Deformation monitoring system and method based on phase information

Abstract

The present invention provides a phase information-based deformation monitoring system and method, relating to the field of engineering measurement technology. The system includes: a camera group that captures time-series images of a monitoring area and transmits them to a data processing unit; multiple deformation monitoring targets and control targets, respectively, disposed on the surface and surrounding areas of a target deformable body; and a data processing unit configured to extract the center coordinates of each target based on the time-series images, correct the center coordinates of the deformation monitoring targets, correct the time-series images obtained by correcting the center coordinates of the deformation monitoring targets based on the center coordinates of the control targets, calculate the displacement change of the control targets based on the corrected time-series images using a moiré fringe method, and obtain the corrected coordinates of the control targets. Based on the corrected coordinates, the position of the deformation monitoring targets in the corrected time-series images is finely compensated to determine the deformation monitoring results of the target deformable body. This achieves real-time and high-resolution deformation monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering measurement, and in particular to a deformation monitoring system and method based on phase information. Background Art

[0002] Deformation is a common engineering disaster, widely present in geological disasters, bridges, tunnels, dams, and other important engineering fields. It refers to the phenomenon of overall or partial displacement of rock, soil, or artificial structures under the action of external forces. In geological disasters, deformation usually manifests as slope sliding, forming a slip surface. The displaced part is called the slip body, and the undisplaced part is called the sliding bed, with the weak surface between the two. In projects such as bridges, tunnels, and dams, deformation manifests as structural settlement, crack expansion, or local instability. The causes of deformation include both natural factors, such as slope softening caused by rainwater infiltration, soil instability caused by groundwater activity, weakening of foundations by river scouring, and structural vibration caused by earthquakes; as well as human factors, such as excessive excavation, artificial slope cutting, excessive loads, and improper construction.

[0003] Deformation monitoring technology has significantly advanced with the development of aerospace remote sensing, Global Navigation Satellite System (GNSS), photogrammetry, and computer vision. Key technologies currently include satellite-based Differential Interferometric Synthetic Aperture Radar (DInSAR), which offers wide coverage and high precision, but struggles to achieve high-frequency, real-time monitoring. Satellite optical remote sensing provides high-resolution imagery, but its real-time performance is limited by satellite overflight frequency and weather conditions. Unmanned aerial vehicle (UAV) photogrammetry and Light Detection and Ranging (LiDAR) offer flexibility and high precision, but are hampered by flight time and weather, making continuous monitoring difficult. GNSS technology can achieve high-precision displacement monitoring, but its monitoring range is limited, preventing it from providing high-resolution information over large areas. Photogrammetry has certain limitations in its efficiency for large-scale monitoring, especially when high-frequency data collection is required, as it is susceptible to equipment operating time and measurement accuracy constraints. Computer vision, a rapidly developing technology in recent years, enables deformation monitoring through template matching, feature point detection, and optical flow analysis. While offering high real-time performance and flexibility, it still has limitations in areas with varying lighting conditions and low texture. While these technologies have made significant progress in their respective fields, deformation monitoring still faces challenges such as high cost, poor real-time performance, and limited spatial resolution. A more cost-effective, efficient, real-time, and high-resolution monitoring solution is urgently needed. Summary of the Invention

[0004] The present invention provides a deformation monitoring system and method based on phase information, which is used to solve the defects of deformation monitoring in the existing technology such as high cost, poor real-time performance and limited spatial resolution, and realize more economical, efficient, real-time and high-resolution deformation monitoring.

[0005] The present invention provides a deformation monitoring system based on phase information, comprising:

[0006] A shooting equipment group, multiple deformation monitoring targets, multiple control targets and a data processing unit;

[0007] The deformation monitoring target is set on the surface of the target deformable body; the control target is set in the peripheral area of the target deformable body and serves as a spatial reference benchmark for the deformation monitoring target; the target deformable body, the multiple deformation monitoring targets and the multiple control targets together constitute a monitoring area;

[0008] The shooting equipment group includes a drone group and / or a camera group, which is used to collect time-series images of the monitoring area and transmit the time-series images to the data processing unit; the drone group includes at least one drone, which is set directly above the monitoring area; the camera group includes at least one camera, which is set in the peripheral area of the monitoring area, and the camera covers the monitoring area by scanning;

[0009] The data processing unit is used to extract the center coordinates of the deformation monitoring target and the control target according to the time series image by maximizing the cross-correlation algorithm, correct the center coordinates of the deformation monitoring target in the time series image by a height compensation algorithm, correct the time series image after the center coordinates of the deformation monitoring target are corrected according to the center coordinates of the control target by a similarity transformation algorithm, calculate the displacement change of the control target according to the corrected time series image by a Moire fringe method and obtain the corrected coordinates of the control target, finely compensate the position of the deformation monitoring target in the corrected time series image according to the corrected coordinates of the control target by a similarity transformation algorithm, and finally determine the deformation monitoring result of the target deformable body based on the finely compensated time series image.

[0010] According to a phase information-based deformation monitoring system provided by the present invention, the center coordinates of the deformation monitoring target and the control target are extracted from the time series image by using a maximum cross-correlation algorithm, including:

[0011] Using an initial frame image in the time series image as a template image, and calculating a cross-correlation function between each subsequent frame image in the time series image and the template image;

[0012] Based on the maximum correlation value in the cross-correlation function between each subsequent frame image and the template image, the center coordinates of the deformation monitoring target and the control target in each subsequent frame image are determined.

[0013] According to a phase information-based deformation monitoring system provided by the present invention, a time series image after the center coordinates of the deformation monitoring target are corrected is corrected according to the center coordinates of the control target by using a similarity transformation algorithm, including:

[0014] Determining a reference coordinate system based on the center coordinates of a plurality of control targets in the template image and a predetermined reference control target; the reference control target is used for unified spatial reference of control targets other than the reference control target in the plurality of control targets;

[0015] Calculating similarity transformation parameters based on the central coordinates of the control target in the reference coordinate system and the central coordinates of the control target in the time series image after the central coordinates of the deformation monitoring target are corrected;

[0016] Based on the similarity transformation parameters, the time series images after the central coordinates of the deformation monitoring target are corrected are corrected.

[0017] According to a phase information-based deformation monitoring system provided by the present invention, the displacement change of the control target is calculated based on the corrected time series image by using the Moire fringe method and the corrected coordinates of the control target are obtained, including:

[0018] Extracting multiple frames of moiré fringe images based on the corrected time series images;

[0019] Performing low-pass filtering and downsampling processing on the multiple frames of moiré fringe images to obtain multiple frames of downsampled images;

[0020] Based on the moiré fringe phases of the multiple frames of downsampled images, the displacement change of the control target is calculated and the corrected coordinates of the control target are obtained.

[0021] According to a phase information-based deformation monitoring system provided by the present invention, the number of the control targets is three, and the three control targets form an L-shaped structure.

[0022] According to a phase information-based deformation monitoring system provided by the present invention, the deformation monitoring target and the control target are two-dimensional rectangular planes or three-dimensional cubes; the surfaces of the deformation monitoring target and the control target are provided with black or colored regularly spaced dots, and the dot shapes of the regularly spaced dots are polygonal or circular; the printing material of the regularly spaced dots is a reflective material that can be used day and night.

[0023] The present invention further provides a deformation monitoring method based on phase information, which is applied to any of the above-mentioned deformation monitoring systems based on phase information, comprising:

[0024] Collecting time series images of a monitoring area; the monitoring area is composed of a target deformable body, a plurality of deformation monitoring targets, and a plurality of control targets;

[0025] Extracting the center coordinates of the deformation monitoring target and the control target based on the time series image by maximizing the cross-correlation algorithm;

[0026] Correcting the center coordinates of the deformation monitoring target in the time series image by a height compensation algorithm;

[0027] Correcting the time series images after correcting the center coordinates of the deformation monitoring target according to the center coordinates of the control target using a similarity transformation algorithm;

[0028] Calculating the displacement change of the control target and obtaining the corrected coordinates of the control target based on the corrected time series images using a Moire fringe method;

[0029] Performing fine compensation on the position of the deformation monitoring target in the corrected time series image according to the corrected coordinates of the control target by using a similarity transformation algorithm;

[0030] Based on the finely compensated time series images, a deformation monitoring result of the target deformable body is determined.

