager to obtain an initial infrared image and a time series image set of a sample, using a polynomial fitting method based on the least squares method for calculation, then performing linear gain on the second-order derivative image set, and performing enhancement processing on the second-order derivative image after gain, and finally performing grayscale processing using a histogram equalization method. The processing method directly processes the initial infrared image collected by the infrared thermal imager, and the obtained temperature data is closer to the actual sampling temperature; it can overcome the interference of uneven heating and random noise on defect recognition, improve the image signal-to-noise ratio and contrast, make the defect more obvious, the edge clearer, more conducive to defect recognition, improve the defect recognition depth, and enhance the defect detection capability of the infrared thermal imager.

Description

Technical Field

[0001] The present invention relates to the field of infrared nondestructive testing technology, and in particular to an infrared nondestructive testing sequence image processing method and processing system. Background Art

[0002] Carbon fiber reinforced composite materials are a new type of material that has been increasingly studied and applied in recent years. They have many advantages such as high strength, light weight, high temperature resistance, and corrosion resistance. Therefore, they have been widely used in aviation, aerospace, shipbuilding, automobile manufacturing, new energy, military industry, etc. Carbon fiber materials are made of carbon fiber and resin matrix. During the production, processing and use process, they are prone to defects such as delamination, holes, debonding, and cracks due to various reasons. These defects will seriously affect the quality and performance of the material and reduce its service life.

[0003] Active infrared thermal imaging detection is a detection technology that uses halogen lamps, flash lamps and other excitation sources to apply certain thermal excitation to objects, uses infrared thermal imagers to measure the infrared radiation energy of objects and converts it into temperature distribution, performs corresponding calculations and processing on the collected results, and then determines whether there are defects. Active infrared thermal imaging detection has the characteristics of fast detection speed, large detection area, intuitive results, and repeatability. Therefore, it has been widely used in aerospace composite materials, electronic circuit production and other fields in recent years. However, the original thermal images collected by infrared thermal imagers have low contrast and high noise, and are easily affected by the uneven radiation of the excitation source, which makes the internal defects of the sample unclear and unclear, affecting the discovery, identification and judgment of internal defects of the object. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes an infrared non-destructive testing sequence image processing method, which can overcome the interference of uneven heating and random noise on defect recognition, improve the image signal-to-noise ratio and contrast, make the defects more obvious and the edges clearer, facilitate defect recognition, improve the defect recognition depth, and enhance the defect detection capability of the infrared thermal imager.

[0005] On the one hand, an embodiment of the present invention provides a method for processing infrared nondestructive testing sequence images, comprising:

[0006] Collecting infrared thermal imaging images of the sample to obtain a first image data set;

[0007] Applying thermal excitation to the surface of the sample and collecting infrared thermal imaging images of the sample to obtain a second image data set;

[0008] According to the first image data set, performing temperature difference processing at the pixel level on the second image data set to obtain a temperature difference image data set;

[0009] Taking logarithmic calculations on the temperature difference image data set, and calculating using a polynomial fitting method based on the least squares method to obtain temperature difference-time data;

[0010] According to the temperature difference-time data, the first image data set is converted into a third image data set, and the second-order derivative of the time of the temperature data of the third image data set is taken to obtain a fourth image data set with respect to the second-order derivative;

[0011] Performing linear gain on data corresponding to each image in the fourth image data set to expand the dynamic range of the fourth image data set to obtain a fifth image data set;

[0012] Performing enhancement processing on the fifth image data set to obtain an image basic layer and an image detail layer, and amplifying the image detail layer and superimposing it with the image basic layer to obtain a first infrared image;

[0013] The grayscale range of the first infrared image is adjusted and processed using a histogram equalization method to obtain a second infrared image.

[0014] According to some embodiments of the present invention, before the step of displaying the detection result on the display module, the step further includes:

[0015] According to some embodiments of the present invention, performing pixel-level temperature difference processing on the second image data set according to the first image data set to obtain the temperature difference image data set includes:

[0016] The second image data set is processed at the pixel level for temperature difference based on the temperature difference formula to obtain a temperature difference image data set. The temperature difference formula is:

[0017] ΔT(t) = T(t) - T 0 (t)

[0018] Where, T 0 is the first image data set, T is the second image data set, and t is time.

