General thermal drift suppression method for visual displacement measurement and visual measurement device

Abstract

The invention relates to the technical field of vision measurement, and discloses a general thermal drift suppression method for vision displacement measurement and a vision measurement device.The general thermal drift suppression method for vision displacement measurement comprises the steps that reference information and current information of common imaging of a reference calibration plate and a target object are acquired; obtaining a thermal drift correction constraint model according to the reference information and the current information; and obtaining coordinate information of the target object according to the current information and the thermal drift correction constraint model. According to the invention, the measurement precision in an actual complex environment can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of visual measurement, in particular to a general thermal drift suppression method for visual displacement measurement and a visual measurement device. BACKGROUND

[0002] Visual measurement technology is suitable for structural displacement health monitoring systems of wind power, nuclear power, hoisting equipment, bridge, building, tunnel and other industries and civil infrastructure. Structural displacement is an important monitoring index for evaluating the operation state of the structure, identifying abnormal behavior and ensuring the long-term safe operation of the engineering system, which has significant engineering significance and application value. In recent years, with the rapid development of computer vision technology, image-based non-contact displacement measurement methods have been widely used in many engineering scenes. Such methods have the advantages of non-contact, long distance, high precision, fast response speed and relatively low cost, and have gradually become a research hotspot and development direction in the field of structural health monitoring. With the wide application of visual-based structural displacement measurement technology in engineering monitoring field, how to deal with the fluctuation problem of imaging system performance in complex environment has become a key challenge to improve the measurement accuracy and stability of the system. Especially in long-term outdoor monitoring tasks, the camera system is significantly affected by environmental temperature changes, which is easy to produce image thermal drift error, directly affecting the positioning and tracking accuracy of the target point.

[0003] For the problem of image thermal drift error, existing researches mainly focus on two types of compensation ideas: parameter modeling-based method and data-driven method. Among them, the parameter modeling method assumes that the internal and external parameters of the camera (such as focal length, principal point coordinates, camera pose, etc.) change in a deterministic law with temperature change, and then establishes an analytical function model of thermal drift; while the data-driven method collects a large amount of image point drift data under temperature change, and constructs a statistical mapping relationship between temperature and pixel drift, usually based on machine learning algorithms (such as linear regression, support vector machine, neural network, etc.) to train the model, instead of explicit modeling of complex physical processes.

[0004] However, the existing thermal drift compensation methods often cannot balance between modeling accuracy, method universality and engineering applicability. On the one hand, the parameter model method highly depends on idealized assumptions (such as linear response of thermal stability), which is sensitive to multi-factor interference in the actual environment; on the other hand, the data-driven method can deal with nonlinear changes to a certain extent, but its model generalization ability is limited, and usually needs to be retrained and calibrated for specific cameras, which is difficult to popularize and apply in engineering sites with various types of equipment and dramatic temperature changes.

[0005] Therefore, the prior art still needs to be improved and developed. SUMMARY

[0006] The main purpose of this application is to provide a universal thermal drift suppression method and visual measurement device for visual displacement measurement, aiming to solve the problem in the prior art that the camera system is easily affected by changes in ambient temperature during outdoor monitoring, which affects the positioning and tracking accuracy of the target point and leads to low accuracy of visual displacement measurement.

[0007] A first aspect of an embodiment of the present application provides a universal thermal drift suppression method for visual displacement measurement, which includes the following steps: obtaining reference information and current information of a reference calibration plate and a target object imaged together; obtaining a thermal drift correction constraint model based on the reference information and the current information; and obtaining coordinate information of the target object based on the current information and the thermal drift correction constraint model.

[0008] Optionally, in one embodiment of the present application, the reference calibration plate has multiple calibration points, the target object has measurement points, the reference information includes reference data of the multiple calibration points under the correction light path, and the current information includes current data of the multiple calibration points under the correction light path and data to be corrected of the measurement points under the measurement light path; the obtaining of the reference information and current information of the reference calibration plate and the target object jointly imaged specifically includes: obtaining a reference frame image of the reference calibration plate and the target object jointly imaged at an initial temperature, and obtaining the reference data of the multiple calibration points under the correction light path based on the reference frame image; obtaining a current frame image of the reference calibration plate and the target object jointly imaged at a current temperature, and obtaining the current data of the multiple calibration points under the correction light path and data to be corrected of the measurement points under the measurement light path based on the reference frame image.

[0009] Optionally, in one embodiment of the present application, obtaining a reference frame image of the reference calibration plate and the target object at the initial temperature specifically includes: obtaining an initial image of the reference calibration plate and the target object at the initial temperature; if the plurality of calibration points in the initial image do not overlap with the measurement points, using the initial image as the reference frame image; if the calibration points in the initial image overlap with the measurement points, obtaining an updated image of the reference calibration plate and the target object after moving the visual measurement device, until the plurality of calibration points in the updated image do not overlap with the measurement points, and using the updated image as the reference frame image.

[0010] Optionally, in one embodiment of the present application, the reference data includes a reference corner point coordinate set, the current data includes a current corner point coordinate set, and the data to be corrected includes measurement point coordinates; the reference data of multiple calibration points under the correction optical path obtained based on the reference frame image specifically includes: dividing the reference frame image into regions to obtain multiple reference local network sub-regions; extracting feature points for each of the reference local network sub-regions to obtain a reference corner point coordinate set corresponding to each of the reference local network sub-regions; the current data of multiple calibration points under the correction optical path and the data to be corrected for the measurement points under the measurement optical path obtained based on the reference frame image specifically include: dividing the current frame image into regions to obtain multiple current local network sub-regions, wherein the current local network sub-regions correspond to the reference local network sub-regions; extracting feature points for each of the current local network sub-regions to obtain a current corner point coordinate set and measurement point coordinates corresponding to each of the current local network sub-regions.