[0031] According to a deformation monitoring method based on phase information provided by the present invention, the center coordinates of the deformation monitoring target and the control target are extracted according to the time series image by using a maximum cross-correlation algorithm, including:

[0032] Using an initial frame image in the time series image as a template image, and calculating a cross-correlation function between each subsequent frame image in the time series image and the template image;

[0033] Based on the maximum correlation value in the cross-correlation function between each subsequent frame image and the template image, the center coordinates of the deformation monitoring target and the control target in each subsequent frame image are determined.

[0034] According to a deformation monitoring method based on phase information provided by the present invention, a time series image after the center coordinates of the deformation monitoring target are corrected is corrected according to the center coordinates of the control target by using a similarity transformation algorithm, including:

[0035] Determining a reference coordinate system based on the center coordinates of a plurality of control targets in the template image and a predetermined reference control target; the reference control target is used for unified spatial reference of control targets other than the reference control target in the plurality of control targets;

[0036] Calculating similarity transformation parameters based on the central coordinates of the control target in the reference coordinate system and the central coordinates of the control target in the time series image after the central coordinates of the deformation monitoring target are corrected;

[0037] Based on the similarity transformation parameters, the time series images after the central coordinates of the deformation monitoring target are corrected are corrected.

[0038] According to a deformation monitoring method based on phase information provided by the present invention, the displacement change of the control target is calculated based on the corrected time series image by using the Moire fringe method and the corrected coordinates of the control target are obtained, including:

[0039] Extracting multiple frames of moiré fringe images based on the corrected time series images;

[0040] Performing low-pass filtering and downsampling processing on the multiple frames of moiré fringe images to obtain multiple frames of downsampled images;

[0041] Based on the moiré fringe phases of the multiple frames of downsampled images, the displacement change of the control target is calculated and the corrected coordinates of the control target are obtained.

[0042] The deformation monitoring system and method based on phase information provided by the present invention comprises a target deformable body, multiple deformation monitoring targets and multiple control targets, and a shooting device group collects time series images of the monitoring area and transmits the time series images to a data processing unit. The data processing unit extracts the center coordinates of each target from the time series images using a maximum cross-correlation algorithm, corrects the center coordinates of the deformation monitoring targets in the time series images using a height compensation algorithm, corrects the time series images after the center coordinates of the deformation monitoring targets are corrected using a similarity transformation algorithm based on the center coordinates of the control targets, calculates the displacement change of the control targets based on the corrected time series images using a moiré fringe method and obtains the corrected coordinates of the control targets, and then finely compensates the positions of the deformation monitoring targets in the corrected time series images based on the corrected coordinates of the control targets. Finally, the displacement change of the deformation monitoring targets is determined based on the finely compensated time series images to obtain the deformation monitoring results of the target deformable body, thereby achieving high-precision deformation monitoring of the target deformable body at a low cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 It is a structural diagram of the deformation monitoring system based on phase information provided by the present invention.

[0045] Figure 2 It is a flow chart of the deformation monitoring method based on phase information provided by the present invention.

[0046] Figure 3 It is a structural schematic diagram of the deformation monitoring system provided by the present invention.

[0047] Figure 4 It is a schematic diagram of the imaging principle of the reflective mark provided by the present invention.

[0048] Figure 5 It is a schematic diagram of the relationship between the square deformation of the reflective marker and the square deformation of the photo image provided by the present invention.

[0049] Figure 6 It is a schematic diagram of the target plane polygonal and circular designs provided by the present invention.

[0050] Figure 7 It is a schematic diagram of projecting a target onto a reference plane based on height compensation provided by the present invention.

[0051] Figure 8 It is a schematic diagram of the principle of the sampling moiré method for performing precise displacement measurement provided by the present invention.

[0052] Figure 9 It is a schematic diagram of the algorithm flow for deformation calculation provided by the present invention. DETAILED DESCRIPTION

[0053] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0054] Figure 1 FIG. 1 is a structural diagram of a deformation monitoring system based on phase information provided by the present invention. Figure 1 As shown, the system includes:

[0055] A photographing device group 100, a plurality of deformation monitoring targets 101, a plurality of control targets 102 and a data processing unit 103;

[0056] The deformation monitoring target 101 is disposed on the surface of the target deformable body 104; the control target 102 is disposed in the peripheral area of the target deformable body 104 and serves as a spatial reference for the deformation monitoring target 101; the target deformable body 104, the plurality of deformation monitoring targets 101, and the plurality of control targets 102 together constitute a monitoring area 105;

[0057] The photographing device group 100 includes a drone group and / or a camera group, which is used to collect time-series images of the monitoring area 105 and transmit the time-series images to the data processing unit 103; the drone group includes at least one drone, which is set directly above the monitoring area; the camera group includes at least one camera, which is set in the surrounding area of the monitoring area, and the camera covers the monitoring area by scanning;

[0058] The data processing unit 103 is used to extract the center coordinates of the deformation monitoring target and the control target from the time series image using a maximum cross-correlation algorithm, correct the center coordinates of the deformation monitoring target in the time series image using a height compensation algorithm, correct the time series image after the center coordinates of the deformation monitoring target are corrected using a similarity transformation algorithm based on the center coordinates of the control target, calculate the displacement change of the control target based on the corrected time series image using a Moire fringe method and obtain the corrected coordinates of the control target, finely compensate the position of the deformation monitoring target in the corrected time series image based on the corrected coordinates of the control target using a similarity transformation algorithm, and finally determine the deformation monitoring result of the target deformable body based on the finely compensated time series image.

[0059] Specifically, the target deformable body is a deformable body whose deformation degree needs to be monitored. The phase information-based deformation monitoring system provided by the present invention includes a shooting equipment group, multiple deformation monitoring targets, multiple control targets and a data processing unit, which work together to obtain the deformation monitoring results of the target deformable body.

[0060] In embodiments of the present invention, multiple deformation monitoring targets are provided, arranged on the surface of a target deformable object. These targets possess specific geometric shapes and reflective properties, enabling the capture of high-precision image data using a camera system. Positional changes in these targets can reflect the deformation process of the target deformable object. The number of these targets can be determined based on the target deformable object's area and the complexity of its internal motion.

[0061] In this embodiment of the present invention, multiple control targets are provided. These control targets are positioned around the target deformable body, primarily to provide a spatial reference for the deformation monitoring targets. It is understood that the positions of the control targets should be stable so that they remain fixed in the image. The relative positions and motion trajectories of the deformation monitoring targets can be accurately calibrated from the perspectives of the camera and drone group.

[0062] Optionally, the number of the control targets is three, and the three control targets form an L-shaped structure.

[0063] Specifically, the control targets can be placed in a stable area directly in front of the camera, with the center control target as the coordinate origin. The control targets on both sides are arranged in the extension direction of the center control target, forming a mutually orthogonal structural layout with the center control target. The three control targets form an "L"-shaped frame.

[0064] Optionally, the deformation monitoring target and the control target are two-dimensional rectangular planes or three-dimensional cubes; the surfaces of the deformation monitoring target and the control target are provided with black or colored regularly spaced dots, and the dot shapes of the regularly spaced dots are polygonal or circular; the printing material of the regularly spaced dots is a reflective material that can be used day and night.

[0065] The monitoring area consists of the target deformable body, deformation monitoring targets, and control targets. This area is covered by the camera's lens. All targets (deformation monitoring targets and control targets) should be placed within this area to ensure that the camera can fully capture the changes in the deformable body.

[0066] The shooting equipment group can be composed of a drone group, or the shooting equipment group can be composed of a camera group, or the shooting equipment group can be composed of a drone group and a camera group, and is used to obtain time series images of the monitoring area and transmit the time series images to the data processing unit.

[0067] A camera group can be set up to synchronously capture images at preset intervals. This interval can be flexibly adjusted based on the sliding speed and danger level of the target deformable object. When multiple cameras or drones are monitoring 3D deformation, photos or videos can be synchronized using time tags.

