Method for nondestructive testing of metal corrosion area under anticorrosive coating based on terahertz time-domain spectroscopy

Abstract

The invention discloses a method for nondestructive testing of a metal corrosion area under an anticorrosive coating based on terahertz time-domain spectroscopy, which comprises the following steps: putting a metal substrate corrosion sample with the anticorrosive coating in a terahertz time-domain spectroscopy system, carrying out two-dimensional scanning on the sample in the xy direction to obtain a reflection time-domain waveform signal of each discrete point, carrying out time-domain peak imaging, and determining the metal corrosion area under the anticorrosive coating according to the reflection time-domain waveform signal. The resolution of an image subjected to time domain peak imaging is improved through gray value normalization, morphological corrosion expansion processing and other algorithms, then an adaptive threshold segmentation algorithm is adopted to binarize the image so as to obtain a corrosion region, the interior of the corrosion region segmented based on a threshold is filled, finally, the corrosion region is marked on an original image, and finally, the original image is subjected to time domain peak imaging. And identifying an edge. According to the invention, rapid and lossless detection of the metal corrosion area under the anticorrosive coating can be realized, and the integrity of the original anticorrosive coating is not damaged.

Description

Technical Field

[0001] The invention belongs to the technical field of terahertz time-domain spectroscopy detection technology and computer image processing technology, and in particular refers to a method for nondestructive detection of metal corrosion areas under anti-corrosion coatings based on terahertz time-domain spectroscopy. Background Art

[0002] Terahertz nondestructive testing technology is an emerging detection method that uses electromagnetic waves in the terahertz band to perform non-destructive testing on materials and structures. Terahertz waves are located between microwaves and infrared rays, and have the characteristics of low energy and non-ionizing radiation, so they are harmless to living organisms. It can not only penetrate non-metallic materials, but also analyze the spectral characteristics of different materials to identify material composition and structure. In addition, the short wavelength of terahertz waves gives it a high spatial resolution, which can detect tiny structural defects and material heterogeneity.

[0003] Terahertz time-domain spectroscopy is a technology that uses electromagnetic waves in the terahertz band to analyze materials and substances. The core of terahertz time-domain spectroscopy technology lies in the generation and detection of terahertz pulses. Common methods for generating terahertz pulses include photoconductive antennas and nonlinear optical crystals. When a femtosecond laser pulse is irradiated onto a photoconductive antenna or a nonlinear crystal, an extremely short terahertz pulse is generated. The terahertz pulse after passing through the sample is received by the detector and its time domain signal is recorded. Due to the different absorption and reflection characteristics of different materials in the terahertz band, terahertz time-domain spectroscopy can identify the composition and structure of different materials. In summary, terahertz time-domain spectroscopy technology is suitable for fast, contactless, and lossless detection.

[0004] Traditional metal corrosion detection under anti-corrosion coatings mainly involves manually peeling off the anti-corrosion coating in the corroded area and then visually determining the corroded area. This is not only time-consuming and labor-intensive, but also destroys the integrity of the anti-corrosion coating. Summary of the invention

[0005] The main purpose of the present invention is to provide a method for non-destructive detection of metal corrosion areas under anti-corrosion coatings based on terahertz time-domain spectroscopy, so as to solve the problems existing in the prior art and realize rapid and non-destructive detection of metal corrosion areas under anti-corrosion coatings without destroying the integrity of the original anti-corrosion coatings.

