A system and method for evaluating and automatically correcting speckle image quality in a high temperature environment

Abstract

The present invention relates to a system and method for evaluating and automatically correcting a speckle image quality under a high temperature environment, and belongs to the field of digital image processing; the system and method comprise a speckle field preparation module and an evaluation and correction module; the speckle field preparation module comprises a support constraint component, a two-dimensional motion component, a speckle spraying component, and a host computer control component; the support constraint component comprises a test piece, a back support plate, a side clamping block, and a bottom support plate; the two-dimensional motion component comprises a motion support beam, a longitudinal motion component, and a transverse motion component; the evaluation and correction module comprises two CCD cameras, a data solving host computer, and a high and low temperature test chamber; a correction-improvement-correction automatic adjustment mode is adopted to realize clear and reliable speckle image acquisition, and the present invention realizes speckle image preparation, quality evaluation, and automatic correction under high and low temperature environments, further ensures accurate data collection in digital speckle detection, and realizes reliable solution of deformation of structural mechanisms under high and low temperature environments.

Description

Technical Field

[0001] The invention belongs to the field of digital image processing and relates to a system and method for evaluating and automatically correcting speckle image quality in a high temperature environment. Background Art

[0002] At present, in many industrial fields such as aviation, aerospace, and military industry, conducting on-orbit mechanical properties testing of mechanical structures in high and low temperature environments is an effective way to analyze the rationality of spacecraft design. Analysis of product mechanical properties often requires displacement and deformation testing. As a typical application of digital image correlation technology, digital speckle detection technology has the technical advantages of non-contact and high precision, and is suitable for in-situ detection of displacement and deformation in high and low temperature environments.

[0003] The detection accuracy and accuracy of the detection results of digital speckle detection technology directly depend on the quality of the speckle field. Clear, accurate and random speckle images are the prerequisite for accurately reflecting deformation and displacement. However, the preparation of speckle fields is often carried out manually, and the randomness and size of the speckle spots are difficult to reliably guarantee. In addition, there are currently no effective control measures for the interference of factors such as light and vibration in high and low temperature environments on the acquisition and imaging of speckle images. Measurement errors are inevitably introduced during measurement, which makes the displacement and deformation detection in high and low temperature environments inaccurate, bringing errors and uncertainties to the performance evaluation of subsequent structural products based on the detection results. Summary of the invention

[0004] The technical problem solved by the present invention is: to overcome the shortcomings of the prior art, to propose a system and method for speckle image quality assessment and automatic correction in a high temperature environment, to realize the preparation, quality assessment and automatic correction of speckle images in high and low temperature environments, to further ensure the accurate collection of data in digital speckle detection, and to realize reliable solution of the deformation of structural mechanisms in high and low temperature environments.

[0005] The solution of the present invention is:

[0006] A speckle image quality assessment and automatic correction system in a high temperature environment, comprising a speckle field preparation module and an evaluation and correction module; wherein the speckle field preparation module comprises a support constraint component, a two-dimensional motion component, a speckle spraying component, and a host computer control component; the support constraint component comprises a test piece, a back support plate, a side clamping block, and a bottom support plate; the two-dimensional motion component comprises a motion support beam, a longitudinal motion component, and a lateral motion component; the evaluation and correction module comprises two CCD cameras, a data solution host computer, and a high and low temperature test chamber;

[0007] Among them, the bottom support plate is placed horizontally; the back support plate is vertically installed on the upper surface of the bottom support plate; the test piece is vertically attached to the side wall of the back support plate; the two sides of the test piece are clamped and constrained by side clamping blocks respectively; the moving support beam is vertically arranged on the bottom support plate; the moving support beam is combined in different lengths through a clamping device to adapt to test pieces of different sizes; a guide rail is arranged on the upper surface of the bottom support plate; the transverse direction movement component is arranged on the guide rail of the bottom support plate, and the transverse direction movement component drives the moving support beam to move along the guide rail; the longitudinal direction movement component is arranged on the side wall guide rail of the moving support beam; the speckle spraying component is installed on the longitudinal direction movement component; the speckle spraying component is driven by the longitudinal direction movement component to move up and down along the direction of the moving support beam guide rail; when the speckle spraying component reaches the preset position on the surface of the test piece, the upper computer control component drives the speckle spraying component to spray the surface of the test piece; the upper computer control component realizes the control of the spraying trajectory of the speckle spraying component;

[0008] After spraying, the test piece is placed in a high and low temperature test chamber. Two CCD cameras form a binocular imaging system, which captures the state of the speckle field of the test piece under high and low temperature environments. The captured image representing the state of the speckle field is transmitted to the data solving host computer for solving and correction processing.