[0068] The drone group consists of at least one drone, positioned directly above the monitoring area. The drone provides an aerial perspective, efficiently covering the entire monitoring area. The images captured by the drone provide real-time spatial information about the deformed area, which is particularly advantageous for monitoring large or irregular areas.

[0069] The camera group includes at least one camera, positioned around the perimeter of the monitored area. The camera provides a multi-angle ground view, scanning the monitored area and providing information at different orientations and depths, facilitating accurate three-dimensional reconstruction of the target area. It should be understood that the term "camera" in the present invention may also refer to a camera head, a video camera, or the like, and this is not intended to be limiting.

[0070] It is understandable that the shooting equipment group may include one camera, or multiple cameras, or one drone, or multiple drones, or one camera plus one drone, or one camera plus multiple drones, etc.

[0071] The data processing unit is the core part, responsible for processing the time series images transmitted by the shooting equipment group, performing deformation monitoring analysis through the maximum cross-correlation algorithm, height compensation algorithm, similarity transformation algorithm and Moire fringe method, and finally outputting the deformation monitoring results.

[0072] The maximum cross-correlation algorithm is used to extract the center coordinate trajectory of each target (including deformation monitoring targets and control targets). This algorithm can be used to compare continuous time series images to extract target position changes within the image. The target position in each frame is calculated by cross-correlating it with the template image, thereby obtaining the target center coordinate trajectory.

[0073] The height compensation algorithm corrects image coordinate transformation errors caused by the height difference (vertical displacement) of the deformation monitoring target relative to the reference surface. Adjusting the position of the deformation monitoring target in the image coordinate system eliminates geometric distortion caused by vertical displacement, ensuring the accuracy of the deformation monitoring target's coordinates in the image, thereby improving the accuracy of the similarity transformation matrix when correcting the deformation monitoring target's coordinates.

[0074] In the process of correcting the center coordinates of the deformation monitoring target in the time series image by using the height compensation algorithm, the projection center coordinates of the deformation monitoring target on the reference plane after correction can be determined based on the center coordinates of multiple control targets in the time series image and the height difference (vertical displacement) of the deformation monitoring target relative to the reference plane.

[0075] Specifically, the height difference (vertical displacement) of the target object relative to the reference plane will cause the projection position of the target point in the image to shift. In order to eliminate the image coordinate offset caused by the height difference, the coordinates of the target point are highly compensated to make it more accurate when calculating the similarity transformation. It can be calculated by the following formula:

[0076]

[0077] in, It's the height difference, is the distance from the camera to the reference plane, The target point in the image coordinate, The target point in the image coordinate. The reference point in the image coordinate, The reference point in the image coordinate.

[0078] The similarity transformation algorithm is used to correct time-series images, eliminating errors caused by camera motion (such as rotation, scaling, and translation). The algorithm calculates the change in the center position of the control target within each frame. Based on the center coordinates of the control target, geometric transformations (rotation, translation, and scaling) of the image are used to compensate for this, resulting in an accurate target trajectory in a unified coordinate system.

[0079] The moiré fringe method can be used to calculate the displacement change of a control target and correct the control target coordinates based on a corrected time-series image. The moiré fringe method extracts the control target's fringe information from the image, calculates the phase difference, and thus infers the displacement change of the control target. Deformed objects cause changes in the fringe pattern, and the phase difference in the fringe patterns allows accurate displacement calculation, making it particularly suitable for precise displacement measurement.

[0080] Through the Moire fringe method, the displacement change of the control target can be calculated based on the corrected time series images and the control target coordinates can be corrected to obtain the corrected coordinates of the control target. Then, the similarity transformation algorithm is used to finely compensate the position of the deformation monitoring target in the corrected time series images (i.e., the time series images processed by the first similarity transformation) according to the corrected coordinates of the control target. Finally, the displacement change of the deformation monitoring target can be calculated based on the finely compensated time series images and the displacement change of the deformation monitoring target and the control target baseline.

[0081] Finally, the data processing unit determines the deformation monitoring results based on the calculated displacement changes of the deformation monitoring targets. By comprehensively analyzing the displacement changes of the deformation monitoring targets, the data processing unit can output the deformation monitoring results of the target deformable body in real time. Combined with the spatial position and deformation process of the deformation monitoring targets, the overall deformation of the target deformable body can be monitored, analyzed, and predicted.

[0082] The deformation monitoring system based on phase information provided by the present invention comprises a target deformable body, multiple deformation monitoring targets and multiple control targets, and a shooting device group collects time series images of the monitoring area and transmits the time series images to a data processing unit. The data processing unit extracts the center coordinates of each target from the time series images by a maximum cross-correlation algorithm, corrects the center coordinates of the deformation monitoring targets in the time series images by a height compensation algorithm, corrects the time series images after the center coordinates of the deformation monitoring targets are corrected by a similarity transformation algorithm according to the center coordinates of the control targets, calculates the displacement change of the control targets according to the corrected time series images by a moiré fringe method and obtains the corrected coordinates of the control targets, and then finely compensates the positions of the deformation monitoring targets in the corrected time series images according to the corrected coordinates of the control targets. Finally, the displacement change of the deformation monitoring targets is calculated according to the finely compensated time series images to obtain the deformation monitoring results of the target deformable body, so as to achieve high-precision deformation monitoring of the target deformable body at a low cost.

[0083] According to the present invention, a deformation monitoring system based on phase information is provided, which extracts the center coordinates of deformation monitoring targets and control targets from time series images using a maximum cross-correlation algorithm, including:

[0084] The initial frame image in the time series image is used as a template image, and the cross-correlation function between each subsequent frame image in the time series image and the template image is calculated;

[0085] Based on the maximum correlation value in the cross-correlation function between each subsequent frame image and the template image, the center coordinates of the deformation monitoring target and the control target in each subsequent frame image are determined.

[0086] Specifically, continuous time series images contain the image features of the deformation monitoring target and the control target. These images are arranged in chronological order, and each frame of the image corresponds to the deformation monitoring target and the control target image at a different time point. By analyzing these images, the movement trajectory of the deformation monitoring target and the control target at different time points can be tracked.

[0087] In the process of extracting the center coordinates of the deformation monitoring target and the control target based on the time series image, the initial frame image can be selected as the template image ,Usually this image contains obvious feature points or stripe patterns, which are used for subsequent image matching.,The template image should have sufficient identification features to ensure the,accuracy of the matching process.

[0088] By maximizing the cross-correlation algorithm, the subsequent frame images in the time series image can be used as input images , with the template image Perform comparison and calculate cross-correlation function The cross-correlation function is used to measure the similarity between the template image and the input image, and the positions of the deformation monitoring target and the control target center are determined by finding the position of the maximum correlation value.

[0089] Cross-correlation function It can be calculated by Fourier transform. The specific steps are as follows:

[0090] For a given template image and the input image , perform two-dimensional discrete Fourier transform, and get and :

[0091]

[0092] Cross-correlation function Calculated by inverse Fourier transform:

[0093]

[0094] in, yes The complex conjugate of and is the dimension of the image.

[0095] In order to simplify the optimization objective, facilitate calculation and find the optimal solution, find the cross-correlation function Maximized displacement , for each frame image, define the objective function The formula is as follows:

[0096]

[0097] Calculating constant factors :

[0098]

[0099] Displacement parameters determined by maximizing cross-correlation and , the center coordinates of the deformation monitoring target and the control target in each frame of the image can be accurately recorded. The center coordinates of the deformation monitoring target and the control target can be expressed as ,in Indicates a specific mark. Indicates the number of video frames.

[0100] According to a phase information-based deformation monitoring system provided by the present invention, a time series image after the center coordinates of the deformation monitoring target are corrected is corrected according to the center coordinates of the control target by using a similarity transformation algorithm, including:

[0101] A reference coordinate system is determined based on the center coordinates of multiple control targets in the template image and a predetermined reference control target; the reference control target is used to provide a unified spatial reference for control targets other than the reference control target in the multiple control targets;

[0102] Calculating similarity transformation parameters based on the center coordinates of the control target in the reference coordinate system and the center coordinates of the control target in the time series image after correcting the center coordinates of the deformation monitoring target;

[0103] Based on the similarity transformation parameters, the time series images after the central coordinates of the deformation monitoring target are corrected are corrected.

