Digital image correlation method for space-time coupling of high-speed camera and low-speed camera

Abstract

The invention discloses a high and low speed camera space-time coupling digital image correlation method based on a digital image correlation method (DIC), a multi-view digital image correlation method (M-DIC) and a space-time digital image correlation method (ST-DIC). According to the method, single-camera image full-field displacement analysis is realized through DIC, and high-speed and low-speed camera digital image coupling is performed from a spatial dimension in combination with an M-DIC method; displacement fields of corresponding moments of a high-speed camera and a low-speed camera are obtained, a time shape function is obtained according to a displacement increment separable variable hypothesis, high-speed camera digital image coupling and low-speed camera digital image coupling are carried out from a space-time dimension in combination with an ST-DIC method, displacement and strain field information with high time and high spatial resolution are obtained, and the space-time limitation of a single camera in DIC application is broken through.

Description

Technical Field

[0001] This invention relates to a spatiotemporally coupled digital image correlation method for high and low speed cameras based on digital image correlation (DIC), multi-view digital image correlation (M-DIC), and spatiotemporal digital image correlation (ST-DIC). Technical Background

[0002] Digital image correlation (DIC) measures the displacement and deformation field of a measured object by analyzing the grayscale changes in two digital images before and after surface deformation. Image resolution directly affects the measurement accuracy of DIC. The image size captured by the camera is negatively correlated with the shooting speed; high resolution (large image) reduces shooting speed, while low resolution (small image) increases shooting speed. For example, the Photron FASTCAM SA1.1 high-speed camera can achieve a shooting speed of 200,000 frames per second at a resolution of 128×112 pixels. With 8GB of internal memory, the shooting time is 1.01 seconds. In contrast, the MV-VD500SM conventional CMOS low-speed camera achieves a shooting speed of 7 frames per second at a resolution of 2592×1944 pixels, 9 frames per second at 1536×2048 pixels, and a maximum of 52 frames per second at the lowest resolution of 640×480 pixels. The shooting rate of low-speed cameras cannot obtain sufficient instantaneous information. Taking the three-point bending deformation and failure process of rock as an example, when the loading speed is 0.1 mm / min, the entire loading process takes 90 seconds, which is much longer than the high-speed imaging time. Among them, the crack generation and expansion to failure takes only 0.89 seconds, and the low-speed camera can capture 8 frames of images at a resolution of 1536×2048 pixels. Using a single camera alone cannot meet the requirements of high spatiotemporal resolution.

[0003] Multiview digital image correlation (M-DIC) is an extension of DIC technology, which acquires images of the object under test through multiple cameras. The motion field (displacement and strain field) is obtained through global calculation by calibrating and backtracking the mesh of the multiple cameras. However, this method only involves instantaneous coupling between the multiple cameras.

[0004] Spacetime digital image correlation (ST-DIC) calculates object displacement using continuous images captured by a single camera. ST-DIC extends the spatial regularization strategy of traditional DIC to the time domain, leveraging the physical property that velocity fields typically exhibit smooth temporal evolution during dynamic processes to improve performance through joint spatiotemporal analysis. Currently, this method only performs calculations on continuous images from a single camera.

[0005] In summary, the main problems with current technology are: limited by the shooting frame rate of low-speed cameras, it is difficult to capture instantaneous changes; limited by the low resolution of high-speed cameras during high-speed acquisition, it is impossible to guarantee the accuracy of DIC measurement; and limited by the current DIC algorithm, it is impossible to fuse high frame rate low resolution images and low frame rate high resolution images to achieve coupled measurement under high spatiotemporal resolution.

[0006] This invention aims to construct a spatiotemporally coupled digital imaging method that integrates low-speed and high-speed cameras. By combining high-resolution, low-frame-rate images from low-speed cameras with low-resolution, high-frame-rate images from high-speed cameras, displacement field information with high temporal and spatial accuracy can be obtained in the spatiotemporal dimension. Summary of the Invention

[0007] This application proposes a spatiotemporal coupling digital image correlation method based on DIC, M-DIC and ST-DIC for high and low speed cameras to obtain displacement field information with high spatiotemporal accuracy.