[0011] Optionally, in one embodiment of the present application, the thermal drift correction constraint model includes multiple geometric transformation models; the thermal drift correction constraint model is obtained based on the reference information and the current information, specifically: for multiple reference local network sub-areas and corresponding multiple current local network sub-areas, multiple geometric transformation models are obtained based on multiple reference corner point coordinate sets and multiple current corner point coordinate sets.

[0012] Optionally, in one embodiment of the present application, when the geometric transformation model is a local homography matrix, the local homography matrix is ​​expressed as: ; in, Indicates the The local homography matrix of the current local network sub-region, Indicates the horizontal scaling factor, represents the horizontal shear strength, Indicates the horizontal translation amount, represents the vertical shear strength, Indicates the vertical scaling factor, Indicates the vertical translation amount, represents the horizontal perspective distortion coefficient, represents the vertical perspective distortion coefficient, represents the normalization factor, represents the set of real numbers; When the geometric transformation model is a local affine transformation matrix, the local affine transformation matrix is ​​expressed as: ; in, Indicates thei The local unit matrix of the current local site sub-region, Represents the scaling factors in the horizontal and vertical directions respectively, and denote the shear coefficients in the horizontal and vertical directions, respectively. and Represents the translation in the horizontal and vertical directions respectively; When the geometric transformation model is a high-order nonlinear distortion model, the modeling method of the high-order nonlinear distortion model is: ; in, is the horizontal coordinate of the pixel in the reference frame, is the vertical coordinate of the pixel in the reference frame, is the horizontal coordinate after drift in the current frame, is the vertical coordinate after drift in the current frame, is the polynomial coefficient that maps the horizontal coordinate of the pixel in the reference frame to the horizontal coordinate after drift in the current frame, are the polynomial coefficients that map the vertical coordinates of pixels in the reference frame to the vertical coordinates after drift in the current frame, where N is the polynomial order; or The modeling method of the high-order nonlinear distortion model is: ; ; in, , represents the radial distance to the center of the image; 、 、 is the radial distortion coefficient; 、 is the tangential distortion coefficient.

[0013] Optionally, in one embodiment of the present application, the coordinate information includes multiple target point coordinates; The coordinate information of the target object is obtained according to the current information and the thermal drift correction constraint model, specifically: Obtaining multiple target point coordinates according to all the measurement point coordinates and the corresponding multiple geometric transformation models; The target point coordinates are expressed as: ; in, Indicates the coordinates of the target point after the coordinates of the measured point are corrected. Indicates the A geometric transformation model of the current local network sub-region The inverse mapping of the geometric transformation model a local homography matrix a local affine matrix or a high-order nonlinear distortion model, representing the measured point coordinates in the current frame image.

[0014] Optionally, in an embodiment of the present application, the obtaining of the coordinate information of the target object according to the current information and the thermal drift correction constraint model further comprises: obtaining an image scale factor, and obtaining a real displacement amount according to the target point coordinates, the measured point coordinates and the image scale factor.

[0015] A visual measurement device for implementing the general thermal drift suppression method for visual displacement measurement according to any one of the above solutions is also provided in a second aspect of the embodiments of the present application, wherein the visual measurement device comprises a processor, a camera, a light-splitting prism, a collimating lens and a reference calibration board, the processor is in communication connection with the camera, the camera, the light-splitting prism, the collimating lens and the reference calibration board are sequentially arranged, two sides of the light-splitting prism are respectively directed to the reference calibration board and a target structure, the reference calibration board is provided with a plurality of calibration points arranged in an array, and the target structure has a measured point. The camera is configured to obtain reference information and current information of the reference calibration board and the target object jointly imaged, and send the reference information and the current information to the processor; the processor obtains a thermal drift correction constraint model according to the reference information and the current information, and obtains coordinate information of the target object according to the current information and the thermal drift correction constraint model.

[0016] Optionally, in an embodiment of the present application, an infrared band-pass filter is arranged on the side of the light-splitting prism directed to the target structure, the camera, the light-splitting prism, the infrared band-pass filter, the collimating lens and the reference calibration board are arranged in an optical dark box, and the target structure is located outside the optical dark box.

[0017] Beneficial effects: The present application provides a general thermal drift suppression method for visual displacement measurement and a visual measurement device, the present application eliminates the cumulative error caused by the separation of traditional calibration and measurement due to environmental changes through real-time correction in a common field of view, and makes the thermal drift correction accuracy reach the sub-pixel level through block homography transformation (one of the thermal drift correction constraint models), so as to improve the measurement accuracy in actual complex environments, and does not depend on specific camera models or temperature sensors, and can be adapted to different industrial cameras and lens combinations, and ensures the stability of long-term operation in actual complex environments. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is a schematic diagram of the three-dimensional structure of a preferred embodiment of the visual measurement device of the present application; Figure 2 This is a schematic diagram of a common-view imaging module in a preferred embodiment of the visual measurement device of the present application; Figure 3 This is a schematic diagram of an optical coupling module in a preferred embodiment of the visual measurement device of the present application; Figure 4 A schematic diagram of a plane calibration mark on a reference calibration plate in a preferred embodiment of the visual measurement device of the present application; Figure 5 This is a flow chart of a preferred embodiment of the universal thermal drift suppression method for visual displacement measurement of the present application; Figure 6 This is a schematic diagram of the correspondence between calibration points, measurement points and local homography matrix in a preferred embodiment of the universal thermal drift suppression method for visual displacement measurement of the present application; Figure 7 This is a flowchart of specific implementation steps in a preferred embodiment of the universal thermal drift suppression method for visual displacement measurement of the present application.