[0009] In the above-mentioned speckle image quality assessment and automatic correction system under high temperature environment, the data solving host computer scores the image quality by calculating and analyzing the speckle field quality evaluation index, statistically records the items to be corrected, and realizes automatic correction of the speckle image based on the preset image template using the relevant correction algorithm, so as to provide clear and high-quality original data for the subsequent deformation detection of the test piece under high and low temperature environment based on the speckle image.

[0010] The quality assessment and automatic correction method of the speckle image quality assessment and automatic correction system in the above-mentioned high temperature environment comprises:

[0011] Step 1: Confirm that the speckle field preparation module and the evaluation and correction module are working properly;

[0012] Step 2: Install the test piece on the side wall of the back support plate and further tighten the test piece using the side clamping block;

[0013] Step 3: Calculate the specific spraying parameters of the white bottom speckle and the black speckle based on the size and surface state of the test piece, and carry out automatic preparation of the speckle field;

[0014] Step 4: Place the test piece with sprayed speckle in a high and low temperature test chamber, and use the CCD camera in the evaluation and correction module to collect and image the speckle field on the surface of the test piece at different ambient temperatures stage by stage;

[0015] Step 5: The upper computer solves the data set of the whole process speckle field stage by stage through the data solving in the evaluation and correction module to obtain the speckle image that needs to be corrected;

[0016] Step 6: Correct the speckle image that needs to be corrected;

[0017] Step 7: After the speckle image correction is completed, repeat step 5 to evaluate the brightness, de-jittering and clarity of the image again; when the evaluation result meets the preset value, execute step 8; otherwise, repeat step 6;

[0018] Step 8: Store the speckle images that meet the quality requirements according to temperature and time series for subsequent deformation and displacement detection.

[0019] In the above method, in step 3, the specific spraying parameters of the white bottom speckle and the black speckle are calculated, and the automatic preparation method of the speckle field is carried out as follows:

[0020] S31. Use a white light source that is consistent with the shape of the test piece to illuminate the surface of the test piece horizontally; use a CCD camera to perform edge detection on the surface of the test piece illuminated by the light source, and locate the local area whose grayscale is obviously different from the overall grayscale;

[0021] S32, combining statistics of the pixel points enclosed in the closed local area under the illumination of the light source, and solving the position of the closed area center in the image pixels. ;in Indicates the pixel value of a point on the image; i, j indicates the pixel position; n, m indicates the size of the area;

[0022] S33, comparing the pixel values ​​of the center point of each area with those of the edge area to determine the concave-convex state of the current area;

[0023] S34, calculate the degree of regional concavity and convexity, and set the speckle spraying parameters; the data solution host computer controls the speckle spraying component to perform targeted speckle spraying; weaken the spraying driving force in the convex area, and enhance the spraying power in the concave area to achieve uniform spraying of the white coating of the entire test piece; correct the spraying power of each point; where F is the preset global speckle spraying component power value, which is set by the data solution host computer, is the corrected spraying power value;

[0024] S35, establishing a speckle field value table based on the size of the test piece;

[0025] S36, controlling the speckle spraying assembly to move to the upper left and lower right positions of the surface of the test piece through data solving by the host computer for calibration, and realizing the comparison between the movement route of the two-dimensional movement assembly and the surface of the test piece by unifying the spatial coordinates of the two points and the coordinates of the two-dimensional movement assembly;

[0026] S37. Through S31-S36, the speckle field size of the test piece surface, the intensity of each point of the white speckle spraying as the background color, the diameter and movement path of each point of the black speckle are obtained. The formed speckle field value is controlled by the upper computer after data solving. The two-dimensional motion component drives the speckle spraying component to perform speckle spraying on the surface of the test piece point by point, and infrared radiation is cured point by point during the spraying process, finally forming a high-quality speckle field with uniform background color throughout the field and black as the feature point that meets the detection accuracy requirements and has strong randomness.

[0027] In the above method, in S31, edge detection is performed on the image illuminated by the light source based on the Canny detection algorithm combined with a closing operation to form a contour map, and the closed local area detected in the contour map where there are pixels significantly higher than the overall mean value of the image is the concave-convex area to be analyzed.

[0028] In the above method, in S32, the position of the closed area center in the image pixel The solution is:

[0029] .

[0030] In the above method, in S33, the method for determining the concave-convex state of the current area is:

[0031]

[0032] In the formula, is the center pixel value;

[0033] Mean represents the pixel mean of all points in the current area except the center point;

[0034] theta(x,y) represents the ratio of the two;

[0035] The concave and convex state of the current area is determined by the ratio. Once theta is greater than the threshold It is judged as convex state, otherwise it is concave state.