[0008] To achieve the above objectives, this application provides a spatiotemporal coupling digital image correlation method for high and low speed cameras based on DIC, M-DIC, and ST-DIC, comprising the following steps:

[0009] S1. Place high-speed and low-speed cameras to ensure that both cameras capture the same scene;

[0010] S2 and high- and low-speed cameras respectively acquire data before loading. speckle image of the specimen surface at time [time] ;

[0011] S3. Apply a load to the specimen, and use high-speed and low-speed cameras to acquire speckle images of the specimen surface during the loading process. ;

[0012] S4. Establish a physical geometric model based on the shape of the specimen, perform mesh generation on the physical geometric model, and compare it with the shape before loading. speckle image of the specimen surface at time [time] Perform grid synchronization;

[0013] S5. The speckle image of the specimen surface acquired by the high- and low-speed camera. Perform time synchronization and obtain the synchronization moment. ;

[0014] S6. Combining the mesh, the surface speckle image acquired by the high- and low-speed cameras before loading. The speckle image of the specimen surface at the synchronization time of the high and low speed cameras. Calculations are performed to obtain the deformation field information at the synchronization time of the high-speed and low-speed cameras;

[0015] S7. Combining the mesh, the deformation field information at the synchronization time of the high-speed and low-speed cameras, and the speckle image of the surface before loading acquired by the high-speed camera. and speckle images of the specimen surface during loading. Calculations are performed to obtain the temporal amplitude and temporal shape function corresponding to the high-speed image;

[0016] S8. Combining the mesh, the surface speckle image acquired by the high- and low-speed cameras before loading. and speckle images of the specimen surface during loading. The time-shape function is calculated to obtain the deformation field information for all moments corresponding to the image captured by the high-speed camera.

[0017] Representing different cameras;

[0018] Before loading Coordinates of various points on the surface of the specimen in images captured by the time-lapse camera ;

[0019] Represents loading time;

[0020] This represents the synchronization time of images captured by high-speed and low-speed cameras.

[0021] The coordinates of various points on the surface of the specimen in the image captured by the camera after loading. , ;

[0022] The displacement vector of each point on the specimen .

[0023] As a further technical solution of the present invention, the sub-steps of step S1 are as follows:

[0024] S11. High-speed and low-speed cameras are placed perpendicular to each other, one directly in front of the sample and the other to one side. A semi-transparent mirror or beam splitter is used to ensure that the high-speed and low-speed cameras capture the same image.

[0025] As a further technical solution of the present invention, the sub-steps of step S2 are as follows:

[0026] S21. Create speckles on the surface of the specimen or use the natural texture of the specimen to create special markings, such as drawing cross lines on the surface of the specimen covered with speckles.

[0027] S22, in At a certain time, surface speckle images of the specimen before loading are acquired using high- and low-speed cameras, thus obtaining the... ;

[0028] S23. Repeat the acquisition of the pre-loading speckle image at least 10 times. Take the average grayscale value of one or more images as the reference speckle image, and the remaining images as the target speckle image;

[0029] S24. Calculate the root mean square error of displacement for all images using the DIC method, and take the average value as the displacement field measurement uncertainty.

[0030] As a further technical solution of the present invention, the sub-steps of step S3 are as follows:

[0031] S31. Start the testing machine loading and start recording with the high and low speed cameras via the synchronous controller to obtain the speckle image on the surface of the specimen. ;

[0032] S32. When the load on the testing machine reaches its peak or the specimen breaks, the testing machine provides a signal to trigger the synchronous controller to stop the high-speed camera from recording. The high-speed camera records and saves the image for a period of time before the trigger moment.

[0033] S33. When the sample is completely broken, the recording of the low-speed camera is stopped and the image is saved by the synchronous controller.

[0034] As a further technical solution of the present invention, the sub-steps of step S4 are as follows:

[0035] S41. Establish a geometric model based on the actual physical dimensions of the specimen surface, and generate a mesh using software such as GMESH and Mimics.

[0036] S42. Generate a mask image based on the physical mesh, such as setting the computation area to 255 and the non-computation area to 0. The mask image can be blurred to improve the relevance of subsequent calculations;

[0037] S43, before loading speckle image of the specimen surface at time [time] DIC analysis is performed between the physical mesh and the corresponding mask to deform the mask and make it fit the actual shape of the sample captured by the camera, obtaining initial estimates of the transformation parameters (scaling factor, translation, rotation angle). The transformation between the image coordinates and physical coordinates captured by any camera c is defined as:

[0038]

[0039] In the formula A homography matrix is ​​defined ( ). and These are the coordinates of the grid nodes in the image. and It refers to their corresponding positions in physical space (mm). and yes and Translation in direction, It is the scaling factor (unit: pixel / mm). It is a rotation matrix ( ), It is the rotation angle;

[0040] S44, Before loading the high-speed camera image. speckle image of the specimen surface at time [time] Before loading, images were taken with a mask and a low-speed camera. speckle image of the specimen surface at time [time] A DIC analysis is performed across these three systems to obtain the final transformation parameters. Once this step is complete, the mesh is placed over a reference image for each camera.

[0041] As a further technical solution of the present invention, the sub-steps of step S5 are as follows:

[0042] S51. Based on the timing of the synchronization controller and the respective image acquisition rates of the high-speed and low-speed cameras, determine the correspondence between the images. Frames L1, L2, L3…Ln captured by the low-speed camera sequentially correspond to frames H1, H2, H3…Hn captured by the high-speed camera, and they all correspond to specific times. .

