An image-based method and system for measuring surface anomalies in suspension bridges

Abstract

This invention discloses an image-based method and system for measuring surface anomalies in suspension bridges, relating to the fields of optical measurement and image processing technology. The method involves time-division projection of first and second polarization gratings with mutually orthogonal polarization directions onto the test area of ​​the suspension bridge; the use of a polarization state imaging unit to acquire the polarization information of reflected light and generate corresponding first and second moiré phase maps. A data processing unit further performs dual-path parallel processing on the two moiré phase maps: on one hand, a joint solution algorithm is used to calculate the two-dimensional tilt field characterizing the macroscopic contour of the object; on the other hand, differential operations are used to identify and locate microscopic optical anisotropic anomalies caused by material stress, damage, etc., by utilizing the symmetrical response of the optically isotropic surface to the orthogonal gratings. This invention achieves simultaneous decoupling of geometric and physical characteristics from a single data source through a single measurement, offering advantages such as non-contact and high efficiency, and is suitable for precision inspection of large structures such as suspension bridges.

Description

Technical Field

[0001] This invention relates to the field of optical measurement and image processing technology, specifically to an image-based method and system for measuring surface anomalies of suspension bridges. Background Technology

[0002] Long-term and effective health monitoring of large-scale critical infrastructure such as suspension bridges is a core guarantee for ensuring their safe operation and extending their service life. This monitoring typically includes two equally important dimensions: the first is macroscopic geometric attitude assessment, such as measuring the tilt, deflection, and linear changes of the bridge's main cables, towers, or deck to determine the overall structural stability; the second is microscopic surface condition detection, which aims to promptly detect and locate early defects that may indicate structural damage, such as coating cracking, early corrosion, and material fatigue.

[0003] Current technologies for acquiring macroscopic geometric information are relatively mature. Optical methods such as 3D laser scanning and photogrammetry can quickly construct 3D point cloud models of structures, thereby accurately assessing their overall attitude and deformation. However, these technologies are primarily sensitive to the geometric contours of objects and have almost no ability to detect surface optical anomalies (such as birefringence) caused by material stress changes or minor physical damage that are not accompanied by obvious geometric undulations. Therefore, relying solely on geometric measurements often misses the critical window for detecting early structural defects.

[0004] On the other hand, various technologies exist for detecting microscopic surface defects. Traditional contact methods, such as ultrasonic testing, can detect internal defects, but they are inefficient and difficult to implement over large areas. Non-contact methods, such as infrared thermal imaging, have applications, but their detection mechanisms rely on temperature differences, limiting their applicability. More importantly, these defect detection-focused technologies cannot provide precise macroscopic geometric information about the structure being tested.

[0005] Therefore, existing technological systems exhibit an inherent functional separation. A comprehensive assessment of a bridge often requires deploying two or more completely different measurement systems and organizing independent measurement operations to acquire macroscopic geometric data and microscopic defect data, respectively. This separated operational model not only leads to cumbersome and time-consuming on-site workflows but also presents a severe technical challenge: the difficulty of data fusion. Accurately and automatically mapping local defect information obtained from one system to a global three-dimensional geometric model obtained from another system is an unusual process, often accompanied by coordinate registration errors and data incompatibility issues. This data-level barrier hinders the formation of a rapid, unified, and accurate holistic understanding of the structural health status.

[0006] Therefore, developing a new measurement technology that can simultaneously decouple the macroscopic geometric posture and microscopic surface physical anomaly information of a structure from the same set of raw data in a single measurement process is of great technical value and engineering significance, in order to overcome the bottlenecks of existing technologies such as single function and difficulty in data fusion. Summary of the Invention

[0007] The technical problem to be solved by the present invention is that, when performing optical measurements on large structures, the existing technology usually cannot obtain the macroscopic geometric posture (such as tilt) and microscopic surface physical state (such as early defects) of the structure simultaneously and with high precision in a single measurement, resulting in problems of single function and difficulty in data fusion.

[0008] To address the aforementioned technical problems, this invention provides a new technical solution.

[0009] The first aspect of this invention provides an image-based method for measuring surface anomalies of a suspension bridge, the method comprising the following steps:

[0010] S1. Project a first polarization grating and a second polarization grating onto the test area of ​​the suspension bridge in a time-division manner, wherein the polarization direction of the first polarization grating changes periodically along a first spatial direction, and the polarization direction of the second polarization grating changes periodically along a second spatial direction orthogonal to the first spatial direction.

[0011] S2. The polarization information of the first polarization grating and the second polarization grating after reflection from the area to be tested is acquired by a polarization state imaging unit to obtain the first polarization information and the second polarization information.

[0012] S3. Generate a first moiré phase map and a second moiré phase map based on the first polarization information and the second polarization information, respectively;

[0013] S4. Process the first moiré phase map and the second moiré phase map to simultaneously acquire two measurement results, which are as follows:

[0014] a. Calculate the two-dimensional tilt field that characterizes the tilt state of each point on the area to be measured, and determine the tilt measurement result of the area to be measured based on the two-dimensional tilt field;

[0015] b. Based on the response difference between the first and second moiré phase maps, identify and locate surface anomalies in the area to be measured, and determine the measurement results of the surface anomalies.