[0020] Description of reference numerals: 1. Optical darkroom; 2. Camera; 3. Beam splitter; 4. Infrared bandpass filter; 5. Measuring point; 6. Collimating lens; 7. Reference calibration plate; 8. Coaxial quick release plate; 9. Fill light device.

[0021] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and effects of this application clearer and more specific, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. The described embodiments are only possible technical implementations of this application and are not all possible implementations. Based on the embodiments in this application, those skilled in the art can fully combine the embodiments of this application to obtain other embodiments without creative work, and these embodiments are also within the scope of protection of this application.

[0023] In order to solve the problem that the camera system is prone to image thermal drift error when monitoring outdoors, which affects the positioning and tracking accuracy of the target point, and leads to low accuracy of visual displacement measurement, the application corrects in real time through a common field of view, eliminates the cumulative error caused by the separation of traditional calibration and measurement due to environmental changes, and makes the thermal drift correction accuracy reach the sub-pixel level through block homography (one of the thermal drift correction constraint models), so as to improve the measurement accuracy in actual complex environment, and does not depend on a specific camera model or temperature sensor, and can adapt to different industrial cameras and lens combinations, and ensure the stability of long-term operation in actual complex environment.

[0024] The technical solutions of the application will be described in detail below with specific examples. The following specific examples can be combined with each other, and the same or similar concepts or processes may not be described in detail in some examples.

[0025] As shown in Figure 1 , the visual measurement device described in the preferred embodiment of the application is used to implement a general thermal drift suppression method for visual displacement measurement. The visual measurement device includes a processor, a camera 2, a light splitting prism 3, a collimating lens 6 and a reference calibration board 7, the processor is in communication connection with the camera 2, the camera 2, the light splitting prism 3, the collimating lens 6 and the reference calibration board 7 are arranged in sequence, the two sides of the light splitting prism 3 are respectively towards the reference calibration board 7 and a target structure, the reference calibration board 7 is provided with a plurality of calibration points arranged in an array, and the target structure has a measurement point 5. The camera 2 is used to acquire reference information and current information of common imaging of the reference calibration board 7 and a target object, and send the information to the processor; the processor obtains a thermal drift correction constraint model according to the reference information and the current information, and obtains coordinate information of the target object according to the current information and the thermal drift correction constraint model.

[0026] It can be understood that the visual measurement device of the embodiment of the application has stable structure and compact volume, can realize accurate correction of thermal drift error in a visual measurement system, and is suitable for long-term displacement monitoring tasks of various engineering structures.

[0027] Specifically, referring to Figure 1 and Figure 2 , the industrial camera 2, the light splitting prism 3 and the collimating lens 6 (i.e. a convex lens) jointly constitute a common imaging visual measurement module (common imaging module), Figure 2The main channel is used to measure the displacement of the target structure (the measurement point 5 is located on the target structure), and the secondary channel is used to observe the embedded calibration plane (i.e., the plane with multiple calibration points on the reference calibration plate 7). The two channels are projected onto the same image sensor through an optical coupling module. Figure 3 The optical coupling module includes a beam splitter prism 3 for splitting and synthesizing the light path, a collimating lens 6 for adjusting the consistency of the imaging focal plane, and an infrared bandpass filter 4 for filtering out external stray light. The collimating lens 6 and the filter SM01 (model identification of the optical element fixing device) are fixed to the lens sleeve and connected to the beam splitter prism 3 fixed in the prismatic cube through threads. The entire module is only 4.9cm×4.9cm×4.1cm in size and has a compact structure. It can realize the optical path coupling of the main channel and the sub-channel, ensuring the common imaging of the two fields of view on the same image sensor.

[0028] In the embodiment of the present application, a passive plane calibration mark (calibration point) is used on the reference calibration plate 7. The mark is fixed in the auxiliary channel optical path to provide a stable reference feature point to assist in thermal drift modeling and calibration. The mark is made of a material with low thermal expansion coefficient and strong geometric stability (such as a glass ceramic substrate, a metal etching plate, a composite structure flat plate) to ensure dimensional stability and no deformation under different ambient temperatures. The mark pattern (i.e., the image of the calibration plane) can be a checkerboard pattern ( Figure 4 (a) in the figure), circular dot matrix calibration plate ( Figure 4 (b) in the figure), or an optical mask with a high-contrast edge structure ( Figure 4 In (c), these patterns facilitate high-precision extraction of corner points or centroids in the image, and can form a set of feature points that are spatially uniformly distributed and highly sensitive to thermal drift.

[0029] In one embodiment of the present application, an infrared bandpass filter 4 is provided on the side of the dichroic prism 3 facing the target structure, the camera 2, the dichroic prism 3, the infrared bandpass filter 4, the collimating lens 6 and the reference calibration plate 7 are arranged in an optical dark box 1, and the target structure is located outside the optical dark box 1.

[0030] Specifically, the bottom wall of the optical darkroom 1 is removably mounted with a coaxial quick-release plate 8. The camera 2, beam splitter prism 3, collimating lens 6, and reference calibration plate 7 are connected to the coaxial quick-release plate 8, which is also provided with corresponding limit slots to facilitate rapid assembly of the visual measurement device. A fill light device 9 is provided above the reference calibration plate 7 to supplement the imaging of the calibration plane by the industrial camera 2. It is understood that the optical darkroom 1, infrared bandpass filter 4, and fill light device 9 can suppress interference from daytime and nighttime illumination changes, ensuring stable feature point extraction and suitable for low-light scenarios such as tunnels and under bridges.

[0031] The visual measurement device of the embodiment of the present application has a stable structure and a compact size, which ensures its deployability in actual complex environments and can maintain long-term stable operation under variable ambient temperatures.