[0043] S52. The instant of sample failure will cause a displacement transition. If the high and low speed cameras cannot be started by using a synchronous controller, the high and low speed cameras can be synchronized in time by using the curve of the change of the nodal displacement difference over time.

[0044] As a further technical solution of the present invention, the sub-steps of step S6 are as follows:

[0045] S61, according to the synchronization time The correspondence between images captured by high-speed and low-speed cameras is also established, with two distorted images at each corresponding moment: one captured by the high-speed camera and the other by the low-speed camera. Combined with the aforementioned mesh, the surface speckle images acquired by the high-speed and low-speed cameras before loading are... Synchronization time The corresponding deformed images consist of four images. Based on the M-DIC method, the cost function is minimized. The deformation field information at n time points corresponding to high-speed and low-speed cameras is calculated, and spatial coupling is performed to improve displacement accuracy. Cost function. The expression is as follows:

[0046]

[0047] In the formula , It is the spatial node displacement. It is a spatial nodal shape function. Representing different cameras, This represents the displacement uncertainty corresponding to the camera. For reference image, For deformed images, Physical space coordinates , It is the homography matrix that transforms physical coordinates into camera image coordinates.

[0048] As a further technical solution of the present invention, the sub-steps of step S7 are as follows:

[0049] S71, Assumption arrive The displacement increment between time points can be separated into the product of a function of time and a function of spatial displacement; therefore, the displacement field... It can be represented as:

[0050]

[0051] In the formula It is a scalar time amplitude that changes dynamically over time. and They are and The displacement field corresponding to time . and In time Inside, ;

[0052] S72, according to the synchronization time And the correspondence between images captured by high-speed and low-speed cameras, divided into n-1 segments for calculation. and Scalar temporal amplitude corresponding to high-speed images ;

[0053] S73. Unlike instantaneous DIC calculations, the spatiotemporal displacement field is parameterized as... .in It is a time-form function. It is a spatial nodal shape function. It represents the displacement of a spatiotemporal node. The magnitude formula is decomposed as follows: Known amplitude The time shape function can then be obtained. .

[0054] As a further technical solution of the present invention, the sub-steps of step S8 are as follows:

[0055] S81. Substitute all images from frame L1 to frame Ln captured by the low-speed camera and all images from frame H1 to frame H6 captured by the high-speed camera into the spatiotemporal coupling cost function, and minimize the cost function. The displacement fields corresponding to all image frames captured by the high-speed camera were calculated, and spatiotemporal coupling was performed to improve the accuracy of the displacement fields. Cost function. The calculation formula is:

[0056]

[0057] In the formula , It is the displacement of spatiotemporal nodes. It is a spatial nodal shape function. It is a time-form function.

[0058] Compared with the prior art, the advantages of the present invention are:

[0059] 1. This invention innovatively integrates Digital Image Correlation (DIC), Spatiotemporal Digital Image Correlation (ST-DIC), and Multi-view Digital Image Correlation (M-DIC) to construct a digital image correlation method based on spatiotemporal coupling of high-speed and low-speed cameras. This method achieves full-field displacement analysis of single-camera images through DIC; it combines M-DIC for spatial coupling to obtain the displacement fields of high-speed and low-speed cameras at corresponding moments; it obtains the amplitude corresponding to the high-speed image through the assumption of separable variables for displacement increments, thus obtaining the time-varying function; and it further combines ST-DIC for spatiotemporal coupling to obtain high-frame-rate, high-precision displacement and strain field information, overcoming the spatiotemporal limitations of single-camera DIC applications.

[0060] 2. Traditional DIC and ST-DIC calculations can only analyze images captured by a single camera, while the M-DIC method is used for multi-camera instantaneous coupling calculations. This method establishes a spatiotemporal high- and low-speed camera coupling system consisting of DIC (single-camera instantaneous displacement and strain field information), M-DIC (multi-camera instantaneous coupling), amplitude calculation, and ST-DIC (spatiotemporal coupling), achieving cross-spatiotemporal data fusion. Leveraging the spatiotemporal concatenation capability of ST-DIC, it acquires high-temporal-resolution and high-precision displacement fields, thereby obtaining more transient deformation information. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort, and this application can be applied to other similar scenarios based on the provided drawings. Unless obvious from the linguistic context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0062] Figure 1 This is a schematic diagram of the experimental setup used in this application.

[0063] Figure 2 This is a flowchart of the digital image processing of this application.

[0064] Figure 3 This is a schematic diagram of the grid synchronization in an embodiment of this application.

[0065] Figure 4 This is a schematic diagram of the time synchronization between the high-speed and low-speed cameras in this application.