[0104] Specifically, a similarity transformation algorithm is used to correct time-series images. This aims to eliminate motion errors (such as rotation, scaling, and translation) that may be introduced by the camera or other equipment during the capture process, thereby improving the accuracy of deformation monitoring. The similarity transformation algorithm adjusts the target coordinates in the image to align with the reference coordinate system, compensating for the effects of equipment motion.

[0105] The following similarity transformation formula can be used to mark the center position for compensation:

[0106]

[0107] in, is the scaling factor, is the rotation angle, are the translation components in the x and y directions respectively. is the position of the point in the transformed coordinate system; is the point position in the original coordinate system.

[0108] First, a reference coordinate system must be selected as a baseline. In embodiments of the present invention, this reference coordinate system can be determined based on the center coordinates of multiple control targets in the template image and a predetermined baseline control target. This predetermined baseline control target serves as a unified spatial reference for all control targets except the baseline, thereby constructing a complete monitoring target network.

[0109] In some embodiments, the number of control targets is three or more, and these control targets form an L-shaped structure. The control target at the intersection of the L-shaped structure can be set as the benchmark control target, which serves as a unified spatial reference for other control targets to construct a complete monitoring target network and determine the reference coordinate system.

[0110] For the center coordinates of the control target in each frame image, the similarity transformation parameters (i.e., scaling factor, rotation angle, and translation component) can be calculated by comparing the position of the control target in the current frame image with the target position in the reference coordinate system.

[0111] For example, the rotation angle can be obtained by calculating the angle difference between the coordinates of the control target in the current frame image and the coordinates of the target in the reference coordinate system. ; Calculate the scaling factor by comparing the actual size of the control target in the current frame image with the size of the target in the reference coordinate system ; The translation component is obtained by calculating the difference between the reference coordinate system and the center of the control target in the current frame image .

[0112] Then, the coordinates in the original image can be subjected to similarity transformation using the similarity transformation parameters calculated above to obtain corrected coordinates, thereby completing the correction process of the time series image after correcting the central coordinates of the deformation monitoring target.

[0113] Through these steps, the errors caused by equipment movement can be effectively eliminated, and a more accurate target displacement trajectory can be obtained, thereby improving the accuracy of deformation monitoring.

[0114] According to a phase information-based deformation monitoring system provided by the present invention, a displacement change of a control target is calculated based on a corrected time series image by a moire fringe method and a corrected coordinate of the control target is obtained, comprising:

[0115] Extracting multi-frame moiré fringe images based on the corrected time series images;

[0116] Performing low-pass filtering and downsampling processing on the multiple-frame moiré fringe images to obtain multiple-frame downsampled images;

[0117] Based on the moiré fringe phase of multiple frames of downsampled images, the displacement change of the control target is calculated and the correction coordinates of the control target are obtained.

[0118] Specifically, on the target surface, moiré fringes are generated by optical interference, and deformation causes changes in the fringes. To further improve the accuracy of high-precision submillimeter displacement within a plane, the present invention employs a moiré fringe method. This method uses low-pass filtering, downsampling, and phase analysis to extract the phase difference between the moiré fringes before and after deformation and calculate the plane displacement.

[0119] In the original moiré fringe image, the intensity distribution of the moiré fringe It can be expressed by the following formula:

[0120]

[0121] in, is the amplitude of the fringe intensity, is the background intensity, is the fringe period (in pixels), is the initial phase. In the experiment, the fringe period Usually the periodicity is the same in the x and y directions, which is a unified periodicity parameter.

[0122] After extracting multiple frames of moiré fringe images, the original moiré fringe images can be low-pass filtered and downsampled. The image may contain noise and high-frequency interference, such as uneven lighting, complex background, etc.

[0123] In order to extract the main components of the fringes, the moiré fringe image is first low-pass filtered to remove high-frequency noise and retain the main low-frequency information of the fringes. The intensity distribution of the moiré fringe image after low-pass filtering can be obtained. express.

[0124]

[0125] in and represent Fourier transform and inverse Fourier transform respectively, is the transfer function of the low-pass filter. By using a Gaussian low-pass filter:

[0126]

[0127] Where, is the cutoff frequency.

[0128] The moiré fringe image after low-pass filtering (through its intensity distribution The purpose of downsampling is to use multiple sampling points to obtain the phase distribution. The specific formula is:

[0129]

[0130] is an integer number of sampling points, is the phase distribution of the moiré fringes.

[0131] After obtaining the downsampled image, the phase distribution of the moiré fringes can be extracted using the phase shift technique. The specific calculation formula is:

[0132]

[0133] Phase difference and plane displacement calculation, the phase difference of the moiré fringe in the image before and after target deformation Defined as:

[0134]

[0135] is the phase distribution of the deformed moiré fringes, is the phase distribution of the moiré fringes before deformation.

[0136] Finally, the displacement change in the plane can be directly calculated by the phase difference :

[0137]

[0138] The high-precision measurement of plane displacement can be achieved by sampling the moiré fringe method, which can provide an accuracy of 1 / 100 pixel and 1 / 1000 fringe period. The high-frequency noise of the fringe image can be removed by low-pass filtering to extract the fringe information. The periodic phase information can be extracted by using multi-frame downsampling of the fringe image. The displacement change of the fringe before and after deformation is calculated by the phase difference formula, and the corrected coordinates of the control target are obtained. It should be noted that if the target is three-dimensional, or the deformation of the deformed body has a height component, the three-dimensional displacement of the target can be calculated by combining the spatial position of the target (such as through measurement by a camera and a drone). :

[0139]

[0140] , and are the displacements along the X, Y, and Z directions, respectively, calculated by the algorithm.

[0141] The deformation monitoring method based on phase information provided by the present invention is described below. The deformation monitoring method based on phase information described below and the deformation monitoring system based on phase information described above can be referenced to each other.

[0142] Figure 2 FIG. 1 is a flow chart of a deformation monitoring method based on phase information provided by the present invention, such as Figure 2 As shown, the method is applied to any deformation monitoring system based on phase information as described in the previous system embodiment, and includes the following steps:

[0143] Step 200: Acquire time series images of the monitoring area; the monitoring area is composed of a target deformable body, a plurality of deformation monitoring targets, and a plurality of control targets.

[0144] Step 201 : Extract the center coordinates of the deformation monitoring target and the control target based on the time series image by using a maximum cross-correlation algorithm.

[0145] Step 202: Correct the center coordinates of the deformation monitoring target in the time series image using a height compensation algorithm.

[0146] Step 203: Correct the time series images after correcting the center coordinates of the deformation monitoring target according to the center coordinates of the control target using a similarity transformation algorithm.

[0147] Step 204 : Calculate the displacement variation of the control target based on the corrected time series images using the Moire fringe method and obtain the corrected coordinates of the control target.

[0148] Step 205 : Using a similarity transformation algorithm, fine compensation is performed on the position of the deformation monitoring target in the corrected time series image according to the corrected coordinates of the control target.

[0149] Step 206: Determine the deformation monitoring result of the target deformable body based on the finely compensated time series images.

[0150] Specifically, the target deformable body is a deformable body whose deformation degree needs to be monitored. The phase information-based deformation monitoring system provided by the present invention includes a shooting equipment group, multiple deformation monitoring targets, multiple control targets and a data processing unit, which work together to obtain the deformation monitoring results of the target deformable body.

[0151] In embodiments of the present invention, multiple deformation monitoring targets are provided, arranged on the surface of a target deformable object. These targets possess specific geometric shapes and reflective properties, enabling the capture of high-precision image data using a camera system. Positional changes in these targets can reflect the deformation process of the target deformable object. The number of these targets can be determined based on the target deformable object's area and the complexity of its internal motion.

[0152] In this embodiment of the present invention, multiple control targets are provided. These control targets are positioned around the target deformable body, primarily to provide a spatial reference for the deformation monitoring targets. It is understood that the positions of the control targets should be stable so that they remain fixed in the image. The relative positions and motion trajectories of the deformation monitoring targets can be accurately calibrated from the perspectives of the camera and drone group.