[0036] In the above method, in S34, the method for calculating the regional concavity and convexity is:

[0037]

[0038] In the formula, mean_all represents the pixel mean of the center point of non-all regions;

[0039] is the speckle correction factor;

[0040] is the regional status judgment threshold;

[0041] Convex areas receive positive correction coefficients, and concave areas receive negative correction coefficients;

[0042] Spraying power after spot correction at each point for:

[0043]

[0044] Where F is the preset global speckle spray component power value; F is set by the data solution host computer.

[0045] In the above method, in S35, the method for establishing the speckle field value table is:

[0046] Combine the highest detection accuracy of CCD camera and the speckle size spraying capability of the speckle spraying component to determine the minimum diameter of black speckle ; The diagonal length of the smallest circumscribed square of the largest concave-convex area is the maximum diameter of the black speckle ; Set the speckle center coordinates to ; The diameter of the black scattered spots at each point on the surface of the test piece is defined as:

[0047]

[0048] It is ensured that 60% or more of the test piece surface is sprayed with black speckle; the unsprayed position points are randomly selected by the data solving host computer; the further sprayed black speckle points are located at The black speckle diameter value is determined by random sampling within the closed interval.

[0049] In the above method, in step 5, the method for solving the full-process speckle field data set stage by stage is:

[0050] Calculate image brightness: grid the image into The regional distribution of the image is calculated, and the grayscale mean of the whole image and the grayscale mean of each region are calculated respectively. The grayscale mean of the whole image is used to represent the overall brightness of the image, and the grayscale mean of each region is used to represent the local brightness of the image. The ratio of the grayscale mean of each region to the grayscale mean of the whole image is used to represent the local brightness change state of the image. When calculating, the non-heated state is used as the reference value, and the image brightness change is judged by comparing the overall brightness of the image in different states. If the difference between the reference value and the reference value is outside the preset value range, correction is required;

[0051] The jitter degree evaluation is carried out in the same group of images: image correlation calculation is performed through the black speckle feature points randomly sprayed in the speckle field, the feature point vector displacement is calculated in the same group of images, and the displacement field of the specimen surface is solved; when solving, the correlation calculation is first performed, and on this basis, the displacement of the matching speckle spots is calculated, and the displacement offset values ​​of all images in the group compared with the first image are recorded as the jitter degree evaluation index;

[0052] Calculate image clarity:

[0053]

[0054] In the formula, and is the gradient value in the horizontal and vertical directions;

[0055] B is the clarity evaluation parameter;

[0056] The non-heated state is used as the reference value during calculation. The image clarity is determined by comparing the image B values ​​under different states. If the difference with the reference value is outside the preset value range, the clarity is insufficient and needs to be corrected.

[0057] In the above method, in step 6, the method for correcting the speckle image is:

[0058] For images whose brightness index needs to be corrected, the grayscale mean index of the image to be corrected is compared with that of the reference image to determine whether the image is too dark or too bright at this time, and the overall brightness correction value is calculated based on the grayscale mean index of the overall image; further, the grayscale ratio coefficient of each grid area in the reference image and the overall image is calculated, and the brightness of the image to be corrected is corrected according to this ratio coefficient:

[0059]

[0060]

[0061] In the formula, is the grayscale mean of the entire reference image;

[0062] The grayscale mean of the entire image needs to be corrected;

[0063] Correction deviation values ​​for each area of ​​the correction map to be solved;

[0064] and is the image pixel value before and after correction;

[0065] For images whose jitter index needs to be corrected, the displacement field data matrix between all images in the current group and the reference image is calculated, and the displacement states of all detected speckles in the area are counted according to the divided grid area, and the singular points with large change trends, change values ​​and regional displacement mean values ​​are eliminated; the displacement data of each feature point is counted for the eliminated data, and the displacement data is arranged in imaging time series, and the displacement data of the previous image is subtracted from the displacement of the next image to realize a single group of fluctuation data of the displacement data; the mean value of the data with repeated displacement fluctuations is solved, and this mean value is used as the deviation correction value caused by jitter, and this value is recorded as the correction value for subsequent deformation displacement detection based on speckle;

[0066] Perform sharpening enhancement processing on images whose clarity index needs to be corrected:

[0067]

[0068] In the formula, Represents the image data after sharpening;

[0069] Indicates data before sharpening.

[0070] The beneficial effects of the present invention compared with the prior art are:

[0071] (1) The present invention provides a computer-designed and automated speckle field spraying method based on the surface characteristics of the test piece itself. Compared with the current mainstream manual spraying method, it can more reliably achieve high-quality speckle field preparation and provide effective support for subsequent digital image correlation calculation deformation based on speckle field;

[0072] (2) The present invention provides a method for evaluating and correcting the quality of speckle field images under high and low temperature conditions, which improves the detectability of the speckle pattern during the test and effectively suppresses the noise interference of the external environment on the speckle pattern imaging;

[0073] (3) The present invention designs an automated speckle field spraying module, which can adjust the position of the speckle spraying device with high stability and multiple degrees of freedom. It can be combined with the transmission component design to meet the needs of batch assembly line testing of test pieces of different sizes. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a schematic diagram of a speckle field preparation module of the present invention;

[0075] Figure 2 This is a schematic diagram of the evaluation and correction module of the present invention;

[0076] Figure 3 This is a flow chart of speckle image quality assessment and automatic correction according to the present invention. DETAILED DESCRIPTION

[0077] The present invention will be further described below in conjunction with the embodiments.

