Early corrosion detection method based on eddy current thermal imaging and optical flow-PCA (Principal Component Analysis)

Abstract

The invention discloses an early corrosion detection method based on eddy current thermal imaging and optical flow-PCA, and relates to the technical field of corrosion detection.The early corrosion detection method comprises the following steps that temperature response curves of a corrosion area and a non-corrosion area of an early corrosion sample are analyzed, and a time frame interval with the maximum relative tangent slope in the temperature response curves is selected; acquiring an optical flow amplitude image sequence of continuous frames, calculating an optical flow amplitude difference based on the optical flow amplitude image sequence, and evaluating the corrosion degree of the early corrosion sample; reconstruction processing is carried out on an optical flow amplitude image sequence of continuous frames, feature extraction is carried out on a reconstruction matrix by utilizing a principal component analysis method, and weighted reconstruction is carried out on principal component features to obtain heat flow features of a corrosion area. According to the invention, the fine difference between the corrosion area and the non-corrosion area in the heat flow direction and strength can be accurately reflected, and the sensitivity of an early corrosion signal is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of corrosion detection, and in particular to an early corrosion detection method based on eddy current thermal imaging and optical flow-PCA. Background Art

[0002] Pulsed eddy current thermal imaging technology actively excites and heats the surface of conductive materials, and collects infrared image sequences to obtain corrosion information of the materials. However, in early corrosion detection, due to the small changes in the thermal conductivity characteristics of the corrosion area, the local temperature difference is not obvious, and the contrast between the corrosion area and the non-corrosion area in the infrared image is low, making it difficult to effectively identify the early corrosion area. In addition, the infrared thermal image sequence is easily disturbed by factors such as environmental noise, heating unevenness, and background heat reflection, which further reduces the accuracy of feature extraction of early corrosion areas. On the other hand, existing data processing methods are insufficiently sensitive in capturing small dynamic heat flow changes in time series, and cannot effectively characterize the heat flow distribution characteristics of the corrosion area, further limiting the accurate identification and visualization of early corrosion areas.

[0003] The existing fast Fourier transform (FFT) method has obvious limitations in early corrosion detection, which are mainly reflected in the following three aspects:

[0004] First, the FFT method ignores the dynamic heat flow characteristics of the temperature field and cannot accurately capture early corrosion signals. Theoretically, FFT extracts amplitude-frequency and phase-frequency characteristics by performing static frequency domain decomposition on the infrared image sequence, but it cannot characterize the changes in heat flow direction and intensity between the corroded and non-corroded areas during the time evolution process. In practical applications, early corrosion signals are manifested as weak temperature distribution and insignificant dynamic response. Especially in the heating and cooling stages, there is a time delay and local conduction blockage in the heat diffusion of the corroded area. This dynamic heat flow feature is particularly critical in the time domain, while FFT can only extract global frequency information and ignore the time correlation of the temperature response. In addition, due to the uneven distribution of heat flow caused by changes in thermal conductivity and resistivity in the corrosion area, the amplitude and phase characteristics extracted by FFT are difficult to effectively reflect these subtle changes, resulting in a low contrast between the corrosion area and the background, and the corrosion signal is easily masked, which ultimately affects the detection sensitivity and visualization effect.

[0005] Secondly, the FFT method is extremely sensitive to noise, which seriously affects the accuracy and stability of detection. In infrared thermal image sequences, environmental noise, background heat reflection, and heating unevenness are the main sources of interference. Especially in the early stage of corrosion, the temperature change amplitude of the corrosion area is small, the thermal response is weak, and the real signal is easily superimposed with the noise. In the frequency domain, FFT lacks an effective separation mechanism for these noise components, and the noise is easily amplified, thereby obscuring the true characteristics of the corrosion area. Specifically, the signal-to-noise ratio (SNR) is reduced, the feature contrast between the corrosion area and the non-corrosion area is insufficient, false signals or feature loss appear in the reconstructed image, and the boundaries and morphology are difficult to present clearly. Therefore, noise interference makes it difficult for the FFT method to stably extract key information of the corrosion area, limiting its actual detection accuracy.

[0006] Finally, the FFT method lacks an objective basis for frequency selection, which further reduces the feasibility of practical operation. Although FFT can extract the amplitude and phase characteristics of the temperature field signal, in practical applications, infrared image sequences usually contain multiple frequency components, and different frequencies have different response effects on the corrosion area. Therefore, in order to obtain the best detection effect, it is necessary to select the optimal frequency for image reconstruction. However, FFT currently lacks a unified frequency selection standard and usually relies on subjective experience or repeated experiments, making it difficult to ensure the objectivity and consistency of the reconstruction results. In addition, the differences in corrosion degree, thickness and material properties of different samples make the selection of the optimal frequency component more complicated and unstable, further increasing the difficulty of operation and application cost. This leads to the unstable effect of the FFT method in practical applications, and the reconstructed corrosion area image cannot fully and accurately reflect the real characteristics, reducing the reliability of early corrosion detection.

[0007] According to the above analysis, the existing FFT method ignores dynamic heat flow characteristics, is sensitive to noise, and lacks objective basis for frequency selection. These technical defects cause the FFT method to show the shortcomings of low sensitivity, low signal-to-noise ratio, and high complexity in early corrosion detection, which limits its reliability and feasibility in practical applications and cannot effectively meet the needs of weak signal extraction and feature visualization in early corrosion detection.

[0008] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0009] In view of this, the present invention provides an early corrosion detection method based on eddy current thermal imaging and optical flow-PCA to solve the above-mentioned problems.