[0006] In order to achieve the above object, the solution of the present invention is: A method for nondestructive detection of metal corrosion areas under anti-corrosion coatings based on terahertz time-domain spectroscopy comprises the following steps: Step 1. Place the metal substrate corrosion sample with anti-corrosion coating on the detection platform of the terahertz time-domain spectroscopy imaging system, set the scanning area and scanning step length of the imaging system on the sample, and obtain the reflection time domain waveform signal of each discrete point on the scanning area; Step 2. Use the peak-finding algorithm to calculate the peak value of the reflected time-domain waveform signal of each discrete point to obtain the peak value of the reflection peak on the corrosion interface and perform time-domain peak imaging; Step 3. Normalize the grayscale value of the time domain peak imaging image to achieve image enhancement, and then optimize the erosion boundary through morphological erosion and dilation to obtain the processed image; Step 4. Calculate the image segmentation threshold parameters for the processed image, perform binary segmentation on the image, and fill the inside of the segmented corrosion area to optimize the result. Finally, mark the identified corrosion area on the grayscale normalized image and mark the edge of the corrosion area.

[0007] The specific steps of step 2 are: Step 2-1. Set the minimum peak height to detect peaks above the threshold; set the minimum peak distance to control the minimum distance between adjacent peaks; for each discrete point, find the peak that meets the requirements based on the minimum peak height and minimum peak distance; Step 2-2. Each peak value found in step 2-1 corresponds to a position index; when only one peak value is found, the peak value is used as the peak value of the current discrete point; when more than one peak value is found, the peak value with the largest amplitude is selected from all detected peak values ​​as the peak value of the current discrete point; Step 2-3. Calculate the peak value of each discrete point in the scanning area according to steps 2-1 and 2-2, and use the calculated peak value as the grayscale value of the corresponding discrete point, and finally generate a time domain peak image of the scanning area.

[0008] The specific steps of step 3 are: Step 3-1. Improve the contrast of the image by grayscale normalization. The formula for grayscale normalization is: ; in, Represents the normalized gray value matrix; Gray value matrix representing the image, the size of which is the number of discrete points in the scan area ; , Represents the gray value matrix The maximum and minimum values ​​in ; Step 3-2. First, perform an erosion operation on the image, and then perform a dilation operation on the eroded image.

[0009] The specific steps of step 4 are: Step 4-1. Segment the corroded area from the image, and use an adaptive threshold segmentation algorithm to binarize the image to obtain the corroded area; Step 4-2. Fill the blank space inside the corrosion area obtained in step 4-1; the filling criterion is to determine whether the blank space is surrounded by the corrosion area, and if so, fill the area; Step 4-3. Mark the corroded area filled in step 4-2 on the image after grayscale normalization in step 3, compare the recognition effect, and mark the edge of the corroded area, which will help to understand the scope and degree of corrosion more accurately.

[0010] Preferably, the specific steps of step 4-1 are: Step 4-1-1. The image processed in step 3 Calculate integral graph , the integral graph is defined as follows: ; in, Represents any point in the original grayscale image The integral value at ; Represents any point in the original grayscale image The gray value at ; , Respectively represent The horizontal and vertical coordinates of the pixel point in the middle have value ranges of , ; Step 4-1-2. After calculating the integral image, perform pixel classification, specifically determine the size The moving window rectangle Pixel value and local threshold on , so that the window size depends on the width of the image ; Local threshold The calculation formula is: ; in, Represents the size of the moving window; , , , ; Step 4-1-3. Calculate the rectangle using the following formula The number of pixels in ; ; Step 4-1-4. Finally, in order to perform image binarization, the adaptive threshold is defined using the following expression: ; in, Represents a preset percentage value; in the image binarization step, if the value of each pixel satisfies the above inequality condition, it is set to black, otherwise it is set to white.

[0011] After adopting the above technical solution, the present invention has the following technical effects: The present invention can perform non-destructive detection of metal corrosion under anti-corrosion coatings in a non-contact, rapid and comprehensive manner; on the one hand, compared with manually peeling off the anti-corrosion coating to determine the corroded area with the naked eye, the detection efficiency is greatly improved while saving human resources; on the other hand, the present invention gives an image segmentation threshold based on grayscale histogramming, while ensuring the accuracy of image segmentation, avoiding subjective bias in human selection, and finally marking the edge of the corroded area, which can help accurately locate the specific position and shape of the corrosion, thereby improving the detection accuracy, which helps engineers and maintenance personnel to more accurately understand the scope and degree of corrosion so as to take more effective repair measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 The figure is a flow chart of the method of the present invention.