[0153] The monitoring area consists of the target deformable body, deformation monitoring targets, and control targets. This area is covered by the camera's lens. All targets (deformation monitoring targets and control targets) should be placed within this area to ensure that the camera can fully capture the changes in the deformable body.

[0154] First, a deformation monitoring system based on phase information requires acquiring time-series images of the monitored area. These images can be collected synchronously at preset intervals, which can be flexibly adjusted based on the sliding speed and risk level of the target deformable object. When monitoring deformation in three dimensions using multiple cameras or drones, time tags can be used to synchronize photos or videos.

[0155] After obtaining the time series images, the time series images can be processed and deformation monitoring analysis can be performed through the maximum cross-correlation algorithm, height compensation algorithm, similarity transformation algorithm and Moire fringe method, and finally the deformation monitoring results can be output.

[0156] The maximum cross-correlation algorithm is used to extract the center coordinate trajectory of each target (including deformation monitoring targets and control targets). This algorithm can be used to compare continuous time series images to extract target position changes within the image. The target position in each frame is calculated by cross-correlating it with the template image, thereby obtaining the target center coordinate trajectory.

[0157] The height compensation algorithm corrects image coordinate transformation errors caused by the height difference (vertical displacement) of the deformation monitoring target relative to the reference surface. Adjusting the position of the deformation monitoring target in the image coordinate system eliminates geometric distortion caused by vertical displacement, ensuring the accuracy of the deformation monitoring target's coordinates in the image, thereby improving the accuracy of the similarity transformation matrix when correcting the deformation monitoring target's coordinates.

[0158] In the process of correcting the center coordinates of the deformation monitoring target in the time series image by using the height compensation algorithm, the projection center coordinates of the deformation monitoring target on the reference plane after correction can be determined based on the center coordinates of multiple control targets in the time series image and the height difference (vertical displacement) of the deformation monitoring target relative to the reference plane.

[0159] Specifically, the height difference (vertical displacement) of the target object relative to the reference plane will cause the projection position of the target point in the image to shift. In order to eliminate the image coordinate offset caused by the height difference, the coordinates of the target point are highly compensated to make it more accurate when calculating the similarity transformation. It can be calculated by the following formula:

[0160]

[0161] in, It's the height difference, is the distance from the camera to the reference plane, The target point in the image coordinate, The target point in the image coordinate. The reference point in the image coordinate, The reference point in the image coordinate.

[0162] The similarity transformation algorithm is used to correct time-series images, eliminating errors caused by camera motion (such as rotation, scaling, and translation). The algorithm calculates the change in the center position of the control target within each frame. Based on the center coordinates of the control target, geometric transformations (rotation, translation, and scaling) of the image are used to compensate for this, resulting in an accurate target trajectory in a unified coordinate system.

[0163] The moiré fringe method can be used to calculate the displacement change of a control target and correct the control target coordinates based on a corrected time-series image. The moiré fringe method extracts the control target's fringe information from the image, calculates the phase difference, and thus infers the displacement change of the control target. Deformed objects cause changes in the fringe pattern, and the phase difference in the fringe patterns allows accurate displacement calculation, making it particularly suitable for precise displacement measurement.

[0164] Through the Moire fringe method, the displacement change of the control target can be calculated based on the corrected time series images and the control target coordinates can be corrected to obtain the corrected coordinates of the control target. Then, the similarity transformation algorithm is used to finely compensate the position of the deformation monitoring target in the corrected time series images (i.e., the time series images processed by the first similarity transformation) according to the corrected coordinates of the control target. Finally, the displacement change of the deformation monitoring target can be calculated based on the finely compensated time series images and the displacement change of the deformation monitoring target and the control target baseline.

[0165] Finally, the deformation monitoring results can be determined based on the calculated displacement changes of the deformation monitoring targets. By comprehensively analyzing the displacement changes of the deformation monitoring targets, the deformation monitoring results of the target deformable body can be output in real time. Combined with the spatial position and deformation process of the deformation monitoring targets, the overall deformation of the target deformable body can be monitored, analyzed, and predicted.

[0166] The deformation monitoring method based on phase information provided by the present invention comprises a target deformable body, multiple deformation monitoring targets and multiple control targets, and a shooting device group collects time series images of the monitoring area and transmits the time series images to a data processing unit. The data processing unit extracts the center coordinates of each target from the time series images using a maximum cross-correlation algorithm, corrects the center coordinates of the deformation monitoring targets in the time series images using a height compensation algorithm, corrects the time series images after the center coordinates of the deformation monitoring targets are corrected using a similarity transformation algorithm based on the center coordinates of the control targets, calculates the displacement change of the control targets based on the corrected time series images using a moiré fringe method and obtains the corrected coordinates of the control targets, then finely compensates the positions of the deformation monitoring targets in the corrected time series images based on the corrected coordinates of the control targets to obtain finely compensated time series images, and finally calculates the displacement change of the deformation monitoring targets based on the finely compensated time series images to obtain the deformation monitoring results of the target deformable body. High-precision deformation monitoring of the target deformable body can be achieved at a low cost.

[0167] According to a phase information-based deformation monitoring method provided by the present invention, the center coordinates of the deformation monitoring target and the control target are extracted from the time series images by using a maximum cross-correlation algorithm, including:

[0168] The initial frame image in the time series image is used as a template image, and the cross-correlation function between each subsequent frame image in the time series image and the template image is calculated;

[0169] Based on the maximum correlation value in the cross-correlation function between each subsequent frame image and the template image, the center coordinates of the deformation monitoring target and the control target in each subsequent frame image are determined.

[0170] Specifically, continuous time series images contain the image features of the deformation monitoring target and the control target. These images are arranged in chronological order, and each frame of the image corresponds to the deformation monitoring target and the control target image at a different time point. By analyzing these images, the movement trajectory of the deformation monitoring target and the control target at different time points can be tracked.

[0171] In the process of extracting the center coordinates of the deformation monitoring target and the control target based on the time series image, the initial frame image can be selected as the template image ,Usually this image contains obvious feature points or stripe patterns, which are used for subsequent image matching.,The template image should have sufficient identification features to ensure the,accuracy of the matching process.

[0172] By maximizing the cross-correlation algorithm, the subsequent frame images in the time series image can be used as input images , with the template image Perform comparison and calculate cross-correlation function The cross-correlation function is used to measure the similarity between the template image and the input image, and the positions of the deformation monitoring target and the control target center are determined by finding the position of the maximum correlation value.

[0173] Cross-correlation function It can be calculated by Fourier transform. The specific steps are as follows:

[0174] For a given template image and the input image , perform two-dimensional discrete Fourier transform, and get and :

[0175]

[0176] Cross-correlation function Calculated by inverse Fourier transform:

[0177]

[0178] in, yes The complex conjugate of and is the dimension of the image.

[0179] In order to simplify the optimization objective, facilitate calculation and find the optimal solution, find the cross-correlation function Maximized displacement , for each frame image, define the objective function The formula is as follows:

[0180]

[0181] Calculating constant factors :

[0182]

[0183] Displacement parameters determined by maximizing cross-correlation and , the center coordinates of the deformation monitoring target and the control target in each frame of the image can be accurately recorded. The center coordinates of the deformation monitoring target and the control target can be expressed as ,in Indicates a specific mark. Indicates the number of video frames.

[0184] According to a deformation monitoring method based on phase information provided by the present invention, a time series image after the center coordinates of the deformation monitoring target are corrected is corrected according to the center coordinates of the control target by using a similarity transformation algorithm, including:

[0185] A reference coordinate system is determined based on the center coordinates of multiple control targets in the template image and a predetermined reference control target; the reference control target is used to provide a unified spatial reference for control targets other than the reference control target in the multiple control targets;

[0186] Calculating similarity transformation parameters based on the center coordinates of the control target in the reference coordinate system and the center coordinates of the control target in the time series image after correcting the center coordinates of the deformation monitoring target;

[0187] Based on the similarity transformation parameters, the time series images after the central coordinates of the deformation monitoring target are corrected are corrected.