[0010] In order to solve the above problems, the specific technical solutions adopted by the present invention are as follows:

[0011] An early corrosion detection method based on eddy current thermal imaging and optical flow-PCA, the method comprising the following steps:

[0012] S1. Based on the infrared thermal image sequence of the early corrosion sample acquired in advance, the temperature response curves of the corrosion area and the non-corrosion area of ​​the early corrosion sample are analyzed, and the time frame interval with the largest relative tangent slope in the temperature response curve is selected;

[0013] S2. Based on the Lucas-Kanade optical flow method, the optical flow is calculated for the selected time frame interval, and the optical flow amplitude matrix is ​​constructed according to the calculation results; according to the optical flow amplitude matrix, the optical flow amplitude image sequence of continuous frames is obtained, and the optical flow amplitude difference is calculated based on the optical flow amplitude image sequence to evaluate the corrosion degree of the early corrosion sample;

[0014] S3. Reconstruct the optical flow amplitude image sequence of continuous frames to obtain a reconstruction matrix, and use the principal component analysis method to extract features of the reconstruction matrix to obtain principal component features. By weighted reconstruction of the principal component features, the heat flow features of the corrosion area are obtained.

[0015] Preferably, the step of analyzing the temperature response curves of the corrosion area and the non-corrosion area of ​​the early corrosion sample based on the infrared thermal image sequence of the early corrosion sample acquired in advance, and selecting the time frame interval with the largest relative tangent slope in the temperature response curve comprises the following steps:

[0016] S11, obtaining an infrared thermal image sequence of an early corrosion sample pre-detected using eddy current pulse thermal imaging technology, and recording the temperature changes of the corrosion area and the non-corrosion area of ​​the early corrosion sample during the heating and cooling process;

[0017] S12. Based on the temperature changes of the corrosion area and the non-corrosion area of ​​the early corrosion sample during the heating and cooling process, the time frame interval with the largest relative tangent slope is selected by analyzing the temperature change curves of the corrosion area and the non-corrosion area.

[0018] Preferably, the Lucas-Kanade optical flow method is used to perform optical flow calculation on the selected time frame interval, and an optical flow amplitude matrix is ​​constructed according to the calculation results; an optical flow amplitude image sequence of continuous frames is obtained according to the optical flow amplitude matrix, and the optical flow amplitude difference is calculated based on the optical flow amplitude image sequence, and the corrosion degree of the early corrosion sample is evaluated, which includes the following steps:

[0019] S21, using the Lucas-Kanade optical flow method, performing pixel displacement calculation on adjacent frames within the selected time frame interval, and obtaining an optical flow component of each pixel according to the calculation result;

[0020] S22, calculating the optical flow amplitude according to the optical flow component of each pixel using the optical flow amplitude calculation formula;

[0021] S23, normalizing the optical flow amplitude by using a normalization method to obtain a normalized optical flow amplitude, and constructing an optical flow amplitude matrix according to the normalized optical flow amplitude;

[0022] S24, converting the normalized optical flow amplitude into a color image through a Jet color mapping scheme to obtain an optical flow amplitude image sequence of continuous frames;

[0023] S25. According to the normalized optical flow amplitude, by selecting the optical flow amplitudes of the corroded area and the non-corroded area, and calculating the average values ​​of the optical flow amplitudes of the corroded area and the non-corroded area respectively, the corrosion degree of the early corrosion sample is evaluated according to the difference between the average values ​​of the optical flow amplitudes of the corroded area and the non-corroded area.

[0024] Preferably, the Lucas-Kanade optical flow method is used to calculate the pixel displacement of adjacent frames within the selected time frame interval, and obtaining the optical flow component of each pixel according to the calculation result includes the following steps:

[0025] S211, according to the pixel gray value at each position in the selected time frame interval, by setting the gray value change of the pixel in the preset interval to be negligible, a gray value conservation formula is obtained;

[0026] S212, by performing Taylor expansion on the gray value conservation formula, a linear approximation formula including pixel displacement is obtained; and the optical flow constraint equation is obtained in combination with the gray value conservation formula;

[0027] S213. Construct an overdetermined set of equations according to the optical flow constraint equation, and use the least squares method to solve the overdetermined set of equations to obtain the optical flow component of each pixel.

[0028] Preferably, the optical flow amplitude calculation formula is:

[0029]

[0030] Where F represents the optical flow amplitude, u represents the optical flow component of the pixel in the x-axis direction, and v represents the optical flow component of the pixel in the y-axis direction.

[0031] Preferably, the calculation formula for normalizing the optical flow amplitude using the normalization method is:

[0032]

[0033] In the formula, F norm represents the normalized optical flow amplitude, F min Indicates the minimum value of the optical flow amplitude, F max Indicates the maximum value of the optical flow amplitude.

[0034] Preferably, the process of reconstructing the optical flow amplitude image sequence of continuous frames to obtain a reconstruction matrix, extracting features from the reconstruction matrix using a principal component analysis method to obtain principal component features, and performing weighted reconstruction on the principal component features to obtain the heat flow features of the corrosion area comprises the following steps:

[0035] S31, reconstructing the optical flow amplitude image sequence of continuous frames into a two-dimensional matrix, and determining the mean matrix of the row vectors of the two-dimensional matrix;

[0036] S32, constructing an autocovariance matrix of the two-dimensional matrix according to the mean matrix of the row vectors of the two-dimensional matrix and the two-dimensional matrix;

[0037] S33, decomposing the covariance matrix by singular value decomposition method to obtain an eigenvector matrix and an eigenvalue matrix;

[0038] S34. Sort the eigenvectors according to the eigenvalue matrix, select several principal components, and use the several principal components to perform image weighted reconstruction to obtain the heat flow characteristics of the corrosion area.

[0039] Preferably, the calculation formula for constructing the autocovariance matrix of the two-dimensional matrix based on the mean matrix of the row vectors of the two-dimensional matrix and the two-dimensional matrix is:

[0040] C=(RR mean )(RR mean ) T

[0041] In the formula, C represents the covariance matrix, R mean is the mean matrix of the row vectors of the two-dimensional matrix R, where R represents a two-dimensional matrix and T represents a transposed matrix.

[0042] Preferably, the calculation formula for decomposing the covariance matrix by singular value decomposition is:

[0043] C=QΛQ T

[0044] Where C represents the covariance matrix, Q is a matrix consisting of the eigenvectors of the covariance matrix, Λ is the corresponding diagonal eigenvalue matrix, and T represents the transposed matrix.