[0078] The present invention provides a system and method for speckle image quality assessment and automatic correction in a high temperature environment, which is suitable for realizing speckle image preparation, quality assessment, and automatic correction in high and low temperature environments, further ensuring the accurate collection of data in digital speckle detection, and realizing reliable solution of deformation of structural mechanisms in high and low temperature environments.

[0079] Speckle image quality assessment and automatic correction system in high temperature environment, such as Figure 1 , Figure 2 As shown, it specifically includes a speckle field preparation module and an evaluation and correction module; wherein, the speckle field preparation module includes a support constraint component, a two-dimensional motion component, a speckle spraying component 8, and a host computer control component 9; the support constraint component includes a test piece 1, a back support plate 2, a side clamping block 3, and a bottom support plate 4; the two-dimensional motion component includes a motion support beam 5, a longitudinal motion component 6, and a lateral motion component 7; the evaluation and correction module includes two CCD cameras 11, a data solution host computer 12, and a high and low temperature test chamber 13.

[0080] Among them, the bottom support plate 4 is placed horizontally; the back support plate 2 is vertically installed on the upper surface of the bottom support plate 4; the test piece 1 is vertically attached to the side wall of the back support plate 2; the two sides of the test piece 1 are clamped and constrained by the side clamping blocks 3 respectively; the moving support beam 5 is vertically arranged on the bottom support plate 4; the moving support beam 5 is combined with different lengths through the clamping device to adapt to test pieces 1 of different sizes; a guide rail is arranged on the upper surface of the bottom support plate 4; the transverse direction movement component 7 is arranged on the guide rail of the bottom support plate 4, and the transverse direction movement component 7 drives the moving support beam 5 to move along the guide rail; the longitudinal direction movement component 6 is arranged on the side wall guide rail of the moving support beam 5; the speckle spraying component 8 is installed on the longitudinal direction movement component 6; the speckle spraying component 8 is driven by the longitudinal direction movement component 6 to move up and down along the guide rail direction of the moving support beam 5; when the speckle spraying component 8 reaches the preset position on the surface of the test piece 1, the upper computer control component 9 drives the speckle spraying component 8 to spray the surface of the test piece 1; the upper computer control component 9 realizes the control of the spraying trajectory of the speckle spraying component 8;

[0081] After spraying is completed, the test piece 1 is placed in a high and low temperature test box 13; two CCD cameras 11 form a binocular imaging system, through which the state of the speckle field of the test piece 1 under high and low temperature environments is captured; and the captured image representing the state of the speckle field is transmitted to the data solving host computer 12 for solving and correction processing.

[0082] The data solution host computer 12 scores the image quality by calculating and analyzing the speckle field quality evaluation index, statistically records the items to be corrected, and realizes automatic correction of the speckle image based on the preset image template using the relevant correction algorithm, so as to provide clear and high-quality original data for the subsequent deformation detection of the test piece under high and low temperature environments based on the speckle image.

[0083] like Figure 3 As shown, the speckle image quality assessment and automatic correction method in a high temperature environment specifically includes the following steps:

[0084] Step 1: Confirm that the speckle field preparation module and the evaluation and correction module are working properly.

[0085] Step 2: Install the test piece 1 on the side wall of the back support plate 2 and further tighten the test piece 1 using the side clamping block 3 .

[0086] Step 3: Calculate the specific spraying parameters of the white bottom speckle and the black speckle based on the size and surface condition of the test piece 1, and carry out automated preparation of the speckle field.

[0087] The specific spraying parameters of white bottom speckle and black speckle are calculated, and the automatic preparation method of speckle field is carried out as follows:

[0088] S31, use a white light source 10 that is consistent with the shape of the test piece 1 to directly illuminate the surface of the test piece 1 horizontally; use a CCD camera 11 to perform edge detection on the surface of the test piece 1 illuminated by the light source, and locate the local area whose grayscale is obviously different from the overall grayscale.

[0089] Based on the Canny detection algorithm combined with the closing operation, the edge detection of the image under the illumination of the light source is performed to form a contour map. The closed local area detected in the contour map with pixel points significantly higher than the overall mean value of the image is the concave-convex area to be analyzed.