[0066] Figure 5 This is a schematic diagram of amplitude calculation in this application. Detailed Implementation

[0067] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. The described embodiments are only a part of the embodiments of the present application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without inventive effort are within the scope of protection of the present application.

[0068] It should be noted that, for ease of description, only the parts relevant to the application are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in these embodiments can be arbitrarily combined, provided that the combined technical features are not contradictory. All feasible combinations of features are the technical content explicitly described herein. Any one of the multiple sub-features contained in the same statement can be applied independently, without necessarily being applied together with other sub-features.

[0069] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "a," and / or "the" are not specifically singular and may include the plural. Generally, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements. An element defined by the phrase "comprising an..." does not exclude the presence of other identical elements in the process, method, product, or apparatus that includes the element.

[0070] In the description of the embodiments of this application, unless otherwise stated, " / " means "or", for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more.

[0071] Digital image correlation (DIC) measures the displacement and deformation field of a measured object by analyzing the grayscale changes in two digital images before and after surface deformation. Image resolution directly affects the measurement accuracy of DIC. The image size captured by the camera is negatively correlated with the shooting speed; high resolution (large image) reduces shooting speed, while low resolution (small image) increases shooting speed. For example, the Photron FASTCAM SA1.1 high-speed camera can achieve a shooting speed of 200,000 frames per second at a resolution of 128×112 pixels. With 8GB of internal memory, the shooting time is 1.01 seconds. In contrast, the MV-VD500SM conventional CMOS low-speed camera achieves a shooting speed of 7 frames per second at a resolution of 2592×1944 pixels, 9 frames per second at 1536×2048 pixels, and a maximum of 52 frames per second at the lowest resolution of 640×480 pixels. The shooting rate of low-speed cameras cannot obtain sufficient instantaneous information. Taking the three-point bending deformation and failure process of rock as an example, when the loading speed is 0.1 mm / min, the entire loading process takes 90 seconds, which is much longer than the high-speed imaging time. Among them, the crack generation and expansion to failure takes only 0.89 seconds, and the low-speed camera can capture 8 frames of images at a resolution of 1536×2048 pixels. Using a single camera alone cannot meet the requirements of high spatiotemporal resolution.

[0072] Multiview digital image correlation (M-DIC) is an extension of DIC technology. Its core principle is to acquire images of the object under test using multiple cameras with different parameters. The motion field (displacement and strain field) is obtained through global calculation by calibrating and backtracking the mesh of the multiple cameras. However, this method only involves instantaneous coupling between the multiple cameras.

[0073] Spacetime digital image correlation (ST-DIC) calculates object displacement using continuous images captured by a single camera. ST-DIC extends the spatial regularization strategy of traditional DIC to the time domain, leveraging the physical property that velocity fields typically exhibit smooth temporal evolution during dynamic processes to improve performance through joint spatiotemporal analysis. Currently, this method only performs calculations on continuous images from a single camera.

[0074] This application discloses a spatiotemporal coupling digital image correlation method for high and low speed cameras based on DIC, M-DIC, and ST-DIC, including the following:

[0075] Place high-speed and low-speed cameras to ensure that both cameras capture the same image;

[0076] High-speed and low-speed cameras acquire data before loading. speckle image of the specimen surface at time [time] ;

[0077] A load is applied to the specimen, and high-speed and low-speed cameras acquire speckle images of the specimen surface during the loading process. ;

[0078] A physical geometric model is established based on the shape of the specimen. The physical geometric model is then meshed and compared with the model before loading. speckle image of the specimen surface at time [time] Perform grid synchronization;

[0079] The speckle images of the specimen surface acquired by the high- and low-speed cameras Perform time synchronization and obtain the synchronization moment. ;

[0080] Combined with the mesh, the high- and low-speed cameras acquire surface speckle images before loading. The speckle image of the specimen surface at the synchronization time of the high and low speed cameras. Calculations are performed to obtain the deformation field information at the synchronization time of the high-speed and low-speed cameras;

[0081] Combining the aforementioned mesh, the deformation field information at the synchronization time of the high-speed and low-speed cameras, and the speckle image of the surface before loading acquired by the high-speed camera. and speckle images of the specimen surface during loading. Calculations are performed to obtain the temporal amplitude and temporal shape function corresponding to the high-speed image;

[0082] Combined with the mesh, the high- and low-speed cameras acquire surface speckle images before loading. and speckle images of the specimen surface during loading. The time-shape function is calculated to obtain the deformation field information for all moments corresponding to the image captured by the high-speed camera.

[0083] Representing different cameras;

[0084] Before loading Coordinates of various points on the surface of the specimen in images captured by the time-lapse camera ;

[0085] Represents loading time;

[0086] This represents the synchronization time of images captured by high-speed and low-speed cameras.