[0045] Preferably, the calculation formula for obtaining the heat flow characteristics of the corrosion area by performing weighted image reconstruction using several principal components is:

[0046]

[0047]

[0048] In the formula, represents the reconstructed data matrix, represents the reconstruction vector, μ represents the mean vector of the data, y i represents the coefficient of the i-th principal component, w i represents the eigenvector corresponding to the i-th principal component, k represents the number of principal components, N x Indicates the width of the image, N y Indicates the height of the image.

[0049] Compared with the prior art, the present invention provides an early corrosion detection method based on eddy current thermal imaging and optical flow-PCA, which has the following beneficial effects:

[0050] (1) The present invention uses the LK optical flow method to capture the dynamic motion information of pixel points in the infrared image sequence, and converts the temporal and spatial changes of the temperature field into the optical flow amplitude and direction characteristics, thereby making up for the deficiency of the FFT method in ignoring the dynamic evolution characteristics of temperature. This process can accurately reflect the subtle differences in heat flow direction and intensity between the corrosion area and the non-corrosion area, greatly improving the sensitivity of the early corrosion signal.

[0051] (2) The present invention calculates the temperature difference between the corrosion area and the non-corrosion area and selects the key frame with the most significant change in optical flow value for analysis. This adaptive frame selection mechanism effectively reduces the interference of background noise and heating unevenness and improves the stability and accuracy of feature extraction.

[0052] (3) The present invention combines PCA dimensionality reduction and reconstruction, extracts the main components of the optical flow field, performs weighted reconstruction on the corrosion image, highlights the heat flow characteristics of the corrosion area, and suppresses the redundant information of the background area, significantly enhancing the contrast and boundary clarity between the corrosion area and the background area. This method avoids the subjectivity of FFT frequency selection and ensures the objectivity and consistency of the reconstruction results.

[0053] (4) The present invention combines the optical flow method with PCA, breaking through the limitations of the existing technology in dynamic feature capture, noise resistance and visualization effect, and can achieve accurate positioning of early corrosion areas and corrosion degree assessment with high sensitivity and low noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 is a flow chart of an early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to an embodiment of the present invention;

[0056] Figure 2 is a structural block diagram of a pulsed eddy current thermal imaging system in an early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to an embodiment of the present invention;

[0057] Figure 3 is an amplitude / phase image of a specific frequency in an early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to an embodiment of the present invention;

[0058] Figure 4 is a thermal response diagram of different regions in an early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to an embodiment of the present invention;

[0059] Figure 5 is a temperature curve diagram of different pixel points on the sample surface in the early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to an embodiment of the present invention;

[0060] Figure 6 is a thermal difference map between a non-corroded area and a corroded area in an early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to an embodiment of the present invention;

[0061] Figure 7 It is a flow chart of extracting dynamic heat flow features in infrared image sequences using the Lucas-Kanade optical flow method in an early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to an embodiment of the present invention;

[0062] Figure 8 is a flow chart of a PCA feature extraction process in an early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to an embodiment of the present invention;

[0063] Fig. 9 It is the best effect diagram in the visualization enhancement of the corrosion area on the 49th day in the early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to an embodiment of the present invention;

[0064] Fig.10 This is the best effect diagram in the visualization enhancement of the corrosion area on the 66th day in the early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to an embodiment of the present invention. DETAILED DESCRIPTION

[0065] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0066] According to an embodiment of the present invention, an early corrosion detection method based on eddy current thermal imaging and optical flow-PCA is provided.

[0067] The present invention adopts pulsed eddy current thermal imaging (ECPT, Eddy Current Pulsed Thermalgrphy) technology, and the system structure is as follows Figure 2 As shown in the figure. In this system, a high-frequency pulse current is applied to the sample surface through an induction coil, which induces eddy currents and generates Joule heat inside the material to achieve heating of the sample surface. After the heating process is completed, the sample is cooled in a natural environment, and the dynamic changes of its surface temperature are recorded in real time by the infrared thermal imager as a thermal image sequence and transmitted to the computer for subsequent processing. The computer extracts parameters related to corrosion characteristics by applying specific image processing algorithms, and then completes the image reconstruction of the corrosion area and the quantitative evaluation of the corrosion degree.

[0068] At present, the main algorithm for corrosion detection using pulsed eddy current thermal imaging technology is the fast Fourier transform method.

[0069] The steps of the fast Fourier transform method can be divided into:

[0070] ①Fourier transform;

[0071] The infrared image sequence is regarded as a sampling sequence of temperature changes over time. The temperature data of each pixel in the image is subjected to fast Fourier transform (FFT) and converted to the frequency domain for analysis.

[0072]

[0073] Where, T k Indicates the temperature value of a pixel in the infrared image at the kth frame. is a complex exponential factor, a rotation factor in Fourier transform, used to convert time domain signals to frequency domain, N represents the number of frames in the image sequence, F n Represents the complex frequency component after Fourier transform, Re n and Im n Respectively represent F n Get the real and imaginary parts.

[0074] ② Calculate the amplitude-frequency characteristics and phase-frequency characteristics;

[0075] The Fourier transform result F n By analyzing, we can get the amplitude-frequency characteristics and phase-frequency characteristics in the frequency domain:

[0076]

[0077] In the formula, A nRepresents the amplitude-frequency characteristics of the pixel temperature sequence, that is, the amplitude of different frequency components; φ n It represents the phase-frequency characteristic, that is, the phase information of the frequency component.

[0078] ③Draw the amplitude / phase image of a specific frequency;

[0079] Based on the calculated amplitude-frequency characteristics and phase-frequency characteristics, a specific frequency component is selected and its amplitude image and phase image are plotted to characterize the distribution range of the corrosion area, such as Figure 3 As shown, 3Hz corresponds to the amplitude image, (a) corrosion 21 days, (b) corrosion 35 days; 3Hz corresponds to the phase image, (c) corrosion 21 days, (d) corrosion 35 days.

[0080] Amplitude image: used to highlight the thermal response intensity of the corrosion area and reflect the significance of the corrosion characteristics.

[0081] Phase image: used to distinguish the thermal diffusion delay between the corroded area and the non-corroded area, and further confirm the location and scope of corrosion.