[0090] S32, combining statistics of the pixel points enclosed in the closed local area under the illumination of the light source, and solving the position of the closed area center in the image pixels. ;in Represents the pixel value of a point on the image; i, j represent the pixel position; n, m represent the size of the area.

[0091] The position of the center of the closed region in image pixels The solution is:

[0092] .

[0093] S33, comparing the pixel values ​​of the center point of each area with those of the edge area to determine the concave-convex state of the current area.

[0094] The method for judging the concave and convex state of the current area is:

[0095]

[0096] In the formula, is the center pixel value;

[0097] Mean represents the pixel mean of all points in the current area except the center point;

[0098] theta(x,y) represents the ratio of the two;

[0099] The concave and convex state of the current area is determined by the ratio. Once theta is greater than the threshold It is judged as convex state, otherwise it is concave state.

[0100] S34, calculate the degree of regional concavity and convexity, and set the speckle spraying parameters; the data solution host computer 12 controls the speckle spraying component 8 to perform targeted speckle spraying; weaken the spraying driving force in the convex area, and enhance the spraying power in the concave area, so as to achieve uniform spraying of the white coating of the entire test piece 1 with equal thickness; correct the spraying power of each point; wherein F is a preset global speckle spraying component power value, which is set by the data solution host computer (12), It is the corrected spraying power value.

[0101] The calculation method of regional concavity is:

[0102]

[0103] In the formula, mean_all represents the pixel mean of the center point of non-all regions;

[0104] is the speckle correction factor;

[0105] is the regional status judgment threshold;

[0106] Convex areas receive positive correction coefficients, and concave areas receive negative correction coefficients;

[0107] Spraying power after spot correction at each point for:

[0108]

[0109] Wherein, F is the preset global speckle spraying component power value; F is set by the data solving host computer 12.

[0110] S35. Establish a speckle field value table based on the size of the test piece.

[0111] The method for establishing the speckle field numerical table is:

[0112] Combine the highest detection accuracy of CCD camera 11 and the spraying speckle size capability of speckle spraying assembly 8 to determine the minimum diameter of black speckle ; The diagonal length of the smallest circumscribed square of the largest concave-convex area is the maximum diameter of the black speckle ; Set the speckle center coordinates to ; The diameter of the black scattered spots at each point on the surface of the test piece 1 is defined as:

[0113]

[0114] It is ensured that 60% or more of the test piece 1 surface is sprayed with black speckles; and the unsprayed positions are randomly selected by the data solving host computer 12; further spraying black speckles is performed on the positions The black speckle diameter value is determined by random sampling within the closed interval.

[0115] S36, the upper computer 12 controls the speckle spraying assembly 8 to move to the upper left and lower right positions of the surface of the test piece 1 for calibration through data solving, and realizes the comparison between the movement route of the two-dimensional motion assembly and the surface of the test piece 1 by unifying the spatial coordinates of these two points with the coordinates of the two-dimensional motion assembly.

[0116] S37. Through S31-S36, the speckle field size of the surface of the test piece 1, the intensity of each point of the white speckle spraying as the background color, the diameter of each point of the black speckle and the motion path are obtained, and the formed speckle field value is controlled by the data solving host computer 12, and the two-dimensional motion component drives the speckle spraying component 8 to perform speckle spraying on the surface of the test piece 1 point by point, and infrared radiation curing is performed point by point during the spraying process, and finally a high-quality speckle field with uniform background color throughout the field and black as a feature point that meets the detection accuracy requirement and has strong randomness is formed.

[0117] Step 4: Place the test piece 1 with sprayed speckles in a high and low temperature test chamber 13, and use the CCD camera 11 in the evaluation and correction module to collect and image the surface speckle field of the test piece 1 at different ambient temperatures stage by stage.

[0118] Step 5: The host computer 12 solves the data set of the whole process speckle field stage by stage through the data solution in the evaluation and correction module to obtain the speckle image that needs to be corrected.

[0119] The method for solving the full-process speckle field data set stage by stage is:

[0120] Calculate image brightness: grid the image into The regional distribution of the image is calculated, and the grayscale mean of the whole image and the grayscale mean of each region are calculated respectively. The grayscale mean of the whole image is used to represent the overall brightness of the image, and the grayscale mean of each region is used to represent the local brightness of the image. The ratio of the grayscale mean of each region to the grayscale mean of the whole image is used to represent the local brightness change state of the image. When calculating, the non-heated state is used as the reference value, and the image brightness change is judged by comparing the overall brightness of the image in different states. If the difference between the reference value and the reference value is outside the preset value range, correction is required;

[0121] The jitter degree evaluation is carried out in the same group of images: image correlation calculation is performed through the black speckle feature points randomly sprayed in the speckle field, the feature point vector displacement is calculated in the same group of images, and the displacement field of the specimen surface is solved; when solving, the correlation calculation is first performed, and on this basis, the displacement of the matching speckle spots is calculated, and the displacement offset values ​​of all images in the group compared with the first image are recorded as the jitter degree evaluation index;

[0122] Calculate image clarity:

[0123]

[0124] In the formula, and is the gradient value in the horizontal and vertical directions;

[0125] B is the clarity evaluation parameter;

[0126] The non-heated state is used as the reference value during calculation. The image clarity is determined by comparing the image B values ​​under different states. If the difference with the reference value is outside the preset value range, the clarity is insufficient and needs to be corrected.