[0087] The coordinates of various points on the surface of the specimen in the image captured by the camera after loading. , ;

[0088] The displacement vector of each point on the specimen .

[0089] This application discloses a spatiotemporal coupling digital image correlation method for high and low speed cameras based on DIC, M-DIC, and ST-DIC. It integrates Digital Image Correlation (DIC), Spatiotemporal Digital Image Correlation (ST-DIC), and Multi-view Digital Image Correlation (M-DIC) to construct a spatiotemporal digital image coupling technique for high and low speed cameras. This method achieves full-field displacement analysis of single-camera images through DIC; it combines M-DIC for spatial coupling to obtain the displacement fields of high and low speed cameras at corresponding moments; it obtains the amplitude corresponding to the high-speed image through the assumption of separable variables for displacement increments, thus obtaining the time-varying function; and it further combines ST-DIC for spatiotemporal coupling to obtain high-frame-rate, high-precision displacement and strain field information. This overcomes the spatiotemporal limitations of single-camera DIC applications.

[0090] Traditional DIC and ST-DIC calculations can only analyze images captured by a single camera, while M-DIC is used for multi-camera instantaneous coupling calculations. This method establishes a spatiotemporal high- and low-speed camera coupling system, combining DIC (single-camera instantaneous displacement and strain field information), M-DIC (multi-camera instantaneous coupling), amplitude calculation, and ST-DIC (spatiotemporal coupling), achieving cross-spatiotemporal data fusion. Leveraging the spatiotemporal concatenation capability of ST-DIC, high-temporal-resolution and high-precision displacement fields are obtained, thereby acquiring more transient deformation information.

[0091] Please see Figure 2 process, Figure 1 Indication Figure 3 Indication Figure 4 Indication and Figure 5 This application discloses a spatiotemporal coupling digital image correlation method for high and low speed cameras based on DIC, M-DIC, and ST-DIC, and provides the following reference embodiment example, the implementation of which includes:

[0092] exist At any given time, surface speckle images of the specimen before loading are acquired using high-speed and low-speed cameras 1 and 2. Specifically, this involves creating speckle patterns or utilizing the natural texture of the specimen on its surface, and making special markings, such as drawing crosshairs on the surface of the specimen covered with speckles. High-speed and low-speed cameras 1 and 2 are placed perpendicular to each other, with camera 1 positioned directly in front of the specimen 7 and camera 2 positioned to one side. A semi-transparent mirror or beam splitter 3 is used to ensure that the high-speed and low-speed cameras 1 and 2 capture the same image, such as... Figure 1 Illustration. Speckle images of the specimen surface before loading are acquired by cameras 1 and 2. .

[0093] To obtain the measurement uncertainty of the surface displacement field of the specimen, specifically, the speckle image before loading is obtained at least 10 times. One or more images are taken as the average grayscale value as the reference speckle image, and the remaining images are taken as the target speckle images. The root mean square error of displacement for all images is calculated using the DIC method, and the average value is taken as the displacement field measurement uncertainty.

[0094] Obtain speckle images of the specimen surface Specifically, the testing machine is started and loaded, and the high-speed and low-speed cameras 1 and 2 are started recording via the synchronous controller 5. When the load on the testing machine reaches its peak or the specimen breaks, the testing machine provides a signal via the synchronous controller interface 6 to trigger a signal, stopping the high-speed camera recording. The high-speed camera records and saves images from a period of time before the trigger moment. When the sample completely fractures, the recording of the low-speed camera is stopped and the image is saved via the synchronous controller 5. .

[0095] A geometric model is established based on the actual physical dimensions of the specimen surface, and a mesh is generated using software such as GMESH and Mimics. Simultaneously, a mask image is generated based on the physical mesh, with the computational region set to 255 and the non-computational region to 0. The mask image can be blurred to improve the relevance of subsequent calculations. Before loading... speckle image of the specimen surface at time [time] DIC analysis is performed between the physical mesh and the corresponding mask to deform the mask and make it fit the actual shape of the sample captured by the camera, obtaining initial estimates of the transformation parameters (scaling factor, translation, rotation angle). The transformation between the image coordinates and physical coordinates captured by any camera c is defined as:

[0096]

[0097] In the formula A homography matrix is ​​defined ( ). and These are the coordinates of the grid nodes in the image. and It refers to their corresponding positions in physical space (mm). and yes and Translation in direction, It is the scaling factor (unit: pixel / mm). It is a rotation matrix ( ), It is the rotation angle.