[0032] It can be understood that in the optical coupling module, the collimating lens 6 can be replaced with a collimating lens 6 of different focal lengths or a multi-lens group to adjust the imaging distance of the sub-channel reference plane, thereby adapting to the requirements of different working distances or reference surface layout depths; the filter can be flexibly configured. In addition to the fixed-band infrared bandpass filter 4, different band filters (such as near-infrared, short-wave infrared, etc.) can be selected to adapt to different light source environments, or combined filters (such as bandpass + long-wavepass composite filtering) can be used to improve the signal-to-noise ratio and anti-interference ability; in specific environments, polarizers or polarization beam splitters can be used to separate reflected light and direct light to improve the imaging contrast of the reference mark, which is particularly suitable for structural scenes with strong surface reflection interference; when some optical axes cannot be collinear or the sensor size is limited, a micro-mirror, a steering prism or a refractive wedge can be set to slightly deflect the sub-channel optical path to achieve image axis alignment or field of view center alignment, thereby improving imaging consistency. The plane calibration mark has multiple types of plane marker points for selection. Different types of pattern marker points (such as circular dot matrix, checkerboard, coded marker points, optical mask plates, etc.) can be used in the reference area and measurement area to enhance the difference and stability of feature point recognition, and improve the matching accuracy and automation level of the algorithm; when three-dimensional structural deformation monitoring or multi-angle posture estimation is required, the plane mark can be expanded into a three-dimensional target matrix (such as spherical target points, spatial target columns, 3D marker blocks, etc.), combined with stereo vision or multi-view reconstruction technology to realize the displacement measurement of the structural target in the XYZ three-dimensional direction, expanding the functional boundaries of the system.

[0033] In this application, a planar marker point design based on uneven distribution is used to adapt to various complex scenarios. Traditional reference marker plates mostly use regular dot matrices (such as checkerboards and circular dot matrices). However, in actual engineering, due to uneven temperature changes and uneven spatial distribution of marker points, a planar marker point strategy with uneven distribution design is adopted: high-density reference points are arranged in key areas, and low-density reference points are arranged in non-key areas or areas with poor visibility. A large-pattern-small-pattern hybrid nested design is adopted to balance overall rigid alignment with local fine modeling. Reference point design can be targeted at thermally sensitive areas and important structural parts of the target structure. Through the reasonable design of irregular marker patterns, the system's modeling stability and data recovery integrity can be significantly enhanced under actual complex working conditions such as local fine calibration and strong spatial non-uniformity of thermal drift.

[0034] In view of the technical problems that the existing visual measurement system is easily affected by temperature changes during long-term monitoring, resulting in image drift, decreased measurement accuracy, etc., the embodiment of the present application provides a universal and high-precision universal thermal drift suppression method for visual displacement measurement. Based on a measurement-correction common field imaging configuration, an unchanging plane reference calibration plate is embedded in the measurement field of view, and a compact dual-channel common view optical path is used to achieve spatial fusion of the measurement area and the monitoring area, fundamentally eliminating the error accumulation problem caused by the separation of calibration and measurement in traditional visual systems. The present application constructs a thermal drift correction constraint model by measuring a stable reference plane in the field of view, and adopts a block correction strategy on the imaging plane to model and compensate for image point drift, thereby effectively suppressing spatial non-uniform image distortion caused by heat. Specifically, the method describes the thermal drift characteristics of different regions by local homography transformation / constructing a distortion model, and achieves high-precision partition correction of full-field image drift.

[0035] This application can significantly improve the thermal robustness and displacement measurement accuracy of visual measurement systems in complex and changing environments. It offers greater robustness and adaptability, and is independent of camera model and lens type. It is particularly suitable for long-term structural health monitoring of outdoor infrastructure such as wind power, nuclear power, bridges, and tunnels. This application boasts a compact structure, strong stability, and easy deployment. Experiments have proven that it can maintain good measurement stability and correction accuracy even in environments with large temperature differences. It can improve long-term stability and measurement accuracy in complex environments, and has broad practical application prospects.

[0036] The general thermal drift suppression method for visual displacement measurement described in the preferred embodiment of this application is as follows: Figure 5 As shown, the universal thermal drift suppression method for visual displacement measurement includes the following steps: In step S101 , reference information and current information of the joint imaging of the reference calibration plate and the target object are obtained.

[0037] In one possible implementation, the reference calibration plate has multiple calibration points, the target object has measurement points, the reference information includes reference data for the multiple calibration points along the correction optical path, and the current information includes current data for the multiple calibration points along the correction optical path and data to be corrected for measurement points along the measurement optical path. A reference frame image, formed by imaging the reference calibration plate and the target object at an initial temperature, is acquired, and reference data for the multiple calibration points along the correction optical path is obtained based on the reference frame image. A current frame image, formed by imaging the reference calibration plate and the target object at a current temperature, is acquired, and current data for the multiple calibration points along the correction optical path and data to be corrected for measurement points along the measurement optical path are obtained based on the reference frame image.

[0038] It is understood that the initial temperature is the initial working temperature of the camera when it is turned on, and the current temperature is the real-time temperature of the camera when it is running continuously for a period of time.

[0039] The thermal drift modeling of this application can capture and model the local drift behavior of images caused by temperature changes. In the thermal drift modeling process, the entire image is divided into several fixed grid areas (such as 3×3, 5×5, etc.), each sub-area covers a limited pixel range; the area division method can be flexibly adjusted according to the field of view size, reference point density and expected non-uniform drift scale; in each frame of the image, stable feature points embedded in the calibration plane (such as a checkerboard or dot array) are extracted, and the reference feature points in each sub-area are fitted using the least squares method or the RANSAC algorithm (RANdom SAmple Consensus, random sampling consensus algorithm) to estimate the geometric transformation model (local homography matrix) of the area relative to the initial moment. , to approximately describe the local geometric deformation of the region under the action of temperature change; the corresponding homography moments of all sub-regions are Together, they form the thermal drift deformation field of the entire image, providing a modeling basis for subsequent point-by-point correction of target positions. This module fully considers the non-uniform distribution of thermal drift in image space, and compared to traditional modeling methods that assume the entire image is a rigid body drift, it has higher fitting accuracy and adaptability.