[0082] In view of the shortcomings of the existing fast Fourier transform (FFT) method, the present invention proposes an infrared thermal image processing method based on the Lucas-Kanade optical flow method combined with principal component analysis (LK-OF-PCA). This method comprehensively considers the dynamic changes of the temperature field and the characteristics of the heat flow direction of the material surface under the external eddy current excitation, captures the dynamic information of the heat flow movement between pixels through the optical flow method, and combines PCA to reduce the dimension and reconstruct the feature data, and proposes a high-sensitivity and high-contrast early corrosion area image reconstruction method and corrosion degree assessment strategy.

[0083] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to an early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to an embodiment of the present invention, the method comprises the following steps:

[0084] S1. Based on the infrared thermal image sequence of the early corrosion sample acquired in advance, the temperature response curves of the corrosion area and the non-corrosion area of ​​the early corrosion sample are analyzed, and the time frame interval with the largest relative tangent slope in the temperature response curve is selected;

[0085] As a preferred embodiment, the infrared thermal image sequence of the early corrosion sample acquired in advance, analyzing the temperature response curves of the corrosion area and the non-corrosion area of ​​the early corrosion sample, and selecting the time frame interval with the largest relative tangent slope in the temperature response curve includes the following steps:

[0086] S11, obtaining an infrared thermal image sequence of an early corrosion sample pre-detected using eddy current pulse thermal imaging technology, and recording the temperature changes of the corrosion area and the non-corrosion area of ​​the early corrosion sample during the heating and cooling process;

[0087] S12. Based on the temperature changes of the corrosion area and the non-corrosion area of ​​the early corrosion sample during the heating and cooling process, the time frame interval with the largest relative tangent slope is selected by analyzing the temperature change curves of the corrosion area and the non-corrosion area.

[0088] S2. Based on the Lucas-Kanade optical flow method, optical flow calculation is performed on the selected time frame interval, and an optical flow amplitude matrix is ​​constructed according to the calculation results; according to the optical flow amplitude matrix, an optical flow amplitude image sequence of continuous frames is obtained, and the optical flow amplitude difference is calculated based on the optical flow amplitude image sequence to evaluate the corrosion degree of the early corrosion sample;

[0089] As a preferred implementation, the optical flow calculation is performed on the selected time frame interval based on the Lucas-Kanade optical flow method, and an optical flow amplitude matrix is ​​constructed according to the calculation results; an optical flow amplitude image sequence of continuous frames is obtained according to the optical flow amplitude matrix, and the optical flow amplitude difference is calculated based on the optical flow amplitude image sequence, and the corrosion degree of the early corrosion sample is evaluated, including the following steps:

[0090] S21, using the Lucas-Kanade optical flow method, performing pixel displacement calculation on adjacent frames within the selected time frame interval, and obtaining an optical flow component of each pixel according to the calculation result;

[0091] As a preferred implementation, the Lucas-Kanade optical flow method is used to calculate the pixel displacement of adjacent frames within the selected time frame interval, and the optical flow component of each pixel is obtained according to the calculation result, which includes the following steps:

[0092] S211, according to the pixel gray value at each position in the selected time frame interval, by setting the gray value change of the pixel in the preset interval to be negligible, a gray value conservation formula is obtained;

[0093] S212, by performing Taylor expansion on the gray value conservation formula, a linear approximation formula including pixel displacement is obtained; and the optical flow constraint equation is obtained in combination with the gray value conservation formula;

[0094] S213. Construct an overdetermined set of equations according to the optical flow constraint equation, and use the least squares method to solve the overdetermined set of equations to obtain the optical flow component of each pixel.

[0095] S22, calculating the optical flow amplitude according to the optical flow component of each pixel using the optical flow amplitude calculation formula;

[0096] As a preferred implementation, the optical flow amplitude calculation formula is:

[0097]

[0098] Where F represents the optical flow amplitude, u represents the optical flow component of the pixel in the x-axis direction, and v represents the optical flow component of the pixel in the y-axis direction.

[0099] S23, normalizing the optical flow amplitude by using a normalization method to obtain a normalized optical flow amplitude, and constructing an optical flow amplitude matrix according to the normalized optical flow amplitude;

[0100] As a preferred implementation, the calculation formula for normalizing the optical flow amplitude using the normalization method is:

[0101]

[0102] In the formula, F norm represents the normalized optical flow amplitude, F min Indicates the minimum value of the optical flow amplitude, F max Indicates the maximum value of the optical flow amplitude.

[0103] S24, converting the normalized optical flow amplitude into a color image through a Jet color mapping scheme to obtain an optical flow amplitude image sequence of continuous frames;

[0104] S25. According to the normalized optical flow amplitude, by selecting the optical flow amplitudes of the corroded area and the non-corroded area, and calculating the average values ​​of the optical flow amplitudes of the corroded area and the non-corroded area respectively, the corrosion degree of the early corrosion sample is evaluated according to the difference between the average values ​​of the optical flow amplitudes of the corroded area and the non-corroded area.

[0105] S3. Reconstruct the optical flow amplitude image sequence of continuous frames to obtain a reconstruction matrix, and use the principal component analysis method to extract features of the reconstruction matrix to obtain principal component features. By weighted reconstruction of the principal component features, the heat flow features of the corrosion area are obtained.

[0106] As a preferred implementation, the process of reconstructing the optical flow amplitude image sequence of continuous frames to obtain a reconstruction matrix, extracting features from the reconstruction matrix using a principal component analysis method to obtain principal component features, and performing weighted reconstruction on the principal component features to obtain heat flow features of the corrosion area includes the following steps:

[0107] S31, reconstructing the optical flow amplitude image sequence of continuous frames into a two-dimensional matrix, and determining the mean matrix of the row vectors of the two-dimensional matrix;

[0108] S32, constructing an autocovariance matrix of the two-dimensional matrix according to the mean matrix of the row vectors of the two-dimensional matrix and the two-dimensional matrix;

[0109] S33, decomposing the covariance matrix by singular value decomposition method to obtain an eigenvector matrix and an eigenvalue matrix;

[0110] S34. Sort the eigenvectors according to the eigenvalue matrix, select several principal components, and use the several principal components to perform image weighted reconstruction to obtain the heat flow characteristics of the corrosion area.