[0098] Before loading, captured by a high-speed camera speckle image of the specimen surface at time [time] like Figure 3 (c) Masks such as Figure 3 (b) and images taken with a low-speed camera before loading. speckle image of the specimen surface at time [time] like Figure 3 (a) A DIC analysis is performed across these three elements to obtain the final transformation parameters. Once this step is complete, the mesh is placed on the reference image for each camera, such as... Figure 3 Indication.

[0099] The correspondence between the images is determined based on the timing of the synchronous controller 5 and the respective image acquisition rates of the high-speed and low-speed cameras 1 and 2. The L1, L2, L3…Ln frames captured by the low-speed camera sequentially correspond to the H1, H2, H3…Hn frames captured by the high-speed camera, and they all correspond to specific times. ,like Figure 4Illustration. The instant of sample failure will result in a displacement transition. If the high and low speed cameras 1 and 2 cannot be activated by the synchronous controller 5, the time synchronization of the high and low speed cameras 1 and 2 can be achieved by using the curve of the change of the nodal displacement difference over time.

[0100] According to the synchronization time The correspondence between the images captured by high-speed and low-speed cameras 1 and 2 is also shown. At each corresponding moment, there are two deformed images, one captured by the high-speed camera and the other by the low-speed camera. Combined with the grid, the surface speckle images before loading acquired by the high-speed and low-speed cameras 1 and 2 are also shown. Synchronization time The corresponding deformed images consist of four images. Based on the M-DIC method, the cost function is minimized. The deformation field information at n time points corresponding to high- and low-speed cameras 1 and 2 is calculated, and spatial coupling is performed to improve displacement accuracy. Cost function. The expression is as follows:

[0101]

[0102] In the formula , It is the spatial node displacement. It is a spatial nodal shape function. Representing different cameras, This represents the displacement uncertainty corresponding to the camera. For reference image, For deformed images, Physical space coordinates , It is the homography matrix that transforms physical coordinates into camera image coordinates.

[0103] Assumption arrive The displacement increment between time points can be separated into the product of a function of time and a function of spatial displacement; therefore, the displacement field... It can be represented as:

[0104]

[0105] In the formula It is a scalar time amplitude that changes dynamically over time. and They are and The displacement field corresponding to time . and In time Inside, .

[0106] According to the synchronization time The correspondence between the images captured by high-speed and low-speed cameras 1 and 2 is calculated in n-1 segments. and Scalar temporal amplitude corresponding to high-speed images ,like Figure 5 Indication.

[0107] The calculation of the time-varying shape function is as follows: Unlike the instantaneous DIC calculation, the spatiotemporal displacement field is parameterized as... .in It is a time-form function. It is a spatial nodal shape function. It represents the displacement of a spatiotemporal node. The magnitude formula is decomposed as follows: Known amplitude The time shape function can then be obtained. .

[0108] All images from frame L1 to frame Ln captured by the low-speed camera and all images from frame H1 to frame H6 captured by the high-speed camera are input into the spatiotemporal coupling cost function, and the cost function is minimized. The displacement fields corresponding to all image frames captured by the high-speed camera were calculated, and spatiotemporal coupling was performed to improve the accuracy of the displacement fields. Cost function. The calculation formula is:

[0109]

[0110] In the formula , It is the displacement of spatiotemporal nodes. It is a spatial nodal shape function. It is a time-form function.

[0111] Please refer to the experimental setup for a high- and low-speed camera spatiotemporal coupling digital image correlation method based on DIC, M-DIC, and ST-DIC disclosed in this application. Figure 1 It includes camera 1, camera 2, semi-transparent mirror or beam splitter 3, light source 4, synchronization controller 5, synchronization controller interface 6, and sample 7.

[0112] The cameras 1 and 2 can be high-speed cameras or low-speed cameras; the semi-transparent mirror or beam splitter 3 is used to enable the cameras 1 and 2 to capture the same image; the light source 4 includes, but is not limited to, one or more devices; the sample 7 includes, but is not limited to, the appearance shown in this application.

[0113] This application discloses a spatiotemporal coupling digital image correlation method based on DIC, M-DIC, and ST-DIC high and low speed cameras, which can output structures including but not limited to the following: load data (load-time curve, loading history), image data (surface images of specimens under various loads), microstructure evolution (crack propagation path), etc.

[0114] This method integrates Digital Image Correlation (DIC), Spatiotemporal Digital Image Correlation (ST-DIC), and Multi-view Digital Image Correlation (M-DIC) to construct a spatiotemporally coupled digital image correlation method for high- and low-speed cameras. DIC enables full-field displacement analysis of single-camera images; combined with M-DIC, it couples the displacement fields of high- and low-speed cameras at corresponding moments in space; by assuming separable variables for displacement increments, it obtains the amplitude corresponding to the high-speed image, thus deriving the time-varying shape function; finally, combined with ST-DIC, it couples the displacement and strain fields in a spatiotemporal dimension to obtain high-frame-rate, high-precision information. This overcomes the spatiotemporal limitations of single-camera DIC applications, thereby acquiring more transient deformation information.