[0040] In one possible implementation, an initial image of a reference calibration plate and a target object is acquired at an initial temperature; if the plurality of calibration points in the initial image do not overlap with the measurement points, the initial image is used as a reference frame image; if the calibration points in the initial image overlap with the measurement points, an updated image of the reference calibration plate and the target object is acquired after moving the visual measurement device until the plurality of calibration points in the updated image do not overlap with the measurement points, and the updated image is used as the reference frame image.

[0041] In one possible implementation, the reference data includes a reference corner point coordinate set, the current data includes a current corner point coordinate set, and the data to be corrected includes measurement point coordinates. The reference frame image is divided into regions to obtain a plurality of reference local network sub-regions; feature points are extracted from each of the reference local network sub-regions to obtain a reference corner point coordinate set corresponding to each reference local network sub-region. The current frame image is divided into regions to obtain a plurality of current local network sub-regions, wherein the current local network sub-region corresponds to the reference local network sub-region; feature points are extracted from each of the current local network sub-regions to obtain a current corner point coordinate set and measurement point coordinates corresponding to each current local network sub-region. The current local network sub-region is the same as the reference local network sub-region.

[0042] Specifically, in the image region division process, a frame image is assumed to be a two-dimensional pixel array ,in The image is divided into n×m local grid sub-regions, where n is the number of network rows and m is the number of network columns. The sub-region is . is a two-dimensional pixel array image function, which indicates that the image is at coordinates The pixel value at ; Define the domain of the image, which is the set of all pixel coordinates of the image; For the local grid sub-regions, is the sub-region index, ( ).

[0043] It is understandable that the region division method can adopt adaptive region division, which dynamically determines the region division boundary based on factors such as the reference corner point distribution density, image texture complexity, or image brightness gradient. For example, if the corner point density in the region is high, the division is smaller to obtain more refined local modeling. Otherwise, the large region processing is maintained to improve robustness and computational efficiency. The region division method can also adopt region division based on temperature gradient. When the system can obtain external or image temperature distribution information (such as infrared images), the image division can be set according to the temperature change gradient. The areas with drastic temperature changes are subdivided more densely, and the areas with gentle changes are merged to ensure that the region division is more in line with the actual thermal deformation behavior.

[0044] During the feature point extraction process, stable feature points (such as checkerboard corners) on the reference plane are extracted from each frame image, and the sub-regions in the initial frame (reference frame) are The set of corner point coordinates inside is: ; in, Represents the reference corner coordinate set, i.e. the first The coordinate set of feature points of each sub-region (superscript 0 indicates the initial reference frame); The reference frame Sub-region Homogeneous coordinates of feature points; The reference frame Sub-region feature points coordinate; The reference frame Sub-region feature points coordinate, It is The number of available reference points in each sub-area, represents the transpose operator; The corresponding corner point set in the current frame (after temperature drift) is: ; in, Indicates the current corner point coordinate set, that is, the current frame (after temperature drift) in the current frame (after temperature drift) in the The corresponding feature point set of the sub-region (superscript Indicates time The corresponding feature point set of each sub-region (at time); The current frame Sub-region The homogeneous coordinates of the feature points.

[0045] In step S102 , a thermal drift correction constraint model is obtained according to the reference information and the current information.

[0046] It should be noted that, see Figure 7 The definition of is relatively broad. Homography is one method for thermal drift modeling. Depending on the application scenario, thermal drift modeling can use affine transformations, high-order nonlinear distortion models, and homography matrices. The versatility of thermal drift modeling lies in the ability to determine the appropriate method based on the duration of thermal drift modeling, the magnitude of pixel drift, and the patterns of local pixel drift. First, we will explain thermal drift modeling using a local homography matrix.

[0047] In one possible implementation, the thermal drift correction constraint model includes multiple geometric transformation models. For the multiple reference local network sub-regions and the corresponding multiple current local network sub-regions, multiple geometric transformation models are obtained based on the multiple reference corner point coordinate sets and the multiple current corner point coordinate sets.

[0048] It should be noted that the geometric transformation model is one of local homography transformation, local affine transformation and high-order nonlinear distortion model. The geometric transformation model in this embodiment is local homography transformation (local homography matrix).

[0049] Specifically, see Figure 4 and Figure 6 The red mark is the calibration point, and the point surrounded by multiple calibration points is the measurement point. A measurement point can be surrounded by multiple (such as 4, 5 or 6) calibration points. In this embodiment, 4 calibration points and 1 measurement point correspond to 1 local homography matrix. Figure 6 (a) in the 、 、 、 , Figure 6 (b) in the above has 、 、 、 and .

[0050] In the local homography matrix estimation process, for each sub-region , use the least squares method or RANSAC method to fit its local homography matrix , so that: .

[0051] In a possible implementation, when the geometric transformation model is a local affine transformation matrix, the affine transformation matrix is ​​expressed as: ; in, Indicates the i The local unit matrix of the current local site sub-region, Represents the scaling factors in the horizontal and vertical directions respectively, denote the shear coefficients in the horizontal and vertical directions, respectively. Represents the amount of translation in the horizontal and vertical directions respectively.

[0052] When the geometric transformation model is a high-order nonlinear distortion model, the high-order nonlinear distortion modeling method is: ; in, is the pixel coordinate in the reference frame, is the coordinate after drifting in the current frame, is the coefficient matrix element of the polynomial to be fitted, and N is the order of the polynomial (such as second, third, fourth, or higher).