[0013] Figure 2 This is the image of time domain peak imaging.

[0014] Figure 3 for Figure 2 Grayscale histogram of .

[0015] Figure 4 is the image after grayscale normalization.

[0016] Figure 5 for Figure 4 Grayscale histogram of .

[0017] Figure 6 This is the image after dilation and erosion.

[0018] Figure 7 is the image after threshold binarization.

[0019] Figure 8 The image to fill the interior of the erosion.

[0020] Fig. 9 A schematic diagram of marking the identification area on the image after grayscale value normalization.

[0021] Fig.10 A schematic diagram of marking the identified edges on an image after grayscale value normalization. DETAILED DESCRIPTION

[0022] In order to further explain the technical solution of the present invention, the present invention is described in detail below through specific embodiments.

[0023] like Figure 1 As shown, the present invention discloses a method for nondestructive detection of metal corrosion areas under anti-corrosion coatings based on terahertz time-domain spectroscopy, comprising the following steps: Step 1. Place the metal substrate corrosion sample with anti-corrosion coating on the detection platform of the terahertz time-domain spectroscopy imaging system, set the scanning area and scanning step length of the imaging system on the sample, and obtain the reflection time domain waveform signal of each discrete point on the scanning area; Step 2. Use the peak-finding algorithm to calculate the peak value of the reflected time-domain waveform signal of each discrete point to obtain the peak value of the reflection peak on the corrosion interface and perform time-domain peak imaging; Step 3. Normalize the grayscale value of the time domain peak imaging image to achieve image enhancement, and then optimize the erosion boundary through morphological erosion and dilation to obtain the processed image; Step 4. Calculate the image segmentation threshold parameters for the processed image, perform binary segmentation on the image, and fill the inside of the segmented corrosion area to optimize the result. Finally, mark the identified corrosion area on the grayscale normalized image and mark the edge of the corrosion area.

[0024] Through the above scheme, the present invention can perform non-destructive detection of metal corrosion under anti-corrosion coating in a non-contact, rapid and comprehensive manner; on the one hand, compared with the manual peeling of anti-corrosion coating to determine the corroded area with the naked eye, it greatly improves the detection efficiency and saves human resources; on the other hand, the present invention gives the image segmentation threshold according to the grayscale histogram, avoids the subjective bias in human selection while ensuring the accuracy of image segmentation, and finally marks the edge of the corroded area, which can help accurately locate the specific position and shape of the corrosion, thereby improving the detection accuracy, which helps engineers and maintenance personnel to understand the scope and degree of corrosion more accurately so as to take more effective repair measures.

[0025] Specific embodiments of the present invention are shown below.

[0026] The specific steps of step 1 above are: Step 1-1. Place the metal substrate corrosion sample with the anti-corrosion coating on the detection platform of the terahertz time-domain spectroscopy imaging system, and stably fix the sample to obtain accurate measurement results; Step 1-2. Set the scanning area of ​​the imaging system on the sample surface according to the experimental requirements. This area can be adjusted according to the degree and distribution of corrosion to ensure that all important areas are covered. At the same time, carefully set the scanning step size to balance resolution and measurement time - a smaller step size can provide higher resolution but requires more time, while a larger step size can speed up the scanning speed but may reduce the clarity of image details. Step 1-3. For each discrete point in the scanning area, obtain the reflection time domain waveform signal data of the point, which provides basic data for subsequent image generation.