[0111] As a preferred implementation, the calculation formula for constructing the autocovariance matrix of the two-dimensional matrix based on the mean matrix of the row vectors of the two-dimensional matrix and the two-dimensional matrix is:

[0112] C=(RR mean )(RR mean ) T

[0113] In the formula, C represents the covariance matrix, R mean is the mean matrix of the row vectors of the two-dimensional matrix R, where R represents a two-dimensional matrix and T represents a transposed matrix.

[0114] As a preferred implementation, the calculation formula for decomposing the covariance matrix by singular value decomposition is:

[0115] C=QΛQ T

[0116] Where C represents the covariance matrix, Q is a matrix consisting of the eigenvectors of the covariance matrix, Λ is the corresponding diagonal eigenvalue matrix, and T represents the transposed matrix.

[0117] As a preferred implementation, the calculation formula for obtaining the heat flow characteristics of the corrosion area by using several principal components for weighted image reconstruction is:

[0118]

[0119]

[0120] In the formula, represents the reconstructed data matrix, represents the reconstruction vector, μ represents the mean vector of the data, y i represents the coefficient of the i-th principal component, w i represents the eigenvector corresponding to the i-th principal component, k represents the number of principal components, N x Indicates the width of the image, N y Indicates the height of the image.

[0121] In order to better understand the above technical solutions of the present invention, the above technical solutions of the present invention are further described below through specific implementation methods.

[0122] First of all, from the perspective of the ECPT technical principle, the function generator under computer control outputs a control signal to enable the infrared thermal imager and the induction heater to work together. The induction heater receives the signal from the function generator, generates a high-frequency alternating current, and transmits it to the induction coil. Due to the electromagnetic induction effect, eddy currents will be generated in the sample. According to Joule's law, when eddy currents pass through conductive materials, due to the resistance of the material, part of the electrical energy will be converted into heat energy, which is specifically manifested as Joule heat Q. The generation process can be expressed by the Joule heat generation formula, which is:

[0123]

[0124] In the formula, J s represents eddy current density, E represents electric field strength, and σ represents the electrical conductivity of the material. The heat generated by eddy current will continue to diffuse inside the sample. This heat diffusion process can be described by the heat diffusion formula, which is:

[0125]

[0126] Where z is the depth from the surface, T(z,t) is the temperature at time t at position z, ρ is the material density, and C p is the heat capacity, and k is the thermal conductivity of the material. For the early corrosion area, the eddy current density distribution in the sample will change non-uniformly. Since the local corrosion of the material causes changes in the electrical conductivity and thermal conductivity, the eddy currents are more likely to concentrate in the corrosion area. The resistance of the corrosion area increases, and more Joule heat is generated, causing the temperature rise rate in the corrosion area to be significantly faster than that in the uncorroded area, thereby forming a high-temperature area in the infrared thermal image. Subsequently, the induction coil stops heating and the sample enters the natural cooling stage. In this process, the heat diffusion in the corrosion area is affected, and the material structure inhomogeneity caused by local corrosion will hinder the conduction of heat, resulting in a difference in the temperature reduction rate in the corrosion area and the uncorroded area, which ultimately manifests as abnormal temperature distribution characteristics in the infrared thermal image.

[0127] The infrared thermal imager can capture the temperature difference between the early corrosion area and the non-corroded area and store it in the computer in the form of a thermal image sequence for subsequent feature extraction and corrosion degree analysis.

[0128] According to the above technical principle, since the temperature response curve of the early corrosion area will show obvious dynamic change characteristics during the heating and cooling stages, in order to more effectively capture this difference, the present invention selects the time interval with the most significant temperature change as the solution window.

[0129] In order to select the frame with the largest relative temperature change difference to optimize the optical flow estimation effect, such as Figure 4-5 As shown in Figure 2, the temperature difference between area A and area B is calculated and obtained as Figure 6 The curve shown in the figure. As can be seen from the figure, during the heating stage, the maximum temperature difference between areas A and B reaches 2.9°C, then drops rapidly and remains at a stable value of 0.78°C during the cooling stage. Therefore, based on the significance of the temperature difference change, the study selected time period II (10-60 frames) as the optimal time period for optical flow analysis, when the temperature change is most obvious and the optical flow algorithm is most effective. This selection mechanism effectively reduces the interference of background thermal reflection and random noise, and reduces the impact of noise and heating unevenness.

[0130] Specifically, to characterize the temperature change characteristics of each point in the infrared image, such as Figure 7 As shown, the present invention chooses to use the Lucas-Kanade optical flow method to extract dynamic heat flow features in infrared image sequences. Specifically, optical flow refers to the velocity field formed by the change of pixel position of a moving object on the observation plane in an image sequence. By analyzing the change of pixel brightness over time in the image and the association between adjacent frames, the optical flow method can find the correspondence between the previous frame and the current frame, thereby calculating the motion information of the object. In a thermal image sequence, temperature distribution is closely related to thermal diffusion and heat flow inside the material, and this thermal flow characteristic can be characterized by a pixel motion field (i.e., an optical flow field). Therefore, optical flow can be described as the movement of heat flow between two adjacent frames of thermal images, and the changing law of heat flow distribution inside the sample can be analyzed by calculating optical flow. Based on optical flow analysis, visualization and quantification of heat flow distribution in space and time dimensions can be achieved, which can then be used to reveal damage characteristics and thermal response behaviors inside the material.