[0016] Preferably, in step S2, the step of acquiring polarization information specifically includes:

[0017] Intensity images I0, I in at least four polarization directions are obtained through a single exposure using the polarization state imaging unit.45 ,I 90 ,I 135 ;

[0018] Based on the intensity image, the Stokes vectors S0, S1, and S2 are calculated: ;

[0019] Where, I0,I 45 ,I 90 ,I 135 S1, S2 are the intensity images acquired by the polarization state imaging unit in the polarization directions of 0°, 45°, 90° and 135°, respectively; S0, S1 and S2 are the components of the calculated Stokes vector.

[0020] Subsequently, the linear polarization angle distribution ψ is calculated from the Stokes vector. meas To obtain the first polarization information and the second polarization information:

[0021] ψ meas (x,y)=1 / 2arctan((S2(x,y)) / (S1(x,y)));

[0022] Where (x,y) are the pixel coordinates in the intensity image; ψ meas (x,y) is the linear polarization angle measured at coordinates (x,y); S1(x,y) and S2(x,y) are the component values ​​of the Stokes vector at coordinates (x,y), respectively.

[0023] In one specific embodiment, step S3, the step of generating the moiré phase map, specifically includes: converting the linear polarization angle distribution ψ... meas The ideal reference models ψ of the first polarization grating and the second polarization grating are respectively compared. ref Perform differential operations to generate the moiré phase diagram ΔΦ of the package:

[0024] ΔΦ(x,y)=ψ meas (x,y)-ψ ref (x,y);

[0025] Where ΔΦ(x,y) is the Moiré phase value generated at coordinates (x,y); ψ ref (x,y) is the theoretical reference linear polarization angle of the projected polarization grating at coordinates (x,y).

[0026] Further, in step S4, the step of determining the tilt measurement result of the region to be measured includes: based on the pre-calibrated system transfer function matrix C, performing a measurement on the first Moiré phase diagram ΔΦ. H Second Mohr phase diagram ΔΦ V By performing a joint solution, the tilt component θ is obtained. x and θy The resulting two-dimensional tilted field:

[0027] ;

[0028] Where, θ x (x,y) and θ y (x, y) represent the tilt components of the surface of the region to be measured along the first and second spatial directions at coordinates (x, y), respectively; C(x, y) is the 2x2 system transfer function matrix pre-calibrated at coordinates (x, y); ΔΦ H (x,y) represents the phase value of the first moiré phase map generated by the first polarization grating at coordinates (x,y); ΔΦ V (x,y) represents the phase value of the second moiré phase map generated by the second polarization grating at coordinates (x,y).

[0029] The two-dimensional tilt field is then converted into a visual cloud map or vector arrow map, which serves as the tilt measurement result.

[0030] In one implementation scheme, the joint solution specifically refers to:

[0031] The integrability condition that the two-dimensional tilted field must satisfy is used as an optimization constraint. The first and second moiré phase diagrams are solved by an optimization algorithm to obtain the two-dimensional tilted field. The calculation error caused by optical distortion or component misalignment is corrected based on the two-dimensional tilted field.

[0032] Preferably, in step S4, the step of determining the measurement results of the surface anomaly points includes: processing the first moiré phase map ΔΦ H Second Mohr phase diagram ΔΦ V Perform difference operations to generate a difference anomaly map M. diff For optically isotropic points in the differential anomaly map, their response to orthogonal polarization gratings is symmetrical, while optically anisotropic points exhibit an asymmetrical response. The differential operation amplifies the asymmetrical response, thereby forming a recognizable signal in the differential anomaly map. Subsequently, points in the differential anomaly map with values ​​higher than a preset threshold are identified as surface anomaly points, and their location information is output as the measurement result of the surface anomaly points.

[0033] In one specific embodiment, the difference operation specifically includes: normalizing the first and second Moiré phase maps, and calculating the difference between the normalized first and second Moiré phase maps to generate the difference anomaly map M. diff :

[0034] M diff (x,y)=|(ΔΦ H(x,y)) / σ H -(ΔΦ V (x,y)) / σ V |;

[0035] Among them, M diff (x,y) represents the value of the difference anomaly plot at coordinates (x,y); σ H and σ v These are the local standard deviations of the first and second Mohr phase maps, respectively, used for normalization.

[0036] Preferably, in step S1, a spatial light modulator is used to generate and switch the first polarization grating and the second polarization grating.

[0037] In one specific embodiment, the first spatial direction is the horizontal direction, and the second spatial direction is the vertical direction.

[0038] A second aspect of the present invention provides an image-based system for measuring surface anomalies of suspension bridges, the system comprising:

[0039] A projection unit is used to project a first polarization grating and a second polarization grating onto the test area of ​​the suspension bridge in a time-division manner, wherein the polarization direction of the first polarization grating changes periodically along a first spatial direction, and the polarization direction of the second polarization grating changes periodically along a second spatial direction orthogonal to the first spatial direction.