[0027] The specific steps of step 2 above are: Step 2-1. Set the minimum peak height to detect peaks above the threshold; set the minimum peak distance to control the minimum distance between adjacent peaks to avoid detecting too dense peaks; for each discrete point, find the peak that meets the requirements based on the minimum peak height and minimum peak distance; Step 2-2. Each peak value found in step 2-1 corresponds to a position index; when only one peak value is found, the peak value is used as the peak value of the current discrete point; when more than one peak value is found, the peak value with the largest amplitude is selected from all detected peak values ​​as the peak value of the current discrete point; Step 2-3. Calculate the peak value of each discrete point in the scanning area according to steps 2-1 and 2-2, and use the calculated peak value as the grayscale value of the corresponding discrete point, and finally generate a time domain peak image of the scanning area, such as Figure 2 , 3 shown.

[0028] The specific steps of step 3 above are: Step 3-1. Improve the contrast of the image by normalizing the grayscale value, so that the difference in reflection characteristics between the corroded area and the uncorroded area is more obvious, such as Figure 4 , 5 As shown, the formula for grayscale value normalization is: ; in, Represents the normalized gray value matrix; Gray value matrix representing the image, the size of which is the number of discrete points in the scan area ; , Represents the gray value matrix The maximum and minimum values ​​in ; Step 3-2. First, perform an erosion operation on the image. The main function of the erosion operation is to remove small noise points in the image, shrink the target area, and make the boundary of the eroded area smoother and more regular. Then, perform an expansion operation on the image after the erosion process. The main function of the expansion operation is to restore the area of ​​the target area and further smooth the boundary, making the edge of the eroded area more obvious and sharper. The combination of these two operations can not only effectively remove the noise in the image, but also highlight the main features of the eroded area. The results are shown in the figure below. Figure 6 shown.

[0029] The specific steps of step 4 above are: Step 4-1. Segment the corroded area from the image, and use an adaptive threshold segmentation algorithm to binarize the image to obtain the corroded area; Specifically, step 4-1 includes the following sub-steps: Step 4-1-1. The image processed in step 3 Calculate integral graph , the integral graph is defined as follows: ; in, Represents any point in the original grayscale image The integral value at ; Represents any point in the original grayscale image The gray value at ; , Respectively represent The horizontal and vertical coordinates of the pixel point in the middle have value ranges of , ; Step 4-1-2. After calculating the integral image, perform pixel classification, specifically determine the size The moving window rectangle Pixel value and local threshold on , so that the window size depends on the width of the image (The maximum possible window width is 1 / 8 of the local threshold The calculation formula is: ; in, Represents the size of the moving window (i.e. width or height, since it is a square, both are equal); , , , ; Step 4-1-3. Calculate the rectangle using the following formula The number of pixels in ; ; Step 4-1-4. Finally, in order to perform image binarization, the adaptive threshold is defined using the following expression: ; in, Represents the preset percentage value, which is generally 50%. The larger the value is, the more dependent it is on the local features of the image. In the image binarization step, if the value of each pixel satisfies the above inequality condition, it is set to black, otherwise it is set to white. Step 4-2. Fill the blank space inside the corrosion area obtained in step 4-1, such as Figure 7 , 8 As shown, the filling standard is to judge whether the blank area is surrounded by the corrosion area. If so, fill this area. The reason for this is that from a long-term perspective, the part surrounded by the corrosion area will be gradually corroded over time, so this part is corroded by default. Step 4-3. Mark the eroded area filled in step 4-2 on the image after grayscale normalization in step 3, as shown in Fig. 9 , 10 As shown, the contrast recognition effect and marking the edge of the corroded area help to understand the scope and extent of corrosion more accurately.

[0030] The above embodiments and drawings do not limit the product form and style of the present invention. Any appropriate changes or modifications made by ordinary technicians in the relevant technical field should be deemed to be within the patent scope of the present invention.