[0188] Specifically, a similarity transformation algorithm is used to correct time-series images. This aims to eliminate motion errors (such as rotation, scaling, and translation) that may be introduced by the camera or other equipment during the capture process, thereby improving the accuracy of deformation monitoring. The similarity transformation algorithm adjusts the target coordinates in the image to align with the reference coordinate system, compensating for the effects of equipment motion.

[0189] The following similarity transformation formula can be used to mark the center position for compensation:

[0190]

[0191] in, is the scaling factor, is the rotation angle, are the translation components in the x and y directions respectively. is the position of the point in the transformed coordinate system; is the point position in the original coordinate system.

[0192] First, a reference coordinate system must be selected as a baseline. In embodiments of the present invention, this reference coordinate system can be determined based on the center coordinates of multiple control targets in the template image and a predetermined baseline control target. This predetermined baseline control target serves as a unified spatial reference for all control targets except the baseline, thereby constructing a complete monitoring target network.

[0193] In some embodiments, the number of control targets is three or more, and these control targets form an L-shaped structure. The control target at the intersection of the L-shaped structure can be set as the benchmark control target, which serves as a unified spatial reference for other control targets to construct a complete monitoring target network and determine the reference coordinate system.

[0194] For the center coordinates of the control target in each frame image, the similarity transformation parameters (i.e., scaling factor, rotation angle, and translation component) can be calculated by comparing the position of the control target in the current frame image with the target position in the reference coordinate system.

[0195] For example, the rotation angle can be obtained by calculating the angle difference between the coordinates of the control target in the current frame image and the coordinates of the target in the reference coordinate system. ; Calculate the scaling factor by comparing the actual size of the control target in the current frame image with the size of the target in the reference coordinate system ; The translation component is obtained by calculating the difference between the reference coordinate system and the center of the control target in the current frame image .

[0196] Then, the coordinates in the original image can be subjected to similarity transformation using the similarity transformation parameters calculated above to obtain corrected coordinates, thereby completing the correction process of the time series image after correcting the central coordinates of the deformation monitoring target.

[0197] Through these steps, the errors caused by equipment movement can be effectively eliminated, and a more accurate target displacement trajectory can be obtained, thereby improving the accuracy of deformation monitoring.

[0198] According to a deformation monitoring method based on phase information provided by the present invention, the displacement change of the control target is calculated based on the corrected time series images by using the Moire fringe method and the corrected coordinates of the control target are obtained, including:

[0199] Extracting multi-frame moiré fringe images based on the corrected time series images;

[0200] Performing low-pass filtering and downsampling processing on the multiple-frame moiré fringe images to obtain multiple-frame downsampled images;

[0201] Based on the moiré fringe phase of multiple frames of downsampled images, the displacement change of the control target is calculated and the correction coordinates of the control target are obtained.

[0202] Specifically, on the target surface, moiré fringes are generated by optical interference, and deformation causes changes in the fringes. To further improve the accuracy of high-precision submillimeter displacement within a plane, the present invention employs a moiré fringe method. This method uses low-pass filtering, downsampling, and phase analysis to extract the phase difference between the moiré fringes before and after deformation and calculate the plane displacement.

[0203] In the original moiré fringe image, the intensity distribution of the moiré fringe It can be expressed by the following formula:

[0204]

[0205] in, is the amplitude of the fringe intensity, is the background intensity, is the fringe period (in pixels), is the initial phase. In the experiment, the fringe period Usually the periodicity is the same in the x and y directions, which is a unified periodicity parameter.

[0206] After extracting multiple frames of moiré fringe images, the original moiré fringe images can be low-pass filtered and downsampled. The image may contain noise and high-frequency interference, such as uneven lighting, complex background, etc.

[0207] In order to extract the main components of the fringes, the moiré fringe image is first low-pass filtered to remove high-frequency noise and retain the main low-frequency information of the fringes. The intensity distribution of the moiré fringe image after low-pass filtering can be obtained. express.

[0208]

[0209] in and represent Fourier transform and inverse Fourier transform respectively, is the transfer function of the low-pass filter. By using a Gaussian low-pass filter:

[0210]

[0211] Where, is the cutoff frequency.

[0212] The moiré fringe image after low-pass filtering (through its intensity distribution The purpose of downsampling is to use multiple sampling points to obtain the phase distribution. The specific formula is:

[0213]

[0214] is an integer number of sampling points, is the phase distribution of the moiré fringes.

[0215] After obtaining the downsampled image, the phase distribution of the moiré fringes can be extracted using the phase shift technique. The specific calculation formula is:

[0216]

[0217] Phase difference and plane displacement calculation, the phase difference of the moiré fringe in the image before and after target deformation Defined as:

[0218]

[0219] is the phase distribution of the deformed moiré fringes, is the phase distribution of the moiré fringes before deformation.

[0220] Finally, the displacement change in the plane can be directly calculated by the phase difference :

[0221]

[0222] The moiré fringe sampling method achieves high-precision measurement of planar displacement, providing accuracy of 1 / 100 pixel and 1 / 1000 fringe period. Low-pass filtering removes high-frequency noise from the fringe image to extract fringe information. Using multiple frames of downsampled fringe images, the periodic phase information is extracted. The phase difference formula is used to calculate the displacement change of the fringe before and after deformation, thereby obtaining the corrected coordinates of the control target.

[0223] It should be noted that if the target is three-dimensional, or the deformation of the deformable body has a height component, the three-dimensional displacement of the target can be calculated by combining the spatial position of the target (such as through measurements of cameras and drones). :

[0224]

[0225] , and are the displacements along the X, Y, and Z directions, respectively, calculated by the algorithm.

[0226] The following further illustrates the phase information-based deformation monitoring system and method provided by the present invention through embodiments in specific application scenarios.

[0227] This embodiment uses target tracking, similarity transformation and moiré fringe technology to build a high-precision three-dimensional deformation monitoring integrated algorithm to achieve high-precision real-time monitoring of deformation at low cost. Figure 3-Figure 9 The specific implementation of this embodiment is described in detail.

[0228] 1. System design:

[0229] Figure 3 It is a structural diagram of the deformation monitoring system provided by the present invention, such as Figure 3 As shown, the deformation monitoring system of this embodiment mainly consists of the following parts (where 110 represents the main direction of landslide sliding):

[0230] 1. Camera

[0231] The camera is a high-resolution monitoring camera or a high-definition camera with full-color or color imaging. The camera's shooting range includes all targets for control points and deformation monitoring, such as Figure 3 The shooting range of the medium- and high-resolution monitoring camera 1 (310) is between 311 and 313, and the shooting range of the high-resolution monitoring camera 2 (320) is between 321 and 323; the single-camera monitoring point control targets are not less than two, and the dual-camera monitoring point control targets are not less than three; the main optical axes of the dual cameras are arranged approximately orthogonally, as shown in the figure, the main optical axis 312 of the high-resolution monitoring camera 1 (310) is approximately orthogonal to the main optical axis 322 of the high-resolution monitoring camera 2 (320), and the camera focal length is infinite. When two or more cameras monitor three-dimensional spatial deformation, photos or videos are synchronized through time tags, and the image data is transmitted to the control center in real time.

[0232] In this embodiment, high-resolution dual monitoring cameras are used. The high-resolution monitoring cameras are respectively arranged in the peripheral area of the deformable body, and the main optical axes are arranged approximately orthogonally, such as Figure 3 The camera uses a 24mm equivalent focal length lens and an imaging unit with a 4k resolution, which can achieve high-precision image data acquisition.

[0233] 2. Drone monitoring module

[0234] The drone adopts a lightweight design and has the ability to shoot high-definition images. The drone hovers above the survey area and collects orthophoto images to cover the entire monitoring area. Figure 3 The acquisition range of the UAV (410) can be represented by 411, 412 and 413. The UAV can independently perform two-dimensional displacement monitoring, or it can cooperate with ground cameras to achieve air-ground integrated three-dimensional monitoring.