[0019] According to some embodiments of the present invention, the step of calculating the logarithm of the temperature difference image data set and calculating using a polynomial fitting method based on a least squares method to obtain temperature difference-time data includes:

[0020] The second image data set is processed at the pixel level for temperature difference based on a polynomial fitting formula to obtain temperature difference-time data. The polynomial fitting formula is: , where N is the number of polynomial fitting times, a n are the coefficients of polynomials of various orders.

[0021] According to some embodiments of the present invention, converting the first image data set into a third image data set according to the temperature difference-time data, and taking the second-order derivative of time of the temperature data of the third image data set to obtain a fourth image data set about the second-order derivative includes:

[0022] The temperature data of each pixel in the first image data set is converted based on a temperature conversion formula to obtain a third image data set, and the temperature conversion formula is: , where T 0 is the temperature data of a single pixel in the first image data set, T is the temperature data of a single pixel in the third image data, and t is time;

[0023] The second-order derivative of time is taken for the temperature data of the third image data set based on the second-order derivative formula to obtain a fourth image data set about the second-order derivative, and the second-order derivative formula is: , where N is the number of polynomial fitting times, a n are the coefficients of polynomials of various orders.

[0024] According to some embodiments of the present invention, performing linear gain on data corresponding to each image in the fourth image data set to expand the dynamic range of the fourth image data set to obtain the fifth image data set includes:

[0025] A linear gain is performed on the grayscale data corresponding to each image in the fourth image data set based on an image linear gain formula, where the image linear gain formula is: , where P is the input image, P 1 is the output gain image, i is the pixel index of the image, P(i) is the grayscale value of the i-th pixel in the input image, P1(i) is the grayscale value of the i-th pixel in the gain image, max(P) is the maximum value of the grayscale data of the input image, and min(P) is the minimum value of the grayscale data of the input image.

[0026] According to some embodiments of the present invention, the step of enhancing the fifth image data set to obtain an image base layer and an image detail layer, and amplifying the image detail layer and superimposing the image detail layer with the image base layer to obtain a first infrared image includes:

[0027] The first image data set is converted into a third image data set based on an image enhancement processing formula, where the image enhancement processing formula is: , where B is Image base layer, D Image detail layer ， i and k are pixel indexes. B (i) is the gray value of the i-th pixel in the basic layer of the image, D(i) is the gray value of the i-th pixel in the detail layer of the image, Ω represents the entire area of ​​the input image, and a is when the window center is at pixel i k and b k The mean value of k , b k is the transformation coefficient when the window center is located at pixel k, is the standard deviation of the local window pixel corresponding to pixel point k, N ω is the number of pixels in the local window, α and β is the algorithm adjustment factor, is the gradient of the local window of the input image, j is the gradient number, Represents the local window pixel average corresponding to pixel point k;

[0028] The image detail layer is enlarged based on an image detail enlargement processing formula and is superimposed on the image basic layer to obtain a first infrared image. The image detail enlargement processing formula is: , where γ and λ are enhancement coefficients, P 2 is the first infrared image output.

[0029] According to some embodiments of the present invention, the grayscale range of the first infrared image is adjusted and processed by a histogram equalization method to obtain a second infrared image, including:

[0030] The grayscale range of the first infrared image is adjusted based on an image grayscale adjustment formula, where the image grayscale adjustment formula is: , where P 3 is the image after grayscale processing, P2(i) is the grayscale value of the i-th pixel of the input image, max(P) is the maximum value of the grayscale data of the input image, and min(P) is the minimum value of the grayscale data of the input image;

[0031] Based on the histogram equalization formula, the image P 3 The second infrared image is obtained by using a histogram equalization method. The histogram equalization formula is: , where n iis the number of gray values ​​i, N is the number of pixels in the image, P 4 is the second infrared image output.