[0053] The radial and tangential distortions can also be jointly modeled. The high-order nonlinear distortion modeling method is: ; ; in, , represents the radial distance to the center of the image; is the radial distortion coefficient, 、 、 is the radial distortion coefficient; is the tangential distortion coefficient, 、 is the tangential distortion coefficient.

[0054] Secondly, the thermal drift modeling is explained using local affine transformation modeling.

[0055] In certain application scenarios where temperature changes are relatively gradual and image deformation primarily manifests as translation and linear transformations, an affine transformation model can be used instead of a homography for local modeling. Affine transformations have fewer parameters and lower computational complexity, making them suitable for drift estimation in scenarios with limited computing resources or high real-time requirements. Specifically, if thermal drift in a localized area of ​​an image primarily manifests as approximately linear changes (such as translation, scaling, and shearing), an affine transformation can be used instead of a homography. Affine models have fewer parameters and are more computationally efficient, making them suitable for conditions with smaller drift amplitudes or uniformly heated structures.

[0056] Again, the thermal drift modeling is explained using high-order polynomial fitting modeling.

[0057] For scenarios where some thermal drift trajectories exhibit stable and regular characteristics, a higher-order nonlinear distortion model (such as a third- or fourth-order polynomial) can be introduced to perform pixel-level fitting of corner points or drift vectors. This method can more precisely represent complex thermal deformation trends and is suitable for systems with clear or predefined temperature variations, improving local modeling accuracy. In other words, using a high-order nonlinear distortion model to model local image thermal drift is suitable for situations where optical systems exhibit nonlinear imaging distortion under complex thermal conditions.

[0058] Specifically, in the modeling scheme of the high-order nonlinear distortion model, the initial reference frame is used as the benchmark, and the drift of each reference marker on the image plane under temperature disturbance is represented as a high-order polynomial function mapping of the input coordinates, such as: ; in, is the pixel coordinate in the reference frame, is the coordinate after drifting in the current frame, is the polynomial coefficient matrix element to be fitted, is the polynomial coefficient that maps the horizontal coordinate of the pixel in the reference frame to the horizontal coordinate after drift in the current frame, is the polynomial coefficient that maps the pixel vertical coordinate in the reference frame to the vertical coordinate after drift in the current frame, and N is the order of the polynomial (such as second, third, fourth, or higher).

[0059] It is also possible to jointly model radial and tangential distortion: ; ; in, , represents the radial distance to the center of the image; is the radial distortion coefficient, 、 、 is the radial distortion coefficient; is the tangential distortion coefficient, 、 is the tangential distortion coefficient.

[0060] Compared with the local homography correction method, this modeling method can more accurately describe the nonlinear drift trend of image points when the imaging system undergoes high-order distortion behaviors such as internal lens micro-displacement, lens deformation, and CCD (Charge-Coupled Device) thermal expansion under the influence of thermal stress. Compared with the model based on geometric transformation, the high-order distortion model has higher fitting ability and stronger expressiveness, and is suitable for situations where the drift of the edge area of ​​the image is uneven or "barrel-shaped" or "pillow-shaped" deviations occur. It is also suitable for compensating for thermal drift of visual systems in scenarios with long time, high temperature difference, and wide-angle lenses.

[0061] In step S103 , the coordinate information of the target object is obtained according to the current information and the thermal drift correction constraint model.

[0062] In a possible implementation, the coordinate information includes multiple target point coordinates. The multiple target point coordinates are obtained according to all the measurement point coordinates and the corresponding multiple geometric transformation models.

[0063] It should be noted that thermal drift calibration applies the local homography transformation estimated in thermal drift modeling to the inverse drift correction of each pixel within the target measurement area. By constructing a local mapping function and compensating for thermally induced drift point by point, the true structural displacement in the image is restored, improving measurement accuracy.

[0064] Specifically, the target point mapping area is determined. First, the sub-area to which the target point in the current frame belongs is determined based on the image coordinate position. The region index is determined by the preset grid division method. Taking the checkerboard mask as an example, quadrilateral division is performed based on the coordinates of the checkerboard corner points. Each sub-region corresponds to a local homography matrix . Assume that the pixel coordinates of the target point in the current frame image are: ; in, Indicates the coordinates of the measurement point in the current frame image, Indicates that the target point is in the current frame (time ) in the image coordinate, Indicates the target point in the current frame image Coordinates. Based on the preset grid division rules of the image, the system determines the local area number to which the point belongs. , and determine the homography matrix corresponding to the area Each local area The spatial range of is determined by the quadrilateral formed by the reference feature points (such as chessboard corner points), and the region index is obtained by judging the geometric fall-in of the points.

[0065] Then perform temperature drift compensation, for each sub-area , extract the spatial changes between the checkerboard corners in its reference frame (initial temperature state) and the current frame (heat drift state). Extract its With the current frame The reference corner coordinate pair under , and the homography matrix from the reference frame to the current frame is calculated by the formula.

[0066] Using the inverse matrix of the local homography , the measurement points in the current frame The thermal drift coordinates are back-projected to the position under the reference frame, and the target point coordinates are expressed as: ; in, Indicates the coordinates of the target point after the coordinates of the measured point are corrected. Indicates the A geometric transformation model of the current local network sub-region The inverse mapping of the geometric transformation model It can be expressed as a local homography matrix , local affine matrix or high-order nonlinear distortion models, Indicates the coordinates of the measurement point in the current frame image.

[0067] This mapping process geometrically compensates for thermal drift errors at that point, resulting in the true position coordinates after removing temperature disturbances. This compensation process only offsets geometric drift caused by ambient temperature variations and does not affect the actual displacement of the structure due to real loads or motion, thus ensuring the accuracy and physical significance of the measured data.

[0068] In a possible implementation, an image scale factor is acquired, and a true displacement is obtained according to the target point coordinates, the measurement point coordinates, and the image scale factor.