[0040] A polarization state imaging unit is used to acquire the polarization information of the first polarization grating and the second polarization grating after reflection from the area under test, respectively, to obtain the first polarization information and the second polarization information;

[0041] A data processing unit, connected to the projection unit and the polarization state imaging unit, is used to generate a first moiré phase map and a second moiré phase map based on the first polarization information and the second polarization information, respectively, and to process the first moiré phase map and the second moiré phase map to simultaneously acquire the tilt measurement results and surface anomaly point measurement results of the area to be measured.

[0042] This invention provides an image-based method and system for measuring surface anomalies in suspension bridges. It offers the following advantages:

[0043] 1. This invention uses time-division projection of a first polarization grating and a second polarization grating. From the acquired first and second moiré phase maps, it determines the two-dimensional tilt field of the area to be measured by joint solution and identifies surface anomalies by differential operation. This scheme integrates macroscopic geometric attitude measurement and microscopic surface state detection into the same data acquisition and processing flow. A single measurement can simultaneously output detection results of two different physical dimensions without the need for additional hardware or independent measurement operations.

[0044] 2. The technical solution of the present invention generates two moiré phase maps containing data redundancy by acquiring independent information generated by two orthogonal polarization gratings. In the process of jointly solving the tilt component, the data redundancy can be used to compensate for and suppress the inherent errors in the system, thereby realizing the self-calibration of the measurement. This design reduces the sensitivity of the system to environmental changes and hardware instability, and ensures the accuracy of the measurement results.

[0045] 2. The measurement system of the present invention uses a single polarization state imaging unit. By quickly switching the projection grating, it can complete the acquisition of information of two orthogonal components. This process does not require complex mechanical movement or system reconstruction. In particular, when a spatial light modulator is used to generate and switch the grating, the data acquisition speed is fast, the operation process is simple, and the on-site operation time is significantly shortened. It is more suitable for rapid and in-situ detection of large structures such as suspension bridges. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the image-based suspension bridge surface anomaly measurement system of the present invention;

[0047] Figure 2 This is a schematic diagram of the projection unit according to an embodiment of the present invention;

[0048] Figure 3 This is a schematic diagram of the structure of a polarization state imaging unit according to an embodiment of the present invention;

[0049] Figure 4 This is a flowchart of the image-based method for measuring surface anomalies of suspension bridges according to the present invention;

[0050] Figure 5 This is a schematic diagram of a standard test sample according to an embodiment of the present invention;

[0051] Figure 6 This is a two-dimensional tilted field cloud map according to an embodiment of the present invention;

[0052] Figure 7 This is a differential anomaly diagram according to an embodiment of the present invention.

[0053] In the attached figures, the reference numerals are as follows: 10, projection unit; 11, light source; 12, collimation and beam expansion system; 13, polarizer; 14, spatial light modulator; 15, projection lens; 20, polarization state imaging unit; 21, imaging lens; 22, micro-polarizer array; 23, image sensor; 30, data processing unit. Detailed Implementation

[0054] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] See attached document Figure 1 This embodiment discloses a measurement system for implementing the image-based method for measuring surface anomalies of suspension bridges. The measurement system may include: a projection unit 10, a polarization imaging unit 20, and a data processing unit 30.

[0056] The projection unit 10 is used to project the first polarization grating and the second polarization grating onto the test area S of the suspension bridge in a time-division manner.

[0057] The polarization state imaging unit 20 is used to acquire the polarization information of the reflected light after being reflected by the region S under test.

[0058] The data processing unit 30 establishes communication connections with both the projection unit 10 and the polarization imaging unit 20. The data processing unit 30 sends commands to the projection unit 10 to control the generation and projection timing of the grating, and simultaneously sends a trigger signal to the polarization imaging unit 20 to synchronize the acquisition of raw image data. The polarization imaging unit 20 transmits the acquired raw image data to the data processing unit 30, which then performs subsequent calculations and analysis, and outputs the final measurement results.

[0059] See attached document Figure 2 In one specific embodiment, the projection unit 10 includes, in sequence along the optical path: a light source 11, a collimating and beam expanding system 12, a polarizer 13, a spatial light modulator 14, and a projection lens 15.

[0060] The light source 11 provides a diverging beam, such as a light-emitting diode (LED) or a laser, whose output diverging beam is unpolarized or randomly polarized. The collimating and beam expanding system 12 is located after the light source 11 and consists of a set of lenses. Its function is to shape the diverging beam emitted from the light source 11 into a parallel beam with a predetermined aperture to ensure complete coverage of the test area S of the suspension bridge.

[0061] A polarizer 13 is disposed between the collimating and beam expanding system 12 and the spatial light modulator 14. The transmission axis of the polarizer 13 is set along a predetermined direction, such as the horizontal direction. After passing through the polarizer 13, the parallel beam is converted into linearly polarized light, providing a reference polarization state for the subsequent spatial light modulator.

[0062] The spatial light modulator 14, for example, is a liquid crystal spatial light modulator (LC-SLM), which is a component used to generate a polarization grating. The spatial light modulator 14 is connected to the data processing unit 30 and receives a digital grayscale image generated by the data processing unit 30. Each pixel unit of the spatial light modulator 14 independently rotates the polarization direction of the linearly polarized light passing through that pixel unit according to the received grayscale value, thereby forming a two-dimensional light field, i.e., a polarization grating, on the cross-section of the outgoing beam with polarization directions spatially distributed according to a predetermined pattern.