[0032] On the other hand, an embodiment of the present invention provides an infrared nondestructive testing sequence image processing system, which is used to implement the above-mentioned infrared nondestructive testing sequence image processing method, and the system includes:

[0033] An image acquisition module, used to acquire a first image data set and a second image data set of a sample;

[0034] an image fitting module, configured to perform pixel-level temperature difference processing on the second image data set to obtain a temperature difference image data set, perform logarithm calculation on the temperature difference image data set, and perform calculation using a polynomial fitting method based on a least squares method to obtain temperature difference-time data, convert the first image data set into a third image data set based on the temperature difference-time data, and take a second-order derivative of time for the temperature data of the third image data set to obtain a fourth image data set with respect to the second-order derivative;

[0035] an image enhancement module, configured to perform linear gain on data corresponding to each image in the fourth image data set to expand the dynamic range of the fourth image data set to obtain a fifth image data set, perform enhancement processing on the fifth image data set to obtain an image basic layer and an image detail layer, and amplify the image detail layer and perform superposition processing on the image basic layer to obtain a first infrared image;

[0036] The image grayscale processing module is used to adjust the grayscale range of the first infrared image and process it using a histogram equalization method to obtain a second infrared image.

[0037] On the other hand, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program, the processor executes the above-mentioned infrared non-destructive testing sequence image processing method.

[0038] On the other hand, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned infrared non-destructive testing sequence image processing method are implemented.

[0039] The embodiments of the present invention have at least the following beneficial effects:

[0040] Therefore, based on the initial infrared image collected by the infrared thermal imager, the present invention proposes a sequence image processing method suitable for active infrared non-destructive testing according to the propagation mode and characteristics of infrared heat waves, collects infrared thermal imaging images of the sample, obtains the initial infrared image and time series image set of the sample, performs temperature difference processing at the pixel level on the time series image set, obtains a temperature difference image data set, and then performs logarithmic calculation, fitting processing, linear gain, image enhancement and grayscale processing to improve the signal-to-noise ratio and contrast of the image. Compared with other infrared image enhancement methods, the original temperature data collected by the infrared thermal imager is directly processed, and the obtained temperature data is closer to the actual sampling temperature. It can overcome the interference of uneven heating and random noise on defect recognition, improve the image signal-to-noise ratio and contrast, make defects more obvious, and make edges clearer, which is more conducive to defect recognition, improve the defect recognition depth, and enhance the defect detection capability of the infrared thermal imager.

[0041] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0043] Figure 1 It is a flow chart of the infrared nondestructive testing sequence image processing method according to an embodiment of the present invention;

[0044] Figure 2 A flowchart of crack defect detection of carbon fiber composite material according to an embodiment of the present invention;

[0045] Figure 3 A first design drawing of a carbon fiber composite material sample according to an embodiment of the present invention;

[0046] Figure 4 A second design drawing of a carbon fiber composite material sample according to an embodiment of the present invention;

[0047] Figure 5 A physical picture of a carbon fiber composite material sample according to an embodiment of the present invention;

[0048] Figure 6 is an initial infrared image of a carbon fiber composite material sample according to an embodiment of the present invention;

[0049] Figure 7 The final infrared image processed by the infrared nondestructive testing sequence image processing method according to the embodiment of the present invention;

[0050] Figure 8It is a module diagram of an infrared nondestructive testing sequence image processing system using an embodiment of the present invention;

[0051] Fig. 9 The figure is a schematic diagram of the internal structure of a computer device using an embodiment of the present invention. DETAILED DESCRIPTION

[0052] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0053] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0054] In the description of the present invention, "several" means one or more, "multiple" means more than two, greater than, less than, and exceeding are understood as not including the number itself, and "above", "below", and "within" are understood as including the number itself. If there is a description of "first", "second", etc., it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0055] In the description of the present invention, unless otherwise clearly defined, words such as “set”, “install”, “connect” and “connected” should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above words in the present invention based on the specific content of the technical solution.

[0056] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments.