[0069] Specifically, to obtain the actual structural displacement of the point, its displacement relative to the reference position in the initial frame can be calculated. The differences between: ; in, represents the difference value, is the target point coordinate, is the coordinate of the measurement point. Introduce the image scale factor (Unit: mm / pixel) to convert pixel displacement into real-world displacement: ; in, Indicates the actual displacement.

[0070] The embodiment of the present application has an abnormal area processing and redundancy mechanism. If there are abnormal situations such as insufficient number of reference points in a sub-area or insufficient accuracy of thermal drift modeling, the system will trigger an adaptive strategy, such as: interpolation filling, estimating the transformation of the current area through weighted interpolation of adjacent sub-area models; area merging, merging with surrounding areas to fit a joint homography transformation; residual detection, judging whether there is an unreasonable jump in the position change of the target point after compensation, and excluding the measurement value of the area if necessary.

[0071] This module performs region-by-region and point-by-point displacement correction for all target points in the image. This not only compensates for overall thermal drift but also adapts to the non-uniformity and local characteristics of thermal drift within the image space, significantly improving the spatial accuracy and robustness of the system's measurements. It is particularly suitable for long-duration visual measurement tasks in demanding scenarios such as those with drastic temperature fluctuations, large fields of view, and high measurement accuracy.

[0072] It should be noted that compared with the common temperature compensation methods in existing visual measurement systems, this application has significant innovations in structural design, thermal drift modeling, correction strategy, and adaptability. The key differences and the resulting technological breakthroughs are as follows: First, it has good system versatility and adaptability. The difference is: traditional thermal drift compensation technology usually relies on the stability of specific camera models, lens structures or temperature changes, and requires the use of camera internal reference models or external temperature sensors for modeling, resulting in poor adaptability in complex environments. The measurement-calibration common field of view visual measurement method proposed in the present invention introduces a fixed reference plane in the same imaging field of view, and combines the local area division and modeling strategy of the image plane to directly perform thermal drift correction based on the geometric changes between images. It does not need to rely on the specific camera structure or temperature change law, and has good adaptability to different models of cameras, lenses and non-constant temperature fields, significantly improving the versatility and practicality of the system. Technical effect: It is insensitive to camera models, lens parameters, and structural design, and has good versatility; it can be applied to different types of industrial cameras and lens combinations; it still has reliability and accuracy guarantees in complex scenarios such as large dynamic temperature changes and harsh environments.

[0073] Second, a thermal drift modeling method based on local image blocks. The difference is that traditional thermal drift correction methods are mostly based on global rigid body models or simple function fitting between temperature and parameters. When faced with the non-uniform thermal distribution that is prevalent in actual imaging processes, they are often unable to effectively describe the local deformation of the image, resulting in insufficient correction accuracy. This application innovatively proposes an image plane block + local homography transformation modeling method. By dividing the image into multiple sub-regions and modeling their thermally induced deformation characteristics separately, a detailed characterization of spatially heterogeneous thermal drift is achieved. Technical effect: This method can accurately model the local non-uniformity of thermal drift on the image plane, significantly improving the spatial resolution and compensation accuracy of thermal drift correction compared to traditional methods. It is particularly suitable for long-term visual monitoring systems under conditions such as multi-source thermal interference and complex thermal field distribution.

[0074] Third: All-weather environmental adaptability design, the difference: Traditional visual temperature compensation systems are usually sensitive to external lighting conditions and are easily affected by strong light, backlight, low illumination at night, etc., which lead to problems such as unstable image features and failure of drift compensation. This application effectively overcomes the interference caused by changes in external ambient light by introducing an all-weather observation optical design. The entire imaging system is placed in a closed optical darkroom, which is equipped with a high-uniformity LED array supplementary light source. An infrared bandpass filter is introduced in the measurement mark path, which only allows imaging of light in a specific wavelength band (such as 850nm), thereby achieving high-contrast, anti-interference mark point extraction. Technical effect: Stable observation can be achieved day and night without relying on natural lighting conditions; external strong light or reflection interference is suppressed to improve the robustness of image feature point extraction; it can operate for a long time in low-illumination environments such as tunnels, under bridges, and at night to ensure measurement continuity and stability.

[0075] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0076] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0077] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0078] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable storage medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (not exhaustive) of computer-readable storage media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable storage medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0079] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0080] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0081] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0082] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

[0083] It should be understood that the application of this application is not limited to the above examples. For ordinary technicians in this field, they can make improvements or changes based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to this application.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application 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 or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A universal thermal drift suppression method for visual displacement measurement, characterized in that: The general thermal drift suppression method for visual displacement measurement includes: Obtain reference information and current information of the joint imaging of the reference calibration plate and the target object; Obtaining a thermal drift correction constraint model according to the reference information and the current information; The coordinate information of the target object is obtained according to the current information and the thermal drift correction constraint model.

2. The universal thermal drift suppression method for visual displacement measurement according to claim 1, characterized in that: The reference calibration plate has a plurality of calibration points, the target object has a measurement point, the reference information includes reference data of the plurality of calibration points in the correction light path, and the current information includes current data of the plurality of calibration points in the correction light path and data to be corrected of the measurement point in the measurement light path; The obtaining of reference information and current information of the joint imaging of the reference calibration plate and the target object specifically includes: Acquire a reference frame image of a reference calibration plate and a target object at an initial temperature, and obtain reference data of a plurality of calibration points under a corrected optical path based on the reference frame image; A current frame image of the reference calibration plate and the target object is obtained at the current temperature, and current data of multiple calibration points under the correction light path and data to be corrected of the measurement points under the measurement light path are obtained according to the reference frame image.