[0131] In the LK optical flow method, it is assumed that at time t, the pixel at position (x, y) has a grayscale value I(x, y, t). After a time interval Δt, the pixel is displaced (Δx, Δy) in the next frame. If it is assumed that the change in the grayscale value of the pixel during this process can be ignored, the grayscale conservation formula can be obtained. Taylor expansion of the grayscale conservation formula can be obtained to obtain a linear approximation formula containing pixel displacement. The grayscale conservation formula and the linear approximation formula containing pixel displacement are:

[0132] I(x,y,t)=I(x+Δx,y+Δy,t+Δt)

[0133]

[0134] After ignoring high-order infinitesimals, the most basic optical flow constraint equation can be derived from the gray value conservation formula and the linear approximation formula including pixel displacement:

[0135]

[0136] make The most basic optical flow constraint equation can be expressed as follows:

[0137] I x u+I y v+I t =0

[0138] In the formula, u and v represent the moving speed in two directions (x-axis and y-axis), I x , I y , I t Represents the partial derivative (gradient) of brightness on three axes. Because it involves two unknowns, u and v, additional constraints are required for the optical flow. In the Lucas-Kanade optical flow method, it is assumed that the optical flow is a constant in the neighborhood of the pixel point, and the optical flow is solved by minimizing the error in the local area.

[0139] In a window containing N pixels (for example, an n×n window), the following overdetermined system of equations can be constructed:

[0140]

[0141] In the formula, I xi , I yi , I ti is the spatial and temporal gradient of the ith pixel, and the system of equations is expressed in matrix form:

[0142]

[0143] Where A is an N×2 matrix, b is an N×1 vector, where I ti Represents the time gradient of the i-th pixel, representing the gradient change of the brightness of the pixel in the image sequence over time. Using the least squares method, the goal is to minimize the error function E(u,v):

[0144]

[0145] Taking partial derivatives of u and v and setting the error function to zero, we get the equation:

[0146]

[0147] The optical flow components can be solved:

[0148]

[0149] After the calculation is completed, the optical flow components u(x, y) and v(x, y) of each pixel can be obtained.

[0150] The optical flow in the thermal image can be used as a mapping of the heat flow, reflecting the direction and speed of heat diffusion. The amplitude F of the optical flow can represent the intensity of the heat flow movement, and the optical flow amplitude can be calculated using the optical flow amplitude calculation formula.

[0151] The formula for calculating the optical flow amplitude is:

[0152]

[0153] Where F represents the optical flow amplitude, u represents the optical flow component of the pixel in the x-axis direction, and v represents the optical flow component of the pixel in the y-axis direction.

[0154] In order to map the optical flow amplitude into the range of the visualized image, the optical flow amplitude is normalized by using the normalization method to obtain the normalized optical flow amplitude. The calculation formula for normalizing the optical flow amplitude by using the normalization method is:

[0155]

[0156] In the formula, F norm represents the normalized optical flow amplitude, F min Indicates the minimum value of the optical flow amplitude, F max It represents the maximum value of the optical flow amplitude. Normalization maps the amplitude to the interval [0,1], and then converts the normalized optical flow amplitude into a color image through the Jet color mapping scheme to make the intensity distribution of the optical flow clearer. The present invention uses the Lucas-Kanade optical flow method to capture the pixel displacement between adjacent frames, quantitatively characterizes the dynamic heat flow change characteristics between the corrosion area and the non-corrosion area, and effectively improves the sensitivity to the tiny heat flow characteristics of the early corrosion area.

[0157] Furthermore, in order to quantitatively evaluate the early corrosion degree, the optical flow amplitudes of the corroded area and the non-corroded area are selected, and the average optical flow amplitudes of the corroded area and the non-corroded area are calculated respectively. and The corrosion degree of the early corrosion samples was evaluated based on the difference in the average optical flow amplitude between the corrosion area and the non-corrosion area.

[0158]

[0159] Where ΔF represents the difference between the average optical flow amplitudes of the corroded area and the non-corroded area.

[0160] The optical flow method is used to calculate the average optical flow amplitude of the early corrosion area and the non-corrosion area, and the difference is obtained. This can effectively capture the dynamic heat flow anomaly characteristics caused by the initial changes in physical properties, thereby achieving a quantitative assessment of the degree of early corrosion.

[0161] The physical properties of materials will undergo subtle but significant changes during the early corrosion process, and this change directly affects the conduction characteristics of heat flow inside the material. Specifically, in the early corrosion stage, the thermal conductivity, resistivity and heat capacity of the corrosion area gradually change. This is because the initial corrosion products (such as iron oxide, ferrous oxide, etc.) begin to form on the surface of the material, resulting in local inhomogeneity in the microstructure and composition of the material. The thermal conductivity of the early corrosion area begins to show a slight downward trend, because the corrosion products generated in the early stage have lower thermal conductivity than the metal material body, which locally hinders the diffusion of heat flow; at the same time, the resistivity begins to increase, and due to the insulation or semiconductor properties of the corrosion products, the local accumulation of Joule heat under the eddy current effect is more obvious. Although these initial physical property changes are relatively weak, they still cause slight delay effects and blocking phenomena in the heat flow conduction process, which is manifested as a slight slowdown in the diffusion rate of heat flow and an increase in local temperature. This subtle heat flow anomaly makes the optical flow amplitude in the early corrosion area slightly higher than that in the non-corrosion area, forming a detectable characteristic difference, providing a scientific basis and technical support for the high-sensitivity identification and quantitative evaluation of early corrosion.

[0162] Finally, in order to extract the corrosion information from the optical flow magnitude image sequence, PCA is applied to the optical flow magnitude, which involves mapping the data of the entire physical process from a high-dimensional space to a low-dimensional space, with the aim of maximizing the variance of the projection data. The optical flow magnitude sequence can be represented as a three-dimensional matrix n x ×n y ×N. In order to obtain a two-dimensional matrix that can be processed by PCA, the optical flow amplitude sequence is reconstructed to generate a new matrix R, such as Figure 8 As shown. The new matrix R contains N rows and n x ×n y Columns, where each row corresponds to a frame of data in the optical flow amplitude matrix. PCA needs to reconstruct the autocovariance matrix of the matrix R, which can be calculated by the following formula:

[0163] C=(RR mean )(RR mean ) T

[0164] In the formula, R mean Represents the mean matrix of the row vectors of the matrix R. Through singular value decomposition (SVD), the covariance matrix C can be decomposed into:

[0165] C=QΛQ T

[0166] In the formula, Q represents the matrix composed of the eigenvectors of the covariance matrix, and Λ represents the corresponding diagonal eigenvalue matrix. These eigenvectors are linearly independent, and each eigenvector corresponds to an eigenvalue. The size of the eigenvalue reflects the degree of variance change of the original data in the direction of the eigenvector. By sorting the eigenvalues ​​in descending order, the first k principal components corresponding to the main features can be extracted. The principal component M of PCA can be expressed as:

[0167] M=Q T R

[0168] In the above formula of principal component M, M represents the data matrix after dimensionality reduction, Q T represents the transposed eigenvector matrix, and R represents the original data matrix. The data matrix M only contains the scores of the first k principal components. In order to reconstruct the image, the first k principal components are used to reconstruct and reshape into a two-dimensional image matrix

[0169]

[0170]

[0171] In the formula, Represents the reconstructed data matrix, which has the same size as the original matrix M. represents the reconstruction vector, which is the result of adding the weighted first k principal components to the mean, μ represents the mean vector of the data, which is the average value of all the original data in each dimension, y i represents the coefficient of the i-th principal component, that is, the projection size of the original data on the i-th principal component, w i represents the eigenvector corresponding to the i-th principal component, that is, the direction of the principal component, N x Indicates the width of the image, N y Indicates the height of the image.

[0172] like Figure 8 As shown in the figure, the first k principal components extracted by PCA are weighted reconstructed to further suppress the background information, improve the contrast between the corrosion area and the non-corrosion area, and solve the problem of difficult recognition caused by unclear local temperature differences.

[0173] The present invention creatively combines the LK optical flow method with PCA to characterize the dynamic change characteristics of the temperature distribution in the corrosion area. Specifically, the changes in the electrical conductivity, magnetic permeability and thermal conductivity of the material lead to obvious differences in the heat diffusion process between the corrosion area and the non-corrosion area, which is manifested in the infrared image as the uneven directionality and intensity of the temperature change. The LK optical flow method can capture the dynamic changes of these temperature fields and extract the motion information of the pixel points; while PCA further reduces the dimension of the optical flow features and reconstructs them to highlight the main heat flow distribution characteristics.

[0174] According to the above analysis, the corrosion area and the non-corrosion area show obvious differences in the optical flow characteristics and PCA principal component results. Through the calculation process of LK-PCA, the present invention can effectively reconstruct the heat flow distribution characteristics of the corrosion area, thereby accurately locating the early corrosion area.

[0175] In addition, optical flow estimation and feature dimension reduction methods based on other principles can replace some of the algorithm steps in the present invention, but the present invention has obvious advantages in method innovation and pertinence. At present, the method of optical flow feature extraction is not limited to the Lucas-Kanade optical flow method, and other methods such as the Horn-Schunck optical flow method and the Farneback optical flow method can also be used; similarly, the dimension reduction and reconstruction steps can also be replaced by independent component analysis (ICA), singular value decomposition (SVD) or convolutional autoencoder (CAE) and other methods to extract features instead of PCA. However, the present invention uses a combination of optical flow amplitude and PCA to capture the dynamic heat flow direction and intensity differences caused by early corrosion. This method is groundbreaking, and no other alternatives have been found to achieve the same effect in early corrosion detection.

[0176] In addition, if Figure 9-10 As shown in the figure, the LK optical flow method combined with PCA is used to process the corrosion detection image, extract the first four principal components, and generate a reconstructed image that only retains key information through weighted combination. Fig. 9 and Fig.10 The best results of the early corrosion detection method based on eddy current thermal imaging and optical flow-PCA proposed in the present invention in the visualization enhancement of the corrosion area on the 49th day and the 66th day are respectively demonstrated. From the results shown in the figure, it can be observed that the proposed method is significantly better than the traditional method in the defect visualization enhancement of the thermal image sequence. The contrast between the corrosion area and the non-corrosion area is significantly improved, and the influence of the background area is greatly reduced. At the same time, since the subtle differences in the heat flow direction and intensity between the corrosion area and the non-corrosion area can be accurately captured, the outline of the corrosion area is better highlighted and its boundary is clearer. These results show that the proposed method has significant advantages in enhancing the visualization effect of early corrosion.

[0177] In summary, with the help of the above technical solutions of the present invention, the present invention can capture the dynamic motion information of pixel points in the infrared image sequence by using the LK optical flow method, and convert the spatiotemporal changes of the temperature field into the optical flow amplitude and direction characteristics, thereby making up for the deficiency of the FFT method in ignoring the dynamic evolution characteristics of temperature. This process can accurately reflect the subtle differences in the heat flow direction and intensity between the corrosion area and the non-corrosion area, greatly improving the sensitivity of the early corrosion signal. The present invention calculates the temperature difference between the corrosion area and the non-corrosion area, selects the key frame with the most significant change in the optical flow value for analysis, and this adaptive frame selection mechanism effectively reduces the interference of background noise and heating unevenness, and improves the stability and accuracy of feature extraction. The present invention combines PCA dimensionality reduction and reconstruction, extracts the main components of the optical flow field, performs weighted reconstruction on the corrosion image, highlights the heat flow characteristics of the corrosion area, and suppresses the redundant information of the background area, significantly enhancing the contrast and boundary clarity between the corrosion area and the background area. This method avoids the subjectivity of FFT frequency selection and ensures the objectivity and consistency of the reconstruction results. The present invention combines the optical flow method with PCA, breaking through the limitations of the existing technology in dynamic feature capture, noise resistance and visualization effect, and can achieve accurate positioning of early corrosion areas and corrosion degree assessment with high sensitivity and low noise.

[0178] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0179] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An early corrosion detection method based on eddy current thermal imaging and optical flow-PCA, characterized in that: The method comprises the following steps: S1. Based on the infrared thermal image sequence of the early corrosion sample acquired in advance, the temperature response curves of the corrosion area and the non-corrosion area of ​​the early corrosion sample are analyzed, and the time frame interval with the largest relative tangent slope in the temperature response curve is selected; S2. Based on the Lucas-Kanade optical flow method, the optical flow is calculated for the selected time frame interval, and the optical flow amplitude matrix is ​​constructed according to the calculation results; according to the optical flow amplitude matrix, the optical flow amplitude image sequence of continuous frames is obtained, and the optical flow amplitude difference is calculated based on the optical flow amplitude image sequence to evaluate the corrosion degree of the early corrosion sample; S3. Reconstruct the optical flow amplitude image sequence of continuous frames to obtain a reconstruction matrix, and use the principal component analysis method to extract features of the reconstruction matrix to obtain principal component features. By weighted reconstruction of the principal component features, the heat flow features of the corrosion area are obtained.