[0063] Specifically, to generate the first polarization grating, the data processing unit 30 generates a digital grayscale image whose grayscale values ​​periodically change along a first spatial direction (e.g., the horizontal x-direction) and loads it onto the spatial light modulator 14. The spatial light modulator 14 then generates the first polarization grating, whose polarization direction changes periodically and continuously in the horizontal direction. Similarly, to generate the second polarization grating, the data processing unit 30 generates another digital grayscale image whose grayscale values ​​periodically change along a second spatial direction orthogonal to the first spatial direction (e.g., the perpendicular y-direction) and loads it onto the spatial light modulator 14, thereby generating a second polarization grating whose polarization direction changes periodically in the vertical direction.

[0064] The data processing unit 30 controls the switching of the digital grayscale image loaded onto the spatial light modulator 14 to achieve time-division projection of the first polarization grating and the second polarization grating. The projection lens 15 is located at the end of the optical path and is used to image the light field output by the spatial light modulator 14, which carries polarization grating information, onto the surface of the area to be measured S.

[0065] See attached document Figure 3 , Figure 3 This is a schematic diagram of the structure of a polarization state imaging unit according to an embodiment of the present invention.

[0066] In a preferred embodiment, the polarization state imaging unit 20 is a "snapshot" focal plane polarization camera. The camera mainly includes an imaging lens 21, a micro polarizer array 22, and an image sensor 23.

[0067] Imaging lens 21 is used to focus and image the light field reflected from the area S to be measured. Image sensor 23 can be a charge-coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS) sensor for photoelectric conversion.

[0068] The micro-polarizer array 22 is the core component of this "snapshot" focal plane polarization camera, integrated and precisely aligned onto the photosensitive pixel array surface of the image sensor 23. The micro-polarizer array 22 consists of a large number of repeating superpixel units, each covering, for example, a 2x2 group of sensor pixels. Within a single superpixel unit, four micro-polarizers have polarization directions of 0°, 45°, 90°, and 135°, respectively, corresponding one-to-one with the four sensor pixels below.

[0069] When reflected light from the area S under test is imaged onto the micro-polarizer array 22 by the imaging lens 21, the light passing through a single superpixel unit is decomposed in real time into components with four different polarization directions before entering the sensor pixels below. Therefore, during a single exposure, different sensor pixels on the image sensor 23 simultaneously record the light intensity distribution after being filtered by four different polarization angles. This "snapshot" focal plane polarization camera outputs a raw mosaic image containing all polarization information, which is then transmitted to the data processing unit 30. The data processing unit 30 uses a specific de-mosaic algorithm to reconstruct four complete, registered intensity images corresponding to polarization directions of 0°, 45°, 90°, and 135° from the raw mosaic image in one go. The raw mosaic image is the raw image data.

[0070] In another embodiment, the polarization imaging unit 20 may also consist of a standard industrial camera and a rotatable polarizer positioned in front of its imaging lens. The rotatable polarizer is driven by a precision motor connected to the data processing unit 30. During measurement, the data processing unit 30 controls the precision motor to sequentially rotate the polarization direction of the polarizer to 0°, 45°, 90°, and 135°, triggering the standard industrial camera to perform one exposure in each polarization direction, thereby acquiring four intensity images in a time-division multiplexing manner.

[0071] See attached document Figure 1 The data processing unit 30 is the control and calculation component of the entire measurement system. In terms of hardware, this unit can be an industrial personal computer, a portable computer, or an embedded system integrating a processor and memory. Internally, it includes a central processing unit (CPU), a graphics processing unit (GPU), RAM, data storage devices (such as solid-state drives), and communication interfaces for connecting external devices.

[0072] The data processing unit 30 is connected to the projection unit 10 and the polarization imaging unit 20 via communication interfaces. For example, it sends a digital grayscale image to the spatial light modulator 14 in the projection unit 10 via a digital video interface (such as HDMI), and sends a trigger signal to the polarization imaging unit 20 via a general purpose input / output (GPIO) or a dedicated communication bus. The polarization imaging unit 20 transmits the acquired raw image data to the data processing unit 30 via a high-speed data interface (such as USB 3.0 or GigEVision).

[0073] The data processing unit 30 internally runs proprietary measurement and analysis software, whose functional modules are responsible for executing the measurement method of this invention. Its main functions include:

[0074] System control and synchronization: Generate and send digital grayscale images to projection unit 10 in a predetermined sequence to form the first polarization grating and the second polarization grating; synchronously send acquisition commands to polarization imaging unit 20 each time the grating is projected to ensure accurate timing matching of data acquisition.

[0075] Data reception and preprocessing: The system receives raw image data transmitted from the polarization imaging unit 20. It processes the received raw image data to obtain intensity images in four polarization directions (0°, 45°, 90°, 135°). It calculates the Stokes vector component corresponding to each pixel coordinate in the intensity image.