3. The universal thermal drift suppression method for visual displacement measurement according to claim 2, characterized in that: The obtaining of a reference frame image obtained by imaging the reference calibration plate and the target object at the initial temperature specifically includes: Acquire an initial image of the reference calibration plate and the target object at the initial temperature; If the plurality of calibration points in the initial image do not overlap with the measurement points, taking the initial image as a reference frame image; If the calibration points overlap with the measurement points in the initial image, after moving the visual measurement device, an updated image of the reference calibration plate and the target object is obtained until multiple calibration points in the updated image do not overlap with the measurement points, and the updated image is used as the reference frame image.

4. The universal thermal drift suppression method for visual displacement measurement according to claim 2, characterized in that: The reference data includes a reference corner point coordinate set, the current data includes a current corner point coordinate set, and the data to be corrected includes measurement point coordinates; The step of obtaining reference data of a plurality of calibration points under the corrected optical path according to the reference frame image specifically includes: Performing region division on the reference frame image to obtain a plurality of reference local network sub-regions; Extracting feature points from each of the reference local network sub-regions to obtain a reference corner point coordinate set corresponding to each of the reference local network sub-regions; The obtaining, according to the reference frame image, current data of a plurality of calibration points in the correction light path and data to be corrected of a measurement point in the measurement light path specifically includes: Performing region division on the current frame image to obtain a plurality of current local network sub-regions, wherein the current local network sub-regions correspond to the reference local network sub-regions; Feature points are extracted for each of the current local network sub-regions to obtain a current corner point coordinate set and measurement point coordinates corresponding to each of the current local network sub-regions.

5. The universal thermal drift suppression method for visual displacement measurement according to claim 4, characterized in that: The thermal drift correction constraint model includes a plurality of geometric transformation models; The thermal drift correction constraint model is obtained according to the reference information and the current information, specifically: For the multiple reference local network sub-areas and the corresponding multiple current local network sub-areas, multiple geometric transformation models are obtained according to the multiple reference corner point coordinate sets and the multiple current corner point coordinate sets.

6. The universal thermal drift suppression method for visual displacement measurement according to claim 5, characterized in that: When the geometric transformation model is a local homography matrix, the local homography matrix is ​​expressed as: ； in, Indicates the The local homography matrix of the current local network sub-region, Indicates the horizontal scaling factor, represents the horizontal shear strength, Indicates the horizontal translation amount, represents the vertical shear strength, Indicates the vertical scaling factor, Indicates the vertical translation amount, represents the horizontal perspective distortion coefficient, represents the vertical perspective distortion coefficient, represents the normalization factor, represents the set of real numbers; When the geometric transformation model is a local affine transformation matrix, the local affine transformation matrix is ​​expressed as: ； in, Indicates the i The local unit matrix of the current local site sub-region, Represents the scaling factors in the horizontal and vertical directions respectively, and denote the shear coefficients in the horizontal and vertical directions, respectively. and Represents the translation in the horizontal and vertical directions respectively; When the geometric transformation model is a high-order nonlinear distortion model, the modeling method of the high-order nonlinear distortion model is: ； in, is the horizontal coordinate of the pixel in the reference frame, is the vertical coordinate of the pixel in the reference frame, is the horizontal coordinate after drift in the current frame, is the vertical coordinate after drift in the current frame, is the polynomial coefficient that maps the horizontal coordinate of the pixel in the reference frame to the horizontal coordinate after drift in the current frame, are the polynomial coefficients that map the vertical coordinates of pixels in the reference frame to the vertical coordinates after drift in the current frame, where N is the polynomial order; or The modeling method of the high-order nonlinear distortion model is: ； ； in, , represents the radial distance to the center of the image; 、 、 is the radial distortion coefficient; 、 is the tangential distortion coefficient.

7. The universal thermal drift suppression method for visual displacement measurement according to claim 6, characterized in that: The coordinate information includes multiple target point coordinates; The coordinate information of the target object is obtained according to the current information and the thermal drift correction constraint model, specifically: Obtaining multiple target point coordinates according to all the measurement point coordinates and the corresponding multiple geometric transformation models; The target point coordinates are expressed as: ； in, Indicates the coordinates of the target point after the coordinates of the measured point are corrected. Indicates the A geometric transformation model of the current local network sub-region The inverse mapping of the geometric transformation model is the local homography matrix , local affine matrix or high-order nonlinear distortion models, Indicates the coordinates of the measurement point in the current frame image.

8. The universal thermal drift suppression method for visual displacement measurement according to claim 7, characterized in that: The method further comprises obtaining coordinate information of the target object according to the current information and the thermal drift correction constraint model, and then further comprising: An image scale factor is obtained, and a true displacement is obtained according to the target point coordinates, the measurement point coordinates, and the image scale factor.

9. A visual measurement device for implementing the universal thermal drift suppression method for visual displacement measurement according to any one of claims 1 to 8, characterized in that: The visual measurement device includes a processor, a camera, a beam splitter prism, a collimating lens, and a reference calibration plate. The processor is communicatively connected to the camera. The camera, the beam splitter prism, the collimating lens, and the reference calibration plate are arranged in sequence. Two sides of the beam splitter prism face the reference calibration plate and the target structure, respectively. The reference calibration plate is provided with a plurality of calibration points arranged in an array, and the target structure has measurement points. The camera is used to obtain reference information and current information of the joint imaging of the reference calibration plate and the target object, and send the information to the processor; The processor obtains a thermal drift correction constraint model according to the reference information and the current information, and obtains coordinate information of the target object according to the current information and the thermal drift correction constraint model.

10. The visual measuring device according to claim 9, characterized in that: An infrared bandpass filter is provided on the side of the beam splitter prism facing the target structure. The camera, the beam splitter prism, the infrared bandpass filter, the collimating lens and the reference calibration plate are arranged in an optical dark box, and the target structure is located outside the optical dark box.

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