[0057] See also Figure 1 This embodiment provides an infrared nondestructive testing sequence image processing method, which mainly includes steps S101 to S108:

[0058] S101, collecting infrared thermal imaging images of the sample to obtain a first image data set;

[0059] S102, applying thermal excitation to the surface of the sample, and collecting infrared thermal imaging images of the sample to obtain a second image data set;

[0060] S103, performing pixel-level temperature difference processing on the second image data set according to the first image data set to obtain a temperature difference image data set;

[0061] S104, performing logarithmic calculation on the temperature difference image data set, and performing calculation using a polynomial fitting method based on the least squares method to obtain temperature difference-time data;

[0062] S105. Convert the first image data set into a third image data set according to the temperature difference-time data, and take the second-order derivative of time of the temperature data of the third image data set to obtain a fourth image data set with respect to the second-order derivative;

[0063] S106, performing linear gain on data corresponding to each image in the fourth image data set to expand the dynamic range of the fourth image data set to obtain a fifth image data set;

[0064] S107, performing enhancement processing on the fifth image data set to obtain an image basic layer and an image detail layer, and enlarging the image detail layer and superimposing it with the image basic layer to obtain a first infrared image;

[0065] S108 , adjusting the grayscale range of the first infrared image and processing it using a histogram equalization method to obtain a second infrared image.

[0066] Based on the initial infrared image collected by the infrared thermal imager, according to the propagation mode and characteristics of infrared heat waves, the embodiment of the present application proposes a sequence image processing method suitable for active infrared non-destructive testing, performs infrared thermal imaging image collection on the sample, obtains the initial infrared image and time series image set of the sample, performs temperature difference processing at the pixel level on the time series image set, obtains a temperature difference image data set, and then performs logarithmic calculation, fitting processing, linear gain, image enhancement and grayscale processing to improve the signal-to-noise ratio and contrast of the image. Compared with other infrared image enhancement methods, the original temperature data collected by the infrared thermal imager is directly processed, and the obtained temperature data is closer to the actual sampling temperature. It can overcome the interference of uneven heating and random noise on defect recognition, improve the image signal-to-noise ratio and contrast, make defects more obvious, and make edges clearer, which is more conducive to defect recognition, improve defect recognition depth, and enhance the defect detection capability of infrared thermal imagers.

[0067] Specifically, the infrared nondestructive testing sequence image processing method of the embodiment of the present application may include the following detailed steps:

[0068] 1. Use an infrared thermal imager to collect images of the sample and obtain the initial infrared image of the sample T 0 (ie, the first image data set in S101).

[0069] For example, the infrared thermal imager uses a sampling frequency of 1s / time, and the sampling period is set to 60s. After one sampling period, 60 infrared images are obtained, and the pixel size of each image is 1080×900. Then the initial infrared image T The data size of 0 is 60×1080×900.

[0070] 2. Actively apply thermal excitation to the sample surface and use an infrared thermal imager to collect a time series of images. T (ie, the second image data set in S102).

[0071] 3. Process the temperature sequence of each pixel in the sequence image set according to the pixel number order to obtain the temperature difference image data set:

[0072] ΔT ( t )= T ( t )- T 0( t )

[0073] In the formula, T 0 is the initial infrared image, T is the time series image set, and t is the time.

[0074] 4. Take the logarithm of the temperature difference of each pixel and its corresponding time series, and then use the polynomial fitting method based on the least squares method to take the N-time fitting coefficients for calculation, and get: , where N is the number of polynomial fitting times, a n are the coefficients of polynomials of various orders.

[0075] 5. Convert the temperature difference-time logarithmic relationship of each pixel obtained by fitting in step 4 into a temperature-time relationship to obtain the third image data set: , where T 0 is the initial infrared image, T is the time series image set, and t is the time.

[0076] 6. Take the second derivative of temperature with respect to time to obtain the fourth image data set: , where N is the number of polynomial fitting times, a n are the coefficients of polynomials of various orders.