[0076] Core algorithm execution: Based on the calculated Stokes vector, the linear polarization angle distribution is further calculated. By differentiating the linear polarization angle distribution with a pre-stored ideal reference model, a first moiré phase map and a second moiré phase map are generated. Subsequently, a joint solution algorithm and differential operations are executed to obtain the two-dimensional tilt field and differential anomaly map characterizing the region under test, respectively.

[0077] Results Generation and Output: The calculated two-dimensional tilt field data is converted into a visualized cloud map or vector arrow map. Thresholding is applied to the differential anomaly map to extract the location information of surface anomalies, which is then used as the measurement results for these anomalies. Finally, the tilt measurement results and the surface anomaly measurement results are displayed on the user interface or saved as a data file in a specified format.

[0078] The principle of the tilt measurement results of this invention is based on the modulation effect of the local tilt of the surface under test on the polarization state of the reflected light.

[0079] When a linearly polarized beam of light is incident on a tiny surface element of the region S to be measured, if the surface element is an ideal Lambertian surface, the polarization state of its reflected light will change. This change is related to the geometric relationship between the surface element's normal vector n and the direction of the incident light and the observation direction. Therefore, by accurately measuring the change in the polarization state of the reflected light, the normal direction of the tiny surface element, i.e., its tilt state, can be deduced.

[0080] In this embodiment, the projection unit 10 first projects a first polarization grating, the polarization direction of which varies periodically along a first spatial direction (e.g., the x-axis). Under ideal conditions, without considering the influence of surface tilt, its reference linear polarization angle distribution ψ on the imaging plane... ref,H (x, y) are known. When the grating is projected onto the test area S with local tilt, the linear polarization angle distribution measured by the polarization state imaging unit 20 is ψ. meas,H (x,y). First Moiré phase ΔΦ H (x,y) is defined as the linear polarization angle distribution ψ meas,H (x,y) and the reference linear polarization angle distribution ψ ref,H The difference between (x, y), this first Moiré phase contains information about the surface tilt.

[0081] Similarly, projection unit 10 projects a second polarization grating. When the polarization direction of the second polarization grating changes periodically along a second spatial direction (e.g., the y-axis) orthogonal to the first spatial direction, the second moiré phase ΔΦ can be obtained. V (x,y).

[0082] Moiré phase ΔΦ H (x,y) and ΔΦ V The coordinates (x, y) and the two orthogonal tilt components θ of the region S to be measured at the corresponding points (x, y) are related. x and θ y There exists a linear relationship between them. Where θ x =∂z / ∂x and θ y =∂z / ∂y, where z(x,y) represents the height distribution of the surface to be measured. This relationship can be described by a system of equations containing two linear equations.

[0083] Writing this system of equations in matrix form, we get:

[0084] ;

[0085] M(x,y) is a 2x2 coefficient matrix. The elements in M(x,y) are determined by the geometry of the measurement system (such as the illumination angle, observation angle, etc.) and are different at each pixel coordinate (x,y).

[0086] By solving the above matrix equations, the tilt component θ corresponding to each pixel coordinate can be uniquely calculated. x and θ y This is achieved by calculating the inverse of matrix M(x,y), which is the pre-calibrated system transfer function matrix C(x,y).

[0087] Therefore, the formula for calculating the tilt component can be clearly expressed as:

[0088] ;

[0089] Where, θ x (x,y) and θ y (x, y) represent the tilt components of the surface of the region to be measured at coordinates (x, y) along the first and second spatial directions, respectively; ΔΦ H (x,y) represents the phase value of the first moiré phase map generated by the first polarization grating at coordinates (x,y); ΔΦ V (x,y) is the phase value of the second moiré phase map generated by the second polarization grating at coordinate (x,y); C(x,y) is the 2x2 system transfer function matrix pre-calibrated at coordinate (x,y).

[0090] By performing this calculation on all pixel coordinates in the first and second moiré phase maps, a complete two-dimensional tilt field can be obtained, thereby characterizing the macroscopic tilt state of the entire region under test.

[0091] The principle of this invention for synchronously detecting surface anomalies is that there is an essential difference in the symmetry of the response of the intact region and the abnormal region to orthogonally polarized light within the test area S.

[0092] For a perfectly intact region with homogeneous physical and chemical properties and optical isotropy, such as a perfectly uniform paint coating, its reflection characteristics of incident light are independent of its polarization direction. Therefore, when two polarization gratings (a first polarization grating and a second polarization grating) with mutually orthogonal polarization directions are projected onto this intact region in a time-division manner, the orthogonally polarized light responses generated in the two measurement channels exhibit symmetry. This means that the first moiré phase diagram ΔΦ generated according to the first polarization grating and the second polarization grating, respectively, will have a symmetrical effect. H Second Mohr phase diagram ΔΦ V Apart from the inherent structural differences caused by the different grating directions, the local variation characteristics of the orthogonal polarized light response in this intact region should be consistent.

[0093] However, when certain early defects or damage exist on the surface of a suspension bridge, such as minor rust in the paint coating, surface scratches, water stains, or material degradation, these are considered anomalous areas. The microstructure of these anomalous areas changes, causing them to exhibit optical anisotropy. An optically anisotropic surface is characterized by its interaction with light (such as reflection and scattering) depending on the polarization direction of the incident light.