[0235] 3. Deformation monitoring targets and control targets

[0236] Deformation monitoring targets are arranged on the surface of the deformable body, and their number is determined according to the deformation characteristics of the deformable body and the required spatial resolution. The monitoring board adopts a two-dimensional plane design, and the target adopts a two-dimensional rectangular or three-dimensional cubic design. The surface is set with a black or colored equidistant regular dot matrix, and the dot shape is polygonal or circular. The printed material of the dots can be a reflective material that can be used day and night. The deformation monitoring targets and control targets adopt a single-sided or double-sided design in two dimensions and a three-dimensional layout in three dimensions. They are directly drawn on the surface of the monitored object, and each surface uses the same pattern design. All deformation monitoring targets are arranged in the overlapping shooting area of cameras, video cameras and drones to ensure complete coverage of the monitoring area and high-precision displacement measurement.

[0237] In this embodiment, multiple three-dimensional deformation monitoring targets are arranged on the surface of the deformable body, such as Figure 3The deformation monitoring target 1 (210), deformation monitoring target 2 (211), deformation monitoring target 3 (212) and deformation monitoring target 4 (213) are provided with a black or colored equidistant regular dot matrix on the surface. The dot shape is polygonal or circular. The printing material of the dot can be a reflective material that is commonly used during the day and night. The target spacing is set to 100 mm.

[0238] The control target is arranged in the area outside the deformable body to correct the imaging deviation between the camera and the deformation monitoring target, providing a reliable benchmark for deformation calculation.

[0239] The control targets can be placed in the stable area of the camera's orthogonal direction, with the middle control target as the coordinate origin. The control targets on both sides are arranged in the extension direction of the middle control target, forming a mutually orthogonal structural layout with the middle control target. The three control targets form an "L" shaped frame, such as Figure 3 Control target 1 (220), control target 2 (221) and control target 3 (222) in.

[0240] Each camera, camera head, and drone can accurately calibrate the deformation monitoring trajectory through three control targets, while ensuring that each device can independently complete the displacement change monitoring within the plane. Dual cameras or dual cameras working together can achieve comprehensive three-dimensional displacement monitoring.

[0241] Figure 4 This is a schematic diagram of the imaging principle of the reflective mark provided by the present invention. Figure 5 This is a schematic diagram of the relationship between the square deformation of the reflective marker and the square deformation of the photo image provided by the present invention. Figure 6 This is a schematic diagram of the target plane polygonal and circular designs provided by the present invention. Figure 7 The present invention provides a schematic diagram of projecting a target based on height compensation onto a reference surface. Figure 4-Figure 7 It can be seen that calibration can be performed based on the control targets in the images obtained by the high-resolution dual monitoring cameras and the UAV monitoring module, and the deformation data of the deformable body can be obtained based on the deformation monitoring targets.

[0242] 4. Control Center

[0243] The control of the camera, storage of photos, and deformation calculation are all completed on the computer in the control center. The control center communicates with the camera and drone through the Internet of Things. It is mainly responsible for sending acquisition instructions, receiving image data, calculating the three-dimensional displacement of the monitoring points, and generating real-time deformation information.

[0244] 2. Implementation steps:

[0245] The specific usage process of the deformation monitoring system of this embodiment is as follows:

[0246] 1. Equipment layout

[0247] 1.1 Camera Layout

[0248] Two cameras are placed in the peripheral area of the deformable body, and the camera focal length is adjusted to cover all monitoring points.

[0249] Make sure the principal optical axes of the two cameras are approximately perpendicular.

[0250] 1.2 Deformation monitoring target layout

[0251] The insertion or buried fixation method is adopted, and the number of deformation monitoring targets is determined according to the area of the deformed body and the complexity of the internal movement.

[0252] 1.3 UAV-assisted deployment

[0253] The drone hovered above the survey area to ensure that the orthophoto covered all monitoring areas.

[0254] 2. Image acquisition

[0255] 2.1 Collection Strategy

[0256] The camera and drone synchronously collect images at preset time intervals, and the time interval is flexibly adjusted according to the sliding speed and danger level of the deformable body.

[0257] Fast Deformation: It is recommended to set a 1-second interval or directly shoot the video to capture continuous dynamic changes.

[0258] Slow Warp: Can be extended to 5-10 seconds or take videos at regular intervals.

[0259] 2.2 Data Management

[0260] The captured images are named according to time tags and device tags and transmitted to the control center in real time.

[0261] 3. Deformation calculation

[0262] The control center processes the image through an embedded algorithm and calculates the three-dimensional displacement of the deformation monitoring point.

[0263] 3.1 Target Tracking

[0264] The maximum cross-correlation technology is used to extract the pixel trajectory of the monitoring point in the time series image and obtain the pixel change.

[0265] 3.2 Altitude compensation correction

[0266] The projection center coordinates of the deformation monitoring target on the reference surface are corrected through the height compensation algorithm.

[0267] 3.3 Similarity Transformation Correction

[0268] Correct the image's rotation, scaling, and translation caused by camera motion or perspective changes to ensure data accuracy.

[0269] 3.4 Moire fringe technology assisted correction

[0270] Figure 8 Schematic diagram of the principle of the sampling moiré method for accurate displacement measurement provided by the present invention, as shown in FIG. Figure 8 As shown in Figure 2, the displacement change of the control mark can be obtained by extracting the phase information of the control mark. Before deformation, the intensity distribution of the moiré fringe in the original moiré fringe image is: , obtained through low-pass filtering , and then obtain the phase distribution of the moiré fringes (moiré phase) ; Correspondingly, after deformation, the intensity distribution of the moiré fringes in the original moiré fringe image is , obtained through low-pass filtering , and then obtain the phase distribution of the moiré fringes (moiré phase) ; Get the phase difference of the moiré fringes in the image before and after deformation , and then calculate the displacement change in the plane .

[0271] 3.5 X, Y displacement calculation

[0272] By performing similarity transformation and ultra-fine image compensation again, the displacement of the deformation monitoring target in the X and Y directions is finally accurately calculated.

[0273] 3.6 Three-dimensional displacement solution

[0274] Based on the above results, the three-dimensional displacement of the monitoring point is calculated through multi-view observation of two cameras. :

[0275]

[0276] 4. Result output

[0277] 4.1 Real-time monitoring information

[0278] The control center generates deformation time series data and displays the deformation process in real time in the form of curve graphs, heat maps or numerical tables.

[0279] 4.2 Accuracy Verification

[0280] At a monitoring distance of 35 meters, based on a 24 mm focal length camera, 4K resolution images and 100 mm marker spacing, the deformation monitoring accuracy of this system can reach sub-millimeter level.

[0281] 3. Effect verification:

[0282] This embodiment demonstrates a high-precision monitoring effect in experiments.

[0283] Three-dimensional deformation monitoring: Experimental verification shows that this embodiment can accurately monitor the displacement changes of the deformation monitoring target on the surface of the deformable body in the X, Y, and Z directions.

[0284] Compared with traditional methods: Compared with monitoring methods based on stereo photogrammetry, this embodiment significantly reduces computational error and achieves higher monitoring accuracy. By combining target tracking, similarity transformation, and moiré fringe techniques, this embodiment can maintain high accuracy even in complex lighting conditions and on complex deformable surfaces.

[0285] Additional notes:

[0286] Adaptability and scalability: This embodiment allows for flexible adjustment of camera focal length, target point spacing, and monitoring point layout based on the size of the deformation area and monitoring accuracy. It is applicable to scenarios of varying deformation types and complexity.

[0287] Multiple configurations supported: This embodiment supports single or dual cameras, single or dual cameras and drone independent monitoring, and drone and camera or camera collaborative operation.

[0288] Compared with the existing deformation monitoring method based on stereo photogrammetry technology, this embodiment has the following beneficial effects:

[0289] This embodiment provides a phase information-based deformation monitoring system and method, including: deformation monitoring targets arranged on the surface of the deformable body and control targets set in the area surrounding the deformation; the control targets are used for calibration and benchmark reference, and the targets are two-dimensional rectangular planes or three-dimensional cubes, with a surface provided with a black or colored polygonal or circular equidistant regular dot matrix, and the printed material of the dots can be a reflective material that can be used day and night; the system also includes a high-resolution monitoring camera, a high-definition camera, and a drone that hovers directly above the monitoring area.