[0077] 7. For each image in the second-order derivative image set P Perform linear gain to obtain an image with expanded dynamic range P 1 : , where P is the input image, P 1is the output gain image, i is the pixel index of the image, P(i) is the grayscale value of the i-th pixel in the input image, P1(i) is the grayscale value of the i-th pixel in the gain image, max(P) is the maximum value of the grayscale data of the input image, and min(P) is the minimum value of the grayscale data of the input image.

[0078] 8. Use the image enhancement algorithm to enhance each image in the image set P 1 Processing to obtain the basic layer of the image B and image detail layer D : , where B is Image base layer, D Image detail layer ， i and k are pixel indexes. B (i) is the gray value of the i-th pixel in the basic layer of the image, D(i) is the gray value of the i-th pixel in the detail layer of the image, Ω represents the entire area of ​​the input image, and a is when the window center is at pixel i k and b k The mean value of k , b k is the transformation coefficient when the window center is located at pixel k, is the standard deviation of the local window pixel corresponding to pixel point k, N ω is the number of pixels in the local window, α and β is the algorithm adjustment factor, is the gradient of the local window of the input image, j is the gradient number, Represents the local window pixel average corresponding to pixel point k.

[0079] 9. Introduce enhancement coefficients γ and λ to enhance the detail layer D Further zoom in to get the enhanced detail layer D 1 , the enhanced detail layer D 1 and base layer B Add the details to get the second-order derivative image P 2 : , where γ and λ are enhancement coefficients, P 2 is the first infrared image output.

[0080] Preferably, γ is 0.4 and λ is 0.85.

[0081] 10. Enhanced second-order derivative image P 2 , use linear mapping to round down and adjust the image grayscale range to [0, 255]: , where P 3 is the image after grayscale processing, P2(i) is the grayscale value of the i-th pixel of the input image, max(P) is the maximum value of the grayscale data of the input image, and min(P) is the minimum value of the grayscale data of the input image;

[0082] 11. Use histogram equalization method to image P 3 Processing, the grayscale of the image with a large number of pixels is widened, and the grayscale of the image with a small number of pixels is reduced, that is, the grayscale value of the first infrared image is changed to obtain the second infrared image: , where n i is the number of gray values ​​i, N is the number of pixels in the image, P 4 is the second infrared image output.

[0083] See also Figure 2 , the following takes the crack defect detection of carbon fiber reinforced composite materials as an example to describe the main steps of defect detection:

[0084] S201, use an infrared thermal imager to collect images of the sample 300 to obtain an initial infrared image. The collected initial infrared image can be found in Figure 6 .

[0085] It should be noted that the sample 300 is based on Figure 3 and Figure 4 It is made of carbon fiber composite material sample design drawings. Figure 5 This is a physical picture of the carbon fiber composite material sample.

[0086] S202, irradiating the sample 300 with an external thermal excitation source for a period of time.

[0087] Exemplarily, the external thermal excitation source uses two halogen lamps with a power of 800W-1500W, which are placed on both sides of the infrared thermal imager to ensure uniform heating of the sample surface; and the heating time is accurately controlled to achieve continuous thermal excitation.

[0088] S203: During the thermal excitation process, an infrared thermal imager is used to collect images to obtain a time series image set.

[0089] S204: Process the time series image set using the above infrared nondestructive testing sequence image processing method to obtain a final infrared image after processing. Figure 7 .

[0090] S205. Determine whether the sample 300 has defects based on the final infrared image.

[0091] See also Figure 7 , the defect position and contour of the crack 310 can be clearly identified from the figure, and the defect length and depth can be quantitatively calculated after calibrating the image.

[0092] The embodiment of the present application proposes a feasible and specific data processing formula to process the original temperature data collected by the thermal imager, so as to obtain an optimized infrared image; the algorithm formula is clear, easy to implement, and has high computational efficiency. It can reduce the change of the original temperature as much as possible and fit the actual sampling temperature, realize image noise reduction, use specific algorithms to improve contrast, achieve detail enhancement, and improve the defect detection depth of the infrared thermal imager. It can overcome the interference of uneven heating and random noise on defect identification, make defects more obvious, and make edges clearer, which is more conducive to defect identification. The detection process is simple and efficient, and has great application value in the non-destructive testing and evaluation of structures such as non-metallic composite materials (such as carbon fiber reinforced composite materials, glass fiber composite materials) and metal materials.