[0094] When an anomaly of this type of optical anisotropy exists in the test region S, the symmetry of the orthogonal polarization response is disrupted. The reflection response of this anomaly to a first polarization grating with polarization direction varying along a first spatial direction differs from the reflection response of this anomaly to a second polarization grating with polarization direction varying along a second spatial direction. This difference is directly reflected in the moiré phase diagram ΔΦ. H and ΔΦ V This causes the anomaly to produce an asymmetric local phase change at the pixel coordinates corresponding to the first and second moiré phase maps.

[0095] To identify and locate this asymmetry, this invention performs a differential operation on the two moiré phase maps. This operation aims to suppress the symmetrical common-mode signal components in the two moiré phase maps caused by macroscopic surface tilt, while amplifying the asymmetric differential-mode signal components caused by local optical anisotropy. In one specific embodiment, this differential operation is implemented using the following formula:

[0096] M diff (x,y)=|(ΔΦ H (x,y)) / σ H -(ΔΦ V (x,y)) / σ V |;

[0097] Among them, M diff (x,y) represents the value of the difference anomaly plot at coordinates (x,y); ΔΦ H (x,y) and ΔΦ V (x, y) represent the phase values ​​of the first and second Mohr phase diagrams at coordinates (x, y), respectively; σ H and σ V , respectively, are the local standard deviations of the first and second Mohr phase maps in a local neighborhood of coordinates (x, y).

[0098] In this calculation, the local standard deviation σ H and σ V It acts as a normalization factor. By dividing the phase value by its local standard deviation, the difference in phase variation amplitude caused by the macroscopic geometry of the surface can be eliminated, allowing the differential operation to more sensitively reflect the response asymmetry caused by the differences in the optical properties of the material itself. The calculated differential anomaly map M is shown below.diff In the diagram, regions with values ​​close to zero correspond to intact optically isotropic surfaces, while regions with values ​​significantly higher than the background indicate the locations of optically anisotropic anomalous points.

[0099] The measurement method of the present invention begins with step S1, namely the generation and projection of the polarization grating. This step is performed by the projection unit 10 under the control of the data processing unit 30.

[0100] First, to generate the first polarization grating, the data processing unit 30 internally calculates and generates a first digital grayscale image. In this first digital grayscale image, the grayscale value of a pixel coordinate is a periodic function of its position in a first spatial direction (e.g., the x-axis direction in the image coordinate system). For example, for an 8-bit first digital grayscale image, its grayscale value G at coordinates (x, y) is... H (x,y) can be obtained from G H (x,y)=mod(ax,256) is used to determine the grating spatial frequency, where a is a coefficient that determines the grating spatial frequency and mod is the modulo operation.

[0101] The data processing unit 30 sends this first digital grayscale image to the spatial light modulator 14 in the projection unit 10 via a data interface. The spatial light modulator 14 receives the first digital grayscale image, and the liquid crystal cells inside the spatial light modulator 14 apply a precise polarization direction rotation corresponding to the grayscale value to the incident uniformly linearly polarized light pre-processed by the polarizer 13, based on the grayscale value received by each pixel cell. As a result, the polarization direction on the cross-section of the light field emitted from the spatial light modulator 14 exhibits a preset periodic change in the first spatial direction, thereby forming a first polarization grating. This first polarization grating is then projected onto the test area S via the projection lens 15.

[0102] After acquiring the image corresponding to the first polarization grating, the data processing unit 30 generates a second digital grayscale image. In this second digital grayscale image, the grayscale value of a pixel coordinate is a periodic function of its position in a second spatial direction (e.g., the y-axis direction in the image coordinate system, which is orthogonal to the first spatial direction). The grayscale value G of the second digital grayscale image at coordinates (x, y) is... V (x,y) can be obtained from G V (x,y)=mod(by,256) is determined, where b is a coefficient that determines the spatial frequency of the grating.

[0103] The data processing unit 30 loads this second digital grayscale image into the spatial light modulator 14 to replace the first digital grayscale image. The spatial light modulator 14 generates a second polarization grating with a polarization direction that changes periodically along the second spatial direction in the same manner and projects it onto the same test area S. In this way, the system achieves time-division projection of the first polarization grating and the second polarization grating.

[0104] In another embodiment, the projection unit 10 can also be implemented using a digital light processing (DLP) projector. In this case, the DLP chip acts as a spatial light modulator. Since a standard DLP projector itself does not modulate polarization, a synchronously rotating filter wheel driven by a motor and containing multiple polarization filters with different orientations can be added behind its projection lens 15. When the DLP projects an intensity image corresponding to the first grating, the data processing unit 30 synchronously controls the filter wheel to place a polarizer of a specific orientation (e.g., a liquid crystal micrograting with a horizontally varying polarization direction) in the optical path to form the first polarization grating. When switching to the second grating, the DLP loads a new intensity image, and the filter wheel rotates to the corresponding second type of polarizer. This method can also achieve time-division projection of polarization gratings.