Claims

1. A method for nondestructive detection of metal corrosion areas under anti-corrosion coatings based on terahertz time-domain spectroscopy, characterized in that The following steps are involved: Step 1. Place the metal substrate corrosion sample with anti-corrosion coating on the detection platform of the terahertz time-domain spectroscopy imaging system, set the scanning area and scanning step length of the imaging system on the sample, and obtain the reflection time domain waveform signal of each discrete point on the scanning area; Step 2. Use the peak-finding algorithm to calculate the peak value of the reflected time-domain waveform signal of each discrete point to obtain the peak value of the reflection peak on the corrosion interface and perform time-domain peak imaging; Step 3. Normalize the grayscale value of the time domain peak imaging image to achieve image enhancement, and then optimize the erosion boundary through morphological erosion and dilation to obtain the processed image; Step 4. Calculate the image segmentation threshold parameters for the processed image, perform binary segmentation on the image, and fill the inside of the segmented corrosion area to optimize the result. Finally, mark the identified corrosion area on the grayscale normalized image and mark the edge of the corrosion area.

2. The method for nondestructive detection of metal corrosion areas under anti-corrosion coatings based on terahertz time-domain spectroscopy as claimed in claim 1, characterized in that The specific steps of step 2 are: Step 2-1. Set the minimum peak height to detect peaks above the threshold; set the minimum peak distance to control the minimum distance between adjacent peaks; for each discrete point, find the peak that meets the requirements based on the minimum peak height and minimum peak distance; Step 2-2. Each peak value found in step 2-1 corresponds to a position index; when only one peak value is found, the peak value is used as the peak value of the current discrete point; when more than one peak value is found, the peak value with the largest amplitude is selected from all detected peak values ​​as the peak value of the current discrete point; Step 2-3. Calculate the peak value of each discrete point in the scanning area according to steps 2-1 and 2-2, and use the calculated peak value as the grayscale value of the corresponding discrete point, and finally generate a time domain peak image of the scanning area.

3. The method for nondestructive detection of metal corrosion areas under anti-corrosion coatings based on terahertz time-domain spectroscopy as claimed in claim 1, characterized in that The specific steps of step 3 are: Step 3-1. Improve the contrast of the image by grayscale normalization. The formula for grayscale normalization is: ； in, Represents the normalized gray value matrix; Gray value matrix representing the image, the size of which is the number of discrete points in the scan area ; , Represents the gray value matrix The maximum and minimum values ​​in ; Step 3-2. First, perform an erosion operation on the image, and then perform a dilation operation on the eroded image.

4. The method for nondestructive detection of metal corrosion areas under anti-corrosion coatings based on terahertz time-domain spectroscopy according to claim 1, characterized in that The specific steps of step 4 are: Step 4-1. Segment the corroded area from the image, and use an adaptive threshold segmentation algorithm to binarize the image to obtain the corroded area; Step 4-2. Fill the blank space inside the corrosion area obtained in step 4-1; the filling criterion is to determine whether the blank space is surrounded by the corrosion area, and if so, fill the area; Step 4-3. Mark the corroded area filled in step 4-2 on the image after grayscale normalization in step 3, compare the recognition effect, and mark the edge of the corroded area, which will help to understand the scope and degree of corrosion more accurately.

5. The method for nondestructive detection of metal corrosion areas under anti-corrosion coatings based on terahertz time-domain spectroscopy as claimed in claim 4, characterized in that The specific steps of step 4-1 are: Step 4-1-1. The image processed in step 3 Calculate integral graph , the integral graph is defined as follows: ； in, Represents any point in the original grayscale image The integral value at ; Represents the original grayscale image at any point The gray value at ; , Respectively represent The horizontal and vertical coordinates of the pixel point in the middle have value ranges of , ; Step 4-1-2. After calculating the integral image, perform pixel classification, specifically determine the size The moving window rectangle Pixel value and local threshold on , so that the window size depends on the width of the image ; Local threshold The calculation formula is: ； in, Represents the size of the moving window; , , , ; Step 4-1-3. Calculate the rectangle using the following formula The number of pixels in ; ； Step 4-1-4. Finally, in order to perform image binarization, the adaptive threshold is defined using the following expression: ； in, Represents a preset percentage value; in the image binarization step, if the value of each pixel satisfies the above inequality condition, it is set to black, otherwise it is set to white.

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