[0093] See also Figure 8 The embodiment of the present application further provides an infrared nondestructive testing sequence image processing system, which is used to implement the above-mentioned infrared nondestructive testing sequence image processing method, and the system includes:

[0094] An image acquisition module 810 is used to acquire a first image data set and a second image data set of a sample;

[0095] An image fitting module 820 is used to perform pixel-level temperature difference processing on the second image data set to obtain a temperature difference image data set, perform logarithm calculation on the temperature difference image data set, and perform calculation using a polynomial fitting method based on a least squares method to obtain temperature difference-time data, convert the first image data set into a third image data set based on the temperature difference-time data, and take a second-order derivative of time for the temperature data of the third image data set to obtain a fourth image data set with respect to the second-order derivative;

[0096] an image enhancement module 830, configured to perform linear gain on data corresponding to each image in the fourth image data set to expand the dynamic range of the fourth image data set to obtain a fifth image data set, perform enhancement processing on the fifth image data set to obtain an image basic layer and an image detail layer, and amplify the image detail layer and perform superposition processing on the image basic layer to obtain a first infrared image;

[0097] The image grayscale processing module 840 is used to adjust the grayscale range of the first infrared image and process it using a histogram equalization method to obtain a second infrared image.

[0098] It should be noted that the infrared non-destructive testing sequence image processing system embodiment and the above-mentioned infrared non-destructive testing sequence image processing method embodiment are based on the same inventive concept. The contents not involved in the infrared non-destructive testing sequence image processing system embodiment can be referred to the above-mentioned infrared non-destructive testing sequence image processing method embodiment, and will not be repeated here.

[0099] See also Fig. 9 The embodiment of the present application further provides a computer device 900, which includes a memory 901 and a processor 902. The processor 902 is used to run the computer program instructions in the memory 901 to implement the infrared non-destructive testing sequence image processing method.

[0100] The memory 901 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 901 may be an internal storage unit of a computer device, such as a hard disk of a computer device. In other embodiments, the memory 901 may also be a storage device of an external computer device, such as a plug-in hard disk configured in a computer device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. The memory 901 may also include both an internal storage unit of a computer device and an external storage device. The memory 901 may be used not only to store application software and various types of data installed in a computer device, such as the code of an image enhancement method, etc., but also to temporarily store data that has been output or is to be output.

[0101] The computer device 900 also includes a bus 903. The bus 903 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0102] The computer device 900 may further include a display component 904. The display component 904 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display component 904 may also be appropriately referred to as a display device or a display unit, which is used to display information processed in the computer device 100 and to display a visual user interface.

[0103] The computer device 900 may further include a communication component 905. The communication component 905 may optionally include a wired communication component and / or a wireless communication component (such as a WI-FI communication component, a Bluetooth communication component, etc.), which is generally used to establish a communication connection between the computer device 100 and other computer devices.

[0104] Fig. 9 The computer device 900 having only some components is shown, and those skilled in the art will appreciate that Fig. 9 The illustrated structure does not constitute a limitation on the computer device 100 , and the computer device 100 may include fewer or more components than illustrated, or may combine certain components, or may have different component arrangements.

[0105] The embodiment of the present application also provides a computer-readable storage medium, when the instructions in the storage medium are executed by the processor of the computer, the computer can execute the infrared non-destructive testing sequence image processing method provided in the above embodiment. For example, the computer-readable storage medium can be a ROM, RAM, CD-ROM, magnetic tape, floppy disk, USB flash drive and optical data storage device. It is worth noting that the computer-readable storage medium mentioned in the embodiment of the present application can be a non-volatile storage medium, in other words, it can be a non-transient storage medium.