[0105] See attached document Figure 4 , Figure 4 This is a flowchart of the image-based method for measuring surface anomalies of suspension bridges according to the present invention. The technical content of the following method can be achieved through the above system part, and the method content is as follows:

[0106] When the first polarization grating is projected in step S1, the data processing unit 30 synchronously sends a trigger command to the polarization imaging unit 20 to perform polarization information acquisition.

[0107] The polarization imaging unit 20 images the first polarization grating reflected from the region S under test. In a preferred embodiment, when a "snapshot" focal plane polarization camera is used, the camera captures a raw mosaic image in a single exposure. This raw mosaic image is immediately transmitted to the data processing unit 30. The data processing unit 30 performs a de-mosaic algorithm on the raw mosaic image. This algorithm interpolates and reconstructs the raw mosaic image based on the known orientations of the four micro-polarizers in each 2x2 superpixel unit, thereby generating four pixel-to-pixel intensity images with the same resolution: I0(x,y), I... 45 (x,y),I 90 (x,y) and I 135 (x,y).

[0108] The data processing unit 30 then uses these four intensity images to calculate the three components S0, S1, and S2 of the Stokes vector for each pixel coordinate (x, y) in these four intensity images:

[0109] ;

[0110] Subsequently, based on the calculated Stokes vector components, the data processing unit 30 further calculates the linear polarization angle distribution ψ measured at each pixel coordinate. meas (x,y), this distribution is the first polarization information: ψ meas,H (x,y)=1 / 2arctan((S2(x,y)) / (S1(x,y))), where the subscript H indicates that the first polarization information is generated by the first polarization grating (e.g., a horizontal grating).

[0111] After acquiring the first polarization information, when step S1 switches and projects the second polarization grating, the above acquisition and calculation process is completely repeated. The polarization state imaging unit 20 acquires the second polarization grating reflected by the region S under test, and the data processing unit 30 processes the received raw image data in exactly the same way, ultimately generating the linear polarization angle distribution ψ corresponding to the second polarization grating. meas,V (x,y), this distribution is the second polarization information.

[0112] In step S2, the linear polarization angle distribution ψ is obtained. meas Next, the method proceeds to step S3, where the data processing unit 30 generates a moiré phase map. This step aims to extract the phase deviation caused by the geometry and surface characteristics of the region S under test from the measurement data.

[0113] The data processing unit 30 internally stores two ideal reference models: the first reference model ψ ref,H (x,y) and the second reference model ψ ref,V (x,y). First reference model ψ ref,H (x, y) represents the theoretical linear polarization angle distribution that should be formed at polarization state imaging unit 20 when the first polarization grating is projected onto an ideal plane without tilt or defects. Similarly, the second reference model ψ ref,V (x, y) represents the theoretical linear polarization angle distribution corresponding to the second polarization grating. These two reference models can be generated through mathematical calculations or obtained through a single calibration measurement of a standard planar sample.

[0114] To generate the first moiré phase map, the data processing unit 30 processes the first polarization information ψ obtained in step S2. meas,H (x,y) and the pre-stored first reference model ψ ref,H (x,y) performs a pixel-by-pixel difference operation, which follows the relationship: ΔΦ H (x,y)=ψ meas,H (x,y)-ψ ref,H The result of this operation is ΔΦ (x, y).H (x,y) is the first Mohr phase diagram.

[0115] Similarly, the data processing unit 30 processes the second polarization information ψ meas,V (x,y) and the pre-stored second reference model ψ ref,V (x,y) performs the same pixel-by-pixel difference operation to generate the second moiré phase map ΔΦ V (x,y): ΔΦ V (x,y)=ψ meas,V (x,y)-ψ ref,V (x,y).

[0116] Due to ψ meas The calculation involves the arctangent function, whose value is restricted to an interval of π. Therefore, the Mohr phase diagram ΔΦ generated by the above difference operation... H and ΔΦ V The range of values ​​is restricted to (-π, π], and this type of phase map is called a wrapped moiré phase map. Before proceeding to the next step, the data processing unit 30 performs a phase unwrapping algorithm on the two wrapped moiré phase maps to eliminate the 2π phase jump, thereby restoring a continuous phase distribution that can truly reflect phase changes, providing a data basis for subsequent tilt field calculations and anomaly identification.

[0117] After generating a continuous moiré phase diagram in step S3, the data processing unit 30 executes step S4 to process the first moiré phase diagram ΔΦ. H (x,y) and the second Mohr phase diagram ΔΦ V (x,y) is processed to simultaneously acquire the tilt measurement results of the area to be measured and the measurement results of surface anomalies.

[0118] To obtain the tilt measurement results for the area to be measured, the data processing unit 30 jointly solves the two moiré phase maps. This solution process is based on a system transfer function matrix C(x,y) pre-obtained through a calibration process and stored in the data processing unit 30. For each pixel coordinate (x,y) in the moiré phase map, the data processing unit 30 performs the following matrix operations to calculate the two-dimensional tilt component θ of each pixel coordinate (x,y). x and θ y :

[0119] ;

[0120] By performing this calculation on the coordinates of all pixels within the field of view, the data processing unit 30 obtains a complete two-dimensional tilt field composed of the tilt components of all pixel coordinates. Finally, the data processing unit 30 visualizes this two-dimensional tilt field data, for example, rendering it as a contour map whose color varies with the tilt amplitude, or a vector arrow map whose arrow direction and length represent the tilt direction and magnitude. This contour map and vector arrow map are then output or displayed as tilt measurement results.