Claims

1. A spatiotemporally coupled digital image correlation method for high and low speed cameras based on digital image correlation (DIC), multi-view digital image correlation (M-DIC), and spatiotemporal digital image correlation (ST-DIC), characterized in that, Includes the following steps: S1. Place high-speed and low-speed cameras to ensure that both cameras capture the same scene; S2 and high- and low-speed cameras respectively acquire data before loading. speckle image of the specimen surface at time [time] ; S3. Apply a load to the specimen, and use high-speed and low-speed cameras to acquire speckle images of the specimen surface during the loading process. ; S4. Establish a physical geometric model based on the shape of the specimen, perform mesh generation on the physical geometric model, and compare it with the shape before loading. speckle image of the specimen surface at time [time] Perform grid synchronization; S5. The speckle image of the specimen surface acquired by the high- and low-speed camera. Perform time synchronization and obtain the synchronization moment. ; S6. Combining the mesh, the surface speckle image acquired by the high- and low-speed cameras before loading. The speckle image of the specimen surface at the synchronization time of the high and low speed cameras. Calculations are performed to obtain the deformation field information at the synchronization time of the high-speed and low-speed cameras; S7. Combining the mesh, the deformation field information at the synchronization time of the high-speed and low-speed cameras, and the speckle image of the surface before loading acquired by the high-speed camera. and speckle images of the specimen surface during loading. Calculations are performed to obtain the temporal amplitude and temporal shape function corresponding to the high-speed image; S8. Combining the mesh, the surface speckle image acquired by the high- and low-speed cameras before loading. and speckle images of the specimen surface during loading. The time-shape function is calculated to obtain the deformation field information for all moments corresponding to the image captured by the high-speed camera. Representing different cameras; Before loading Coordinates of various points on the surface of the specimen in images captured by the time-lapse camera ; Represents loading time; This represents the synchronization time of images captured by high-speed and low-speed cameras. The coordinates of various points on the surface of the specimen in the image captured by the camera after loading. , ; The displacement vector of each point on the specimen .

2. The high- and low-speed camera spatiotemporally coupled digital image correlation method based on digital image correlation (DIC), multi-view digital image correlation (M-DIC), and spatiotemporal digital image correlation (ST-DIC) as described in claim 1, is characterized in that, The sub-steps of step S1 are as follows: S11. High-speed and low-speed cameras are placed perpendicular to each other, one directly in front of the sample and the other to one side. A semi-transparent mirror or beam splitter is used to ensure that the high-speed and low-speed cameras capture the same image.

3. The high- and low-speed camera spatiotemporally coupled digital image correlation method based on digital image correlation (DIC), multi-view digital image correlation (M-DIC), and spatiotemporal digital image correlation (ST-DIC) as described in claim 1, is characterized in that... The sub-steps of step S2 are as follows: S21. Create speckles on the surface of the specimen or use the natural texture of the specimen to create special markings, such as drawing cross lines on the surface of the specimen covered with speckles. S22, in At a certain time, surface speckle images of the specimen before loading are acquired using high- and low-speed cameras, thus obtaining the... ; S23. Repeat the acquisition of the pre-loading speckle image at least 10 times. Take the average grayscale value of one or more images as the reference speckle image, and the remaining images as the target speckle image; S24. Calculate the root mean square error of displacement for all images using the DIC method, and take the average value as the displacement field measurement uncertainty.

4. The high- and low-speed camera spatiotemporally coupled digital image correlation method based on digital image correlation (DIC), multi-view digital image correlation (M-DIC), and spatiotemporal digital image correlation (ST-DIC) as described in claim 1, characterized in that, The sub-steps of step S3 are as follows: S31. Start the testing machine loading and start recording with the high and low speed cameras via the synchronous controller to obtain the speckle image on the surface of the specimen. ; S32. When the load on the testing machine reaches its peak or the specimen breaks, the testing machine provides a signal to trigger the synchronous controller to stop the high-speed camera from recording. The high-speed camera records and saves the image for a period of time before the trigger moment. S33. When the sample is completely broken, the recording of the low-speed camera is stopped and the image is saved by the synchronous controller.