[0106] It should be understood that all or part of the steps to implement the above embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions; the computer instructions can be stored in the above-mentioned computer-readable storage medium. That is, in some embodiments, a computer program product containing instructions is also provided, which, when run on a computer, enables the computer to execute the infrared non-destructive testing sequence image processing method provided in the above-mentioned embodiment.

[0107] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A method for processing infrared nondestructive testing sequence images, characterized in that: include: Collecting infrared thermal imaging images of the sample to obtain a first image data set; Applying thermal excitation to the surface of the sample and collecting infrared thermal imaging images of the sample to obtain a second image data set; According to the first image data set, performing temperature difference processing at the pixel level on the second image data set to obtain a temperature difference image data set; Taking logarithmic calculations on the temperature difference image data set, and calculating using a polynomial fitting method based on the least squares method to obtain temperature difference-time data; According to the temperature difference-time data, the first image data set is converted into a third image data set, and the second-order derivative of the time of the temperature data of the third image data set is taken to obtain a fourth image data set with respect to the second-order derivative; Performing linear gain on data corresponding to each image in the fourth image data set to expand the dynamic range of the fourth image data set to obtain a fifth image data set; Performing enhancement processing on the fifth image data set to obtain an image basic layer and an image detail layer, and amplifying the image detail layer and superimposing it with the image basic layer to obtain a first infrared image; Adjusting the grayscale range of the first infrared image and processing it using a histogram equalization method to obtain a second infrared image; The temperature difference image data set is logarithmically calculated, and a polynomial fitting method based on the least squares method is used to calculate the temperature difference-time data, including: The second image data set is processed at the pixel level for temperature difference based on a polynomial fitting formula to obtain temperature difference-time data. The polynomial fitting formula is: , where N is the number of polynomial fitting times, a n are the coefficients of polynomials of various orders; The method of converting the first image data set into a third image data set according to the temperature difference-time data, and taking the second-order derivative of time of the temperature data of the third image data set to obtain a fourth image data set about the second-order derivative includes: The temperature data of each pixel in the first image data set is converted based on a temperature conversion formula to obtain a third image data set, and the temperature conversion formula is: , where T 0 is the temperature data of a single pixel in the first image data set, T is the temperature data of a single pixel in the third image data, and t is time; The second-order derivative of the temperature data of the third image data set with respect to time is taken based on the second-order derivative formula to obtain a fourth image data set about the second-order derivative, wherein the second-order derivative formula is: , where N is the number of polynomial fitting times, a n are the coefficients of polynomials of various orders.

2. The infrared nondestructive testing sequence image processing method according to claim 1 is characterized in that: The step of performing pixel-level temperature difference processing on the second image data set according to the first image data set to obtain a temperature difference image data set includes: The second image data set is subjected to pixel-level temperature difference processing based on a temperature difference formula to obtain a temperature difference image data set. The temperature difference formula is: ΔT(t) = T(t) - T 0 (t) Where, T 0 is the first image data set, T is the second image data set, and t is time.

3. The infrared nondestructive testing sequence image processing method according to claim 1, characterized in that: The performing linear gain on the data corresponding to each image in the fourth image data set to expand the dynamic range of the fourth image data set to obtain the fifth image data set includes: A linear gain is performed on the grayscale data corresponding to each image in the fourth image data set based on an image linear gain formula, where the image linear gain formula is: , where P is the input image, P 1 is the output gain image, i is the pixel index of the image, P(i) is the grayscale value of the i-th pixel in the input image, P1(i) is the grayscale value of the i-th pixel in the gain image, max(P) is the maximum value of the grayscale data of the input image, and min(P) is the minimum value of the grayscale data of the input image.