2. The early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to claim 1 is characterized in that: The method of analyzing the temperature response curves of the corrosion area and the non-corrosion area of ​​the early corrosion sample based on the infrared thermal image sequence of the early corrosion sample acquired in advance and selecting the time frame interval with the largest relative tangent slope in the temperature response curve comprises the following steps: S11, obtaining an infrared thermal image sequence of an early corrosion sample pre-detected using eddy current pulse thermal imaging technology, and recording the temperature changes of the corrosion area and the non-corrosion area of ​​the early corrosion sample during the heating and cooling process; S12. Based on the temperature changes of the corrosion area and the non-corrosion area of ​​the early corrosion sample during the heating and cooling process, the time frame interval with the largest relative tangent slope is selected by analyzing the temperature change curves of the corrosion area and the non-corrosion area.

3. The early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to claim 1 is characterized in that: The Lucas-Kanade optical flow method is used to calculate the optical flow of the selected time frame interval, and to construct an optical flow amplitude matrix according to the calculation results; According to the optical flow amplitude matrix, an optical flow amplitude image sequence of continuous frames is obtained, and the optical flow amplitude difference is calculated based on the optical flow amplitude image sequence. The corrosion degree of the early corrosion sample is evaluated, which includes the following steps: S21, using the Lucas-Kanade optical flow method, performing pixel displacement calculation on adjacent frames within the selected time frame interval, and obtaining an optical flow component of each pixel according to the calculation result; S22, calculating the optical flow amplitude according to the optical flow component of each pixel using the optical flow amplitude calculation formula; S23, normalizing the optical flow amplitude by using a normalization method to obtain a normalized optical flow amplitude, and constructing an optical flow amplitude matrix according to the normalized optical flow amplitude; S24, converting the normalized optical flow amplitude into a color image through a Jet color mapping scheme to obtain an optical flow amplitude image sequence of continuous frames; S25. According to the normalized optical flow amplitude, by selecting the optical flow amplitudes of the corroded area and the non-corroded area, and calculating the average values ​​of the optical flow amplitudes of the corroded area and the non-corroded area respectively, the corrosion degree of the early corrosion sample is evaluated according to the difference between the average values ​​of the optical flow amplitudes of the corroded area and the non-corroded area.

4. The early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to claim 3 is characterized in that: The Lucas-Kanade optical flow method is used to calculate the pixel displacement of adjacent frames within the selected time frame interval, and the optical flow component of each pixel is obtained according to the calculation result, which includes the following steps: S211, according to the pixel gray value at each position in the selected time frame interval, by setting the gray value change of the pixel in the preset interval to be negligible, a gray value conservation formula is obtained; S212, by performing Taylor expansion on the gray value conservation formula, a linear approximation formula including pixel displacement is obtained; and the optical flow constraint equation is obtained in combination with the gray value conservation formula; S213. Construct an overdetermined set of equations according to the optical flow constraint equation, and use the least squares method to solve the overdetermined set of equations to obtain the optical flow component of each pixel.

5. The early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to claim 3 is characterized in that: The optical flow amplitude calculation formula is: Where F represents the optical flow amplitude, u represents the optical flow component of the pixel in the x-axis direction, and v represents the optical flow component of the pixel in the y-axis direction.

6. The early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to claim 3 is characterized in that: The calculation formula for normalizing the optical flow amplitude using the normalization method is: In the formula, F norm represents the normalized optical flow amplitude, F min Indicates the minimum value of the optical flow amplitude, F max Indicates the maximum value of the optical flow amplitude.

7. The early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to claim 1 is characterized in that: The method of reconstructing the optical flow amplitude image sequence of continuous frames to obtain a reconstructed matrix, extracting features from the reconstructed matrix using a principal component analysis method to obtain principal component features, and weighted reconstructing the principal component features to obtain heat flow features of the corrosion area includes the following steps: S31, reconstructing the optical flow amplitude image sequence of continuous frames into a two-dimensional matrix, and determining the mean matrix of the row vectors of the two-dimensional matrix; S32, constructing an autocovariance matrix of the two-dimensional matrix according to the mean matrix of the row vectors of the two-dimensional matrix and the two-dimensional matrix; S33, decomposing the covariance matrix by singular value decomposition method to obtain an eigenvector matrix and an eigenvalue matrix; S34. Sort the eigenvectors according to the eigenvalue matrix, select several principal components, and use the several principal components to perform image weighted reconstruction to obtain the heat flow characteristics of the corrosion area.

8. The early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to claim 7 is characterized in that: The calculation formula for constructing the autocovariance matrix of the two-dimensional matrix based on the mean matrix of the row vectors of the two-dimensional matrix and the two-dimensional matrix is: C=(R-R mean )(R-R mean ) T In the formula, C represents the covariance matrix, R mean is the mean matrix of the row vectors of the two-dimensional matrix R, where R represents a two-dimensional matrix and T represents a transposed matrix.

9. The early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to claim 7 is characterized in that: The calculation formula for decomposing the covariance matrix by singular value decomposition is: C=QΛQ T Where C represents the covariance matrix, Q is a matrix consisting of the eigenvectors of the covariance matrix, Λ is the corresponding diagonal eigenvalue matrix, and T represents the transposed matrix.

10. The early corrosion detection method based on eddy current thermal imaging and optical flow-PCA according to claim 7, characterized in that: The calculation formula for obtaining the heat flow characteristics of the corrosion area by using several principal components for image weighted reconstruction is: In the formula, represents the reconstructed data matrix, represents the reconstruction vector, μ represents the mean vector of the data, y i represents the coefficient of the i-th principal component, w i represents the eigenvector corresponding to the i-th principal component, k represents the number of principal components, N x Indicates the width of the image, N y Indicates the height of the image.

Images