5. The high- and low-speed camera spatiotemporally coupled digital image correlation method based on digital image correlation (DIC), multi-view digital image correlation (M-DIC), and spatiotemporal digital image correlation (ST-DIC) as described in claim 1, characterized in that, The sub-steps of step S4 are as follows: S41. Establish a geometric model based on the actual physical dimensions of the specimen surface, and generate a mesh using software such as GMESH and Mimics. S42. Generate a mask image based on the physical mesh, such as setting the computation area to 255 and the non-computation area to 0. The mask image can be blurred to improve the relevance of subsequent calculations; S43, before loading speckle image of the specimen surface at time [time] DIC analysis is performed between the physical mesh and the corresponding mask to deform the mask and make it fit the actual shape of the sample captured by the camera, obtaining initial estimates of the transformation parameters (scaling factor, translation, rotation angle). The transformation between the image coordinates and physical coordinates captured by any camera c is defined as: In the formula A homography matrix is ​​defined ( ). and These are the coordinates of the grid nodes in the image. and It refers to their corresponding positions in physical space (mm). and yes and Translation in direction, It is the scaling factor (unit: pixel / mm). It is a rotation matrix ( ), It is the rotation angle; S44, Before loading the high-speed camera image. speckle image of the specimen surface at time [time] Before loading, images were taken with a mask and a low-speed camera. speckle image of the specimen surface at time [time] A DIC analysis is performed between these three elements to obtain the final transformation parameters. After this step, the mesh is placed over the reference image for each camera.

6. The high- and low-speed camera spatiotemporally coupled digital image correlation method based on digital image correlation (DIC), multi-view digital image correlation (M-DIC), and spatiotemporal digital image correlation (ST-DIC) as described in claim 1, is characterized in that, The sub-steps of step S5 are as follows: S51. Based on the timing of the synchronization controller and the respective image acquisition rates of the high-speed and low-speed cameras, determine the correspondence between the images. Frames L1, L2, L3…Ln captured by the low-speed camera sequentially correspond to frames H1, H2, H3…Hn captured by the high-speed camera, and they all correspond to specific times. . S52. The instant of sample failure will cause a displacement transition. If the high and low speed cameras cannot be started by using a synchronous controller, the high and low speed cameras can be synchronized in time by using the curve of the change of the nodal displacement difference over time.

7. The high- and low-speed camera spatiotemporally coupled digital image correlation method based on digital image correlation (DIC), multi-view digital image correlation (M-DIC), and spatiotemporal digital image correlation (ST-DIC) as described in claim 1, characterized in that, The sub-steps of step S6 are as follows: S61, according to the synchronization time The correspondence between images captured by high-speed and low-speed cameras is also established, with two distorted images at each corresponding moment: one captured by the high-speed camera and the other by the low-speed camera. Combined with the aforementioned mesh, the surface speckle images acquired by the high-speed and low-speed cameras before loading are... Synchronization time The corresponding deformed images consist of four images. Based on the M-DIC method, the cost function is minimized. The deformation field information at n time points corresponding to high-speed and low-speed cameras is calculated, and spatial coupling is performed to improve displacement accuracy. Cost function. The expression is as follows: In the formula , It is the spatial node displacement. It is a spatial nodal shape function. Representing different cameras, This represents the displacement uncertainty corresponding to the camera. For reference image, For deformed images, Physical space coordinates , It is the homography matrix that transforms physical coordinates into camera image coordinates.

8. The high- and low-speed camera spatiotemporally coupled digital image correlation method based on digital image correlation (DIC), multi-view digital image correlation (M-DIC), and spatiotemporal digital image correlation (ST-DIC) as described in claim 1, characterized in that, The sub-steps of step S7 are as follows: S71, Assumption arrive The displacement increment between time points can be separated into the product of a function of time and a function of spatial displacement; therefore, the displacement field... It can be represented as: In the formula It is a scalar time amplitude that changes dynamically over time. and They are and The displacement field corresponding to time . and In time Inside, ; S72, according to the synchronization time And the correspondence between images captured by high-speed and low-speed cameras, divided into n-1 segments for calculation. and Scalar temporal amplitude corresponding to high-speed images ; S73. Unlike instantaneous DIC calculations, the spatiotemporal displacement field is parameterized as... .in It is a time-form function. It is a spatial nodal shape function. It represents the displacement of a spatiotemporal node. The magnitude formula is decomposed as follows: Known amplitude The time shape function can then be obtained. .

9. The high- and low-speed camera spatiotemporally coupled digital image correlation method based on digital image correlation (DIC), multi-view digital image correlation (M-DIC), and spatiotemporal digital image correlation (ST-DIC) as described in claim 1, characterized in that, The sub-steps of step S8 are as follows: S81. Substitute all images from frame L1 to frame Ln captured by the low-speed camera and all images from frame H1 to frame H6 captured by the high-speed camera into the spatiotemporal coupling cost function, and minimize the cost function. The displacement fields corresponding to all image frames captured by the high-speed camera were calculated, and spatiotemporal coupling was performed to improve the accuracy of the displacement fields. Cost function. The calculation formula is: In the formula , It is the displacement of spatiotemporal nodes. It is a spatial nodal shape function. It is a time-form function.

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