[0127] Step 6: Correct the speckle image that needs to be corrected.

[0128] The method for correcting the speckle image is:

[0129] For images whose brightness index needs to be corrected, the grayscale mean index of the image to be corrected is compared with that of the reference image to determine whether the image is too dark or too bright at this time, and the overall brightness correction value is calculated based on the grayscale mean index of the overall image; further, the grayscale ratio coefficient of each grid area in the reference image and the overall image is calculated, and the brightness of the image to be corrected is corrected according to this ratio coefficient:

[0130]

[0131]

[0132] In the formula, is the grayscale mean of the entire reference image;

[0133] The grayscale mean of the entire image needs to be corrected;

[0134] Correction deviation values ​​for each area of ​​the correction map to be solved;

[0135] and is the image pixel value before and after correction;

[0136] For images whose jitter index needs to be corrected, the displacement field data matrix between all images in the current group and the reference image is calculated, and the displacement states of all detected speckles in the area are counted according to the divided grid area, and the singular points with large change trends, change values ​​and regional displacement mean values ​​are eliminated; the displacement data of each feature point is counted for the eliminated data, and the displacement data is arranged in imaging time series, and the displacement data of the previous image is subtracted from the displacement of the next image to realize a single group of fluctuation data of the displacement data; the mean value of the data with repeated displacement fluctuations is solved, and this mean value is used as the deviation correction value caused by jitter, and this value is recorded as the correction value for subsequent deformation displacement detection based on speckle;

[0137] Perform sharpening enhancement processing on images whose clarity index needs to be corrected:

[0138]

[0139] In the formula, Represents the image data after sharpening;

[0140] Indicates data before sharpening.

[0141] Step 7: After the speckle image correction is completed, repeat step 5 to evaluate the brightness, de-jittering and clarity of the image again; when the evaluation result meets the preset value, execute step 8; otherwise, repeat step 6.

[0142] Step 8: Store the speckle images that meet the quality requirements according to temperature and time series for subsequent deformation and displacement detection.

[0143] The present invention provides a computer-designed and automated speckle field spraying method based on the surface characteristics of the test piece itself. Compared with the current mainstream manual spraying method, it can more reliably achieve high-quality speckle field preparation and provide effective support for subsequent digital image correlation calculation deformation based on the speckle field.

[0144] The present invention provides a method for evaluating and correcting the quality of speckle field images under high and low temperatures, which improves the detectability of the speckle pattern during the test and effectively suppresses the noise interference of the external environment on the speckle pattern imaging.

[0145] The present invention designs an automated speckle field spraying module, which can adjust the position of the speckle spraying device with high stability and multiple degrees of freedom, and can be combined with the transmission component design to meet the needs of batch assembly line testing of test pieces of different sizes.

[0146] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

Claims

1. A speckle image quality assessment and automatic correction system in a high temperature environment, characterized by: It comprises a speckle field preparation module and an evaluation and correction module; wherein the speckle field preparation module comprises a support constraint component, a two-dimensional motion component, a speckle spraying component (8), and a host computer control component (9); the support constraint component comprises a test piece (1), a back support plate (2), a side clamping block (3), and a bottom support plate (4); the two-dimensional motion component comprises a motion support beam (5), a longitudinal motion component (6), and a transverse motion component (7); the evaluation and correction module comprises two CCD cameras (11), a data solution host computer (12), and a high and low temperature test chamber (13); The bottom support plate (4) is placed horizontally; the back support plate (2) is vertically mounted on the upper surface of the bottom support plate (4); the test piece (1) is vertically placed on the side wall of the back support plate (2); both sides of the test piece (1) are clamped and constrained by side clamping blocks (3); the motion support beam (5) is vertically arranged on the bottom support plate (4); the motion support beam (5) is combined with different lengths through a clamping device to accommodate test pieces (1) of different sizes; a guide rail is arranged on the upper surface of the bottom support plate (4); a lateral motion component (7) is arranged on the guide rail of the bottom support plate (4); and the lateral motion component (7) is arranged on the guide rail of the bottom support plate (4). 7) drives the moving support beam (5) to move along the guide rail; the longitudinal direction moving component (6) is arranged on the side wall guide rail of the moving support beam (5); the speckle spraying component (8) is installed on the longitudinal direction moving component (6); the speckle spraying component (8) is driven by the longitudinal direction moving component (6) to move up and down along the guide rail of the moving support beam (5); when the speckle spraying component (8) reaches a preset position on the surface of the test piece (1), the upper computer control component (9) drives the speckle spraying component (8) to spray the surface of the test piece (1); the upper computer control component (9) realizes the control of the spraying trajectory of the speckle spraying component (8); After the spraying is completed, the test piece (1) is placed in a high and low temperature test chamber (13); two CCD cameras (11) form a binocular imaging system, and the state of the speckle field of the test piece (1) under the high and low temperature environment is captured by the binocular imaging system; and the captured image representing the state of the speckle field is transmitted to a data solving host computer (12) for solving and correction processing.