[0290] Figure 9 FIG. 1 is a flow chart of the algorithm for calculating deformation provided by the present invention. Figure 9As shown, images or videos of the monitored area are captured by cameras, video cameras, and drones. A comprehensive algorithm is formed by combining target tracking, height compensation, similarity transformation correction, and moiré fringe phase analysis. Marker center tracking achieves pixel-level accuracy, while coarse image compensation uses height compensation and similarity transformation to achieve pixel-level accuracy. Reference marker displacement uses a sampled moiré method to achieve 1 / 100 pixel-level accuracy. Ultra-fine image compensation uses similarity transformation to achieve 1 / 100 pixel-level accuracy. Finally, based on reference line displacement calculation, the displacement data of the deformation monitoring point in the X, Y, and Z directions can be accurately calculated, allowing real-time acquisition of three-dimensional deformation information (landslide sliding displacement data) of the deformed body. This embodiment supports a variety of monitoring configurations, including a single high-resolution camera or camera, dual high-resolution cameras or dual cameras, a single drone, and a combination of drones, cameras, and cameras, enabling high-precision deformation monitoring at low cost.

[0291] Furthermore, this embodiment can monitor the deformation process of stable feature points on a deformable object. These feature points, similar to reflective markers, are suitable for environments with good lighting conditions, improving the spatial resolution of deformation monitoring. Through a comprehensive algorithm and the deployment of deformation monitoring targets, this embodiment not only enables high-precision three-dimensional deformation monitoring of deformable objects, but also comprehensively covers the deformation process, providing strong technical support for real-time monitoring and early warning of engineering disasters.

[0292] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0293] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0294] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A deformation monitoring system based on phase information, characterized in that: include: A shooting equipment group, multiple deformation monitoring targets, multiple control targets and a data processing unit; The deformation monitoring target is arranged on the surface of the target deformable body; The control target is set in the peripheral area of the target deformable body and serves as a spatial reference benchmark for the deformation monitoring target; The target deformable body, the plurality of deformation monitoring targets and the plurality of control targets together constitute a monitoring area; The shooting equipment group includes a drone group and / or a camera group, which is used to collect time-series images of the monitoring area and transmit the time-series images to the data processing unit; the drone group includes at least one drone, which is set directly above the monitoring area; the camera group includes at least one camera, which is set in the peripheral area of the monitoring area, and the camera covers the monitoring area by scanning; The data processing unit is used to extract the center coordinates of the deformation monitoring target and the control target according to the time series image by maximizing the cross-correlation algorithm, correct the center coordinates of the deformation monitoring target in the time series image by a height compensation algorithm, correct the time series image after the center coordinates of the deformation monitoring target are corrected according to the center coordinates of the control target by a similarity transformation algorithm, calculate the displacement change of the control target according to the corrected time series image by a Moire fringe method and obtain the corrected coordinates of the control target, finely compensate the position of the deformation monitoring target in the corrected time series image according to the corrected coordinates of the control target by a similarity transformation algorithm, and finally determine the deformation monitoring result of the target deformable body based on the finely compensated time series image.

2. The deformation monitoring system based on phase information according to claim 1, characterized in that: Extracting the center coordinates of the deformation monitoring target and the control target according to the time series image by maximizing the cross-correlation algorithm includes: Using an initial frame image in the time series image as a template image, and calculating a cross-correlation function between each subsequent frame image in the time series image and the template image; Based on the maximum correlation value in the cross-correlation function between each subsequent frame image and the template image, the center coordinates of the deformation monitoring target and the control target in each subsequent frame image are determined.

3. The deformation monitoring system based on phase information according to claim 1 or 2, characterized in that: Correcting the time series images after correcting the center coordinates of the deformation monitoring target according to the center coordinates of the control target by using a similarity transformation algorithm, including: Determining a reference coordinate system based on the center coordinates of a plurality of control targets in the template image and a predetermined reference control target; the reference control target is used for unified spatial reference of control targets other than the reference control target in the plurality of control targets; Calculating similarity transformation parameters based on the central coordinates of the control target in the reference coordinate system and the central coordinates of the control target in the time series image after the central coordinates of the deformation monitoring target are corrected; Based on the similarity transformation parameters, the time series images after the central coordinates of the deformation monitoring target are corrected are corrected.

4. The deformation monitoring system based on phase information according to claim 3, characterized in that: Calculating the displacement change of the control target according to the corrected time series image by using a moire fringe method and obtaining the corrected coordinates of the control target, including: Extracting multiple frames of moiré fringe images based on the corrected time series images; Performing low-pass filtering and downsampling processing on the multiple frames of moiré fringe images to obtain multiple frames of downsampled images; Based on the moiré fringe phases of the multiple frames of downsampled images, the displacement change of the control target is calculated and the corrected coordinates of the control target are obtained.

5. The deformation monitoring system based on phase information according to claim 1, characterized in that: The number of the control targets is three, and the three control targets form an L-shaped structure.

6. The deformation monitoring system based on phase information according to claim 1 or 5, characterized in that: The deformation monitoring target and the control target are two-dimensional rectangular planes or three-dimensional cubes; the surfaces of the deformation monitoring target and the control target are provided with black or colored regularly spaced dots, and the dot shapes of the regularly spaced dots are polygonal or circular; the printing material of the regularly spaced dots is a reflective material that can be used day and night.

7. A deformation monitoring method based on phase information, characterized in that: The phase information-based deformation monitoring system according to any one of claims 1 to 6 comprises: Collecting time series images of a monitoring area; the monitoring area is composed of a target deformable body, a plurality of deformation monitoring targets, and a plurality of control targets; Extracting the center coordinates of the deformation monitoring target and the control target based on the time series image by maximizing the cross-correlation algorithm; Correcting the center coordinates of the deformation monitoring target in the time series image by a height compensation algorithm; Correcting the time series images after correcting the center coordinates of the deformation monitoring target according to the center coordinates of the control target using a similarity transformation algorithm; Calculating the displacement change of the control target and obtaining the corrected coordinates of the control target based on the corrected time series images using a Moire fringe method; Performing fine compensation on the position of the deformation monitoring target in the corrected time series image according to the corrected coordinates of the control target by using a similarity transformation algorithm; Based on the finely compensated time series images, a deformation monitoring result of the target deformable body is determined.

8. The deformation monitoring method based on phase information according to claim 7, characterized in that: Extracting the center coordinates of the deformation monitoring target and the control target according to the time series image by maximizing the cross-correlation algorithm includes: Using an initial frame image in the time series image as a template image, and calculating a cross-correlation function between each subsequent frame image in the time series image and the template image; Based on the maximum correlation value in the cross-correlation function between each subsequent frame image and the template image, the center coordinates of the deformation monitoring target and the control target in each subsequent frame image are determined.

9. The deformation monitoring method based on phase information according to claim 7 or 8, characterized in that: Correcting the time series images after correcting the center coordinates of the deformation monitoring target according to the center coordinates of the control target by a similarity transformation algorithm includes: Determining a reference coordinate system based on the center coordinates of a plurality of control targets in the template image and a predetermined reference control target; the reference control target is used for unified spatial reference of control targets other than the reference control target in the plurality of control targets; Calculating similarity transformation parameters based on the central coordinates of the control target in the reference coordinate system and the central coordinates of the control target in the time series image after the central coordinates of the deformation monitoring target are corrected; Based on the similarity transformation parameters, the time series images after the central coordinates of the deformation monitoring target are corrected are corrected.

10. The deformation monitoring method based on phase information according to claim 9, characterized in that: Calculating the displacement change of the control target and obtaining the corrected coordinates of the control target based on the corrected time series image by using the Moire fringe method includes: Extracting multiple frames of moiré fringe images based on the corrected time series images; Performing low-pass filtering and downsampling processing on the multiple frames of moiré fringe images to obtain multiple frames of downsampled images; Based on the moiré fringe phases of the multiple frames of downsampled images, the displacement change of the control target is calculated and the corrected coordinates of the control target are obtained.

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