4. The infrared nondestructive testing sequence image processing method according to claim 3 is characterized in that: The step of enhancing the fifth image data set to obtain an image base layer and an image detail layer, and amplifying the image detail layer and superimposing the image detail layer with the image base layer to obtain a first infrared image includes: The first image data set is converted into a third image data set based on an image enhancement processing formula, where the image enhancement processing formula is: , where B is Image base layer, D Image detail layer ， i and k are pixel indexes. B (i) is the gray value of the i-th pixel in the basic layer of the image, D(i) is the gray value of the i-th pixel in the detail layer of the image, Ω represents the entire area of ​​the input image, and a is when the center of the window is at pixel i k and b k The mean value of k , b k is the transformation coefficient when the window center is located at pixel k, is the standard deviation of the local window pixel corresponding to pixel point k, N ω is the number of pixels in the local window, α and β is the algorithm adjustment factor, is the gradient of the local window of the input image, j is the gradient number, Represents the local window pixel average corresponding to pixel point k; The image detail layer is enlarged based on an image detail enlargement processing formula and is superimposed on the image basic layer to obtain a first infrared image. The image detail enlargement processing formula is: , where γ and λ are enhancement coefficients, P 2 is the first infrared image output.

5. The infrared nondestructive testing sequence image processing method according to claim 4 is characterized in that: The step of adjusting the grayscale range of the first infrared image and processing the first infrared image using a histogram equalization method to obtain a second infrared image includes: The grayscale range of the first infrared image is adjusted based on an image grayscale adjustment formula, where the image grayscale adjustment formula is: , where P 3 is the image after grayscale processing, P2(i) is the grayscale value of the i-th pixel of the input image, max(P) is the maximum value of the grayscale data of the input image, and min(P) is the minimum value of the grayscale data of the input image; Based on the histogram equalization formula, the image P 3 The second infrared image is obtained by using a histogram equalization method. The histogram equalization formula is: , where n i is the number of gray values ​​i, N is the number of pixels in the image, P 4 is the second infrared image output.

6. An infrared nondestructive testing sequence image processing system, characterized in that: The system includes: An image acquisition module, used to acquire a first image data set and a second image data set of a sample; an image fitting module, configured to perform pixel-level temperature difference processing on the second image data set to obtain a temperature difference image data set, perform logarithm calculation on the temperature difference image data set, and perform calculation using a polynomial fitting method based on a least squares method to obtain temperature difference-time data, convert the first image data set into a third image data set based on the temperature difference-time data, and take a second-order derivative of time for the temperature data of the third image data set to obtain a fourth image data set with respect to the second-order derivative; an image enhancement module, configured to perform linear gain on data corresponding to each image in the fourth image data set to expand the dynamic range of the fourth image data set to obtain a fifth image data set, perform enhancement processing on the fifth image data set to obtain an image basic layer and an image detail layer, and amplify the image detail layer and perform superposition processing on the image basic layer to obtain a first infrared image; An image grayscale processing module, used for adjusting the grayscale range of the first infrared image and processing it using a histogram equalization method to obtain a second infrared image; The temperature difference image data set is logarithmically calculated, and a polynomial fitting method based on the least squares method is used to calculate the temperature difference-time data, including: The second image data set is processed at the pixel level for temperature difference based on a polynomial fitting formula to obtain temperature difference-time data. The polynomial fitting formula is: , where N is the number of polynomial fitting times, a n are the coefficients of polynomials of various orders; The method of converting the first image data set into a third image data set according to the temperature difference-time data, and taking the second-order derivative of time of the temperature data of the third image data set to obtain a fourth image data set about the second-order derivative includes: The temperature data of each pixel in the first image data set is converted based on a temperature conversion formula to obtain a third image data set, and the temperature conversion formula is: , where T 0 is the temperature data of a single pixel in the first image data set, T is the temperature data of a single pixel in the third image data, and t is time; The second-order derivative of the temperature data of the third image data set with respect to time is taken based on the second-order derivative formula to obtain a fourth image data set about the second-order derivative, wherein the second-order derivative formula is: , where N is the number of polynomial fitting times, a n are the coefficients of polynomials of various orders.

7. A computer device comprising a memory and a processor, characterized in that: A computer program is stored in the memory. When the processor runs the computer program, the processor executes the infrared non-destructive testing sequence image processing method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the infrared non-destructive testing sequence image processing method described in any one of claims 1 to 5 are implemented.

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