2. The speckle image quality assessment and automatic correction system in a high temperature environment according to claim 1, characterized in that: The data solving host computer (12) scores the image quality by calculating and analyzing the speckle field quality evaluation index, statistically records the items to be corrected, and realizes automatic correction of the speckle image by using a relevant correction algorithm based on a preset image template, thereby providing clear and high-quality original data for subsequent deformation detection of the test piece under high and low temperature environments based on the speckle image.

3. The quality assessment and automatic correction method of a speckle image quality assessment and automatic correction system in a high temperature environment according to claim 1, characterized in that: include: Step 1: Confirm that the speckle field preparation module and the evaluation and correction module are working properly; Step 2: Install the test piece (1) on the side wall of the back support plate (2), and further tighten the test piece (1) using the side clamping block (3); Step 3: Calculate specific spraying parameters of white bottom speckle and black speckle based on the size and surface condition of the test piece (1), and carry out automatic preparation of the speckle field; Step 4: placing the test piece (1) on which the speckle pattern has been sprayed in a high and low temperature test chamber (13), and using a CCD camera (11) in an evaluation and correction module to collect and image the surface speckle field of the test piece (1) at different ambient temperatures in stages; Step 5: The data solving host computer (12) in the evaluation and correction module solves the whole process speckle field data set stage by stage to obtain the speckle image that needs to be corrected; Step 6: Correct the speckle image that needs to be corrected; Step 7: After the speckle image correction is completed, repeat step 5 to evaluate the brightness, de-jittering and clarity of the image again; when the evaluation result meets the preset value, execute step 8; Otherwise, repeat step 6; Step 8: Store the speckle images that meet the quality requirements according to temperature and time series for subsequent deformation and displacement detection.

4. The method according to claim 3, characterized in that: In step 3, the specific spraying parameters of the white bottom speckle and the black speckle are calculated, and the automatic preparation method of the speckle field is carried out as follows: S31, using a white light source (10) having the same shape as the test piece (1) to directly illuminate the surface of the test piece (1) horizontally; using a CCD camera (11) to perform edge detection on the surface of the test piece (1) illuminated by the light source, and locate a local area whose grayscale is obviously different from the overall area; S32, combining statistics of the pixel points enclosed in the closed local area under the illumination of the light source, and solving the position of the closed area center in the image pixels. ;in Indicates the pixel value of a point on the image; i, j indicates the pixel position; S33, comparing the pixel values ​​of the center point of each area with those of the edge area to determine the concave-convex state of the current area; S34, calculating the degree of regional concavity and convexity, and setting speckle spraying parameters; the data solving host computer (12) controls the speckle spraying component (8) to perform targeted speckle spraying; weakening the spraying driving force in the convex area, and enhancing the spraying power in the concave area, so as to achieve uniform spraying of the white coating of the entire test piece (1) with equal thickness; correcting the spraying power of each spot; wherein F is a preset global speckle spraying component power value, which is set by the data solving host computer (12), is the corrected spraying power value; S35, establishing a speckle field value table based on the size of the test piece; S36, controlling the speckle spraying assembly (8) to move to the upper left and lower right positions of the surface of the test piece (1) through the data solving host computer (12) for calibration, and realizing the comparison between the movement path of the two-dimensional movement assembly and the surface of the test piece (1) by unifying the spatial coordinates of the two points with the coordinates of the two-dimensional movement assembly; S37. Through S31-S36, the speckle field size of the surface of the test piece (1), the intensity of each point of the white speckle spraying as the background color, and the diameter and movement path of each point of the black speckle are obtained. The formed speckle field value is controlled by the data solving host computer (12). The two-dimensional motion component drives the speckle spraying component (8) to perform speckle spraying on the surface of the test piece (1) point by point, and infrared radiation curing is performed point by point during the spraying process. Finally, a high-quality speckle field with a uniform background color throughout the field and black as a feature point that meets the detection accuracy requirements and has strong randomness is formed.

5. The method according to claim 4, characterized in that: In S31, edge detection is performed on the image illuminated by the light source based on the Canny detection algorithm combined with a closing operation to form a contour map, and the closed local area detected in the contour map with pixel points significantly higher than the overall mean value of the image is the concave-convex area to be analyzed.

6. The method according to claim 4, characterized in that: In S32, the position of the closed area center in the image pixel The solution is: Where n and m represent the size of the area.

7. The method according to claim 6, characterized in that: In S33, the method for determining the concave-convex state of the current area is: In the formula, is the center pixel value; Mean represents the pixel mean of all points in the current area except the center point; theta(x,y) represents the ratio of the two; The concave and convex state of the current area is determined by the ratio. Once theta is greater than the threshold It is judged as convex state, otherwise it is concave state.

8. The method according to claim 6, characterized in that: In S34, the method for calculating the regional concavity and convexity is: In the formula, mean_all represents the pixel mean of the center point of non-all regions; is the speckle correction factor; is the regional status judgment threshold; Convex areas receive positive correction coefficients, and concave areas receive negative correction coefficients; Spraying power after spot correction at each point for: Where F is the preset global speckle spray component power value; F is set by the data solution host computer (12).

9. The method according to claim 5, characterized in that: In S35, the method for establishing the speckle field value table is: Combining the highest detection accuracy of the CCD camera (11) and the speckle size spraying capability of the speckle spraying assembly (8) to determine the minimum diameter of the black speckle ; The diagonal length of the smallest circumscribed square of the largest concave-convex area is taken as the maximum diameter of the black speckle. ; Set the speckle center coordinates to ; The diameter of the black scattered spots at each point on the surface of the test piece (1) is defined as: It is ensured that 60% or more of the test pieces (1) are sprayed with black speckles; the positions that are not sprayed are randomly selected by the data solving host computer (12); the positions where the black speckles are sprayed are further determined. The black speckle diameter value is determined by random sampling within the closed interval.

10. The method according to claim 3, characterized in that: In step 5, the method for solving the full-process speckle field data set stage by stage is: Calculate image brightness: grid the image into The grayscale mean of the whole image and the grayscale mean of each region are calculated respectively. The grayscale mean of the whole image represents the overall brightness of the image, the grayscale mean of each region represents the local brightness of the image, and the ratio of the grayscale mean of each region to the grayscale mean of the whole image represents the local brightness change state of the image; The non-heated state is used as the reference value during calculation. The image brightness change is determined by comparing the overall brightness of the image in different states. If the difference with the reference value is outside the preset value range, correction is required. The jitter degree evaluation is carried out in the same group of images: image correlation calculation is performed through the black speckle feature points randomly sprayed in the speckle field, the feature point vector displacement is calculated in the same group of images, and the displacement field of the specimen surface is solved; when solving, the correlation calculation is first performed, and on this basis, the displacement of the matching speckle spots is calculated, and the displacement offset values ​​of all images in the group compared with the first image are recorded as the jitter degree evaluation index; Calculate image clarity: In the formula, and is the gradient value in the horizontal and vertical directions; B is the clarity evaluation parameter; The non-heated state is used as the reference value during calculation. The image clarity is determined by comparing the image B values ​​under different states. If the difference with the reference value is outside the preset value range, the clarity is insufficient and needs to be corrected.

11. The method according to claim 10, characterized in that: In step six, the method for correcting the speckle image is: For images whose brightness index needs to be corrected, the grayscale mean index of the image to be corrected is compared with that of the reference image to determine whether the image is too dark or too bright at this time, and the overall brightness correction value is calculated based on the grayscale mean index of the overall image; further, the grayscale ratio coefficient of each grid area in the reference image and the overall image is calculated, and the brightness of the image to be corrected is corrected according to this ratio coefficient: In the formula, is the grayscale mean of the entire reference image; The grayscale mean of the entire image needs to be corrected; Correction deviation values ​​for each area of ​​the correction map to be solved; and is the image pixel value before and after correction; For images whose jitter index needs to be corrected, the displacement field data matrix between all images in the current group and the reference image is calculated, and the displacement states of all detected speckles in the area are counted according to the divided grid area, and the singular points with large change trends, change values ​​and regional displacement mean values ​​are eliminated; the displacement data of each feature point is counted for the eliminated data, and the displacement data is arranged in imaging time series, and the displacement data of the previous image is subtracted from the displacement of the next image to realize a single group of fluctuation data of the displacement data; the mean value of the data with repeated displacement fluctuations is solved, and this mean value is used as the deviation correction value caused by jitter, and this value is recorded as the correction value for subsequent deformation displacement detection based on speckle; Perform sharpening enhancement processing on images whose clarity index needs to be corrected: In the formula, Represents the image data after sharpening; Indicates data before sharpening.

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