[0121] Simultaneously, the data processing unit 30 uses the response difference between the first and second Moiré phase maps to determine the measurement results of surface anomalies. The data processing unit 30 performs differential operations on the two unwrapped Moiré phase maps to generate a differential anomaly map M. diff (x,y). In one specific embodiment, the operation specifically includes local normalization and difference calculation of the first and second Moiré phase maps: M diff (x,y)=|(ΔΦ H (x,y)) / σ H -(ΔΦ V (x,y)) / σ V |, where σ H and σ V , respectively, are the standard deviations of the first and second Moiré phase maps in their local neighborhoods centered at coordinates (x,y). This normalization step aims to eliminate response amplitude variations caused by the macroscopic geometry of the surface, thereby enhancing sensitivity to asymmetric responses caused by the optical anisotropy of the surface material.

[0122] Generate differential anomaly map M diff Subsequently, the data processing unit 30 compares it with a preset numerical threshold. In the differential anomaly map, pixel coordinates with values ​​higher than the threshold are identified as surface anomalies. Finally, the data processing unit 30 extracts the location information of all identified surface anomalies and outputs this location information as the measurement results of the surface anomalies, or overlays and marks it on the tilt measurement result map.

[0123] In a preferred embodiment, to ensure the accuracy of the measurement results, the method of the present invention further includes a system calibration step performed before the formal measurement and a self-calibration step performed during data processing.

[0124] The purpose of the system calibration process is to accurately determine the system transfer function matrix C(x,y) used in step S4. This process employs a high-precision planar calibration plate, which is mounted on a two-dimensional turntable capable of precise rotation along two orthogonal axes (x-axis and y-axis). First, the calibration plate is placed in a zero-position orientation (i.e., θ) perpendicular to the optical axis. x =0,θy =0). Subsequently, the calibration plate is precisely rotated about the y-axis by a known small angle Δθ. x And rotating about the x-axis by a known small angle Δθ y At each attitude, complete steps S1 to S3 are performed to obtain the corresponding Moiré phase diagram. This is achieved by analyzing the known tilt amount (Δθ). x ,Δθ y The relationship between the coordinates (x, y) and the resulting changes in the Moiré phase diagram allows for the inverse solution of the corresponding transfer function matrix C(x, y) for each coordinate (x, y) in the field of view. This matrix is ​​digitized and stored in the data processing unit 30 for subsequent measurement use.

[0125] More specifically, the calibration process is as follows:

[0126] Set the calibration plate to the zero position (θ) x =0,θ y =0), and a set of reference moiré phase diagrams ΔΦ were collected and calculated. H,0 and ΔΦ V,0 .

[0127] Rotate the calibration plate precisely around the y-axis by a known small angle Δθ. x The attitude at this time is (θ) x =Δθ x ,θ y =0). The moiré phase diagram ΔΦ was obtained by re-acquiring and calculating. H,1 and ΔΦ V,1 .

[0128] Return the calibration plate to its zero position, and then rotate it precisely around the x-axis by a known small angle Δθ. y The attitude at this time is (θ) x =0,θ y =Δθ y The moiré phase diagram ΔΦ was obtained by re-acquiring and calculating the moiré phase diagram. H,2 and ΔΦ V,2 .

[0129] Based on the linear relationship in step S4, for each pixel coordinate (x, y), we have:

[0130] ;

[0131] ;

[0132] Therefore, the inverse matrix C of the transfer function matrix can be solved point by point. -1 The four elements of the given matrix are then inverted to obtain C(x,y). By performing this operation on all pixels in the field of view, the complete system transfer function matrix can be obtained.

[0133] The two formulas above are for solving the transfer function matrix C (or its inverse matrix C). -1 The specific mathematical steps involved. Our goal is to find C. -1 All four unknown elements of this 2x2 matrix.

[0134] First, C -1 Write it in the form of specific elements:

[0135] ;

[0136] For the first formula:

[0137] Physical operation: We rotate the calibration plate around the y-axis by a known angle Δθ. x Meanwhile, the rotation about the x-axis remains 0.

[0138] Input: The tilt change corresponding to this operation is a vector. .

[0139] Output: We measured a change in the Moiré phase diagram compared to the zero-position attitude; this change is a vector. .

[0140] Mathematical relationship: Substitute these into (phase change) = C -1 From the relationship of (angle change), we obtain:

[0141] ;

[0142] Solution: From this matrix multiplication, we can see that ΔΦ V,1 -ΔΦ V,0 =c' 11 Δθ x And ΔΦ H,1 -ΔΦ H,0 =c' 21 Δθ x Since the phase change on the left side of the equation is measured, Δθ x Since it is known, we can directly calculate c'. 11 and c' 21 These two values. This is equivalent to us determining C. -1 The first column of the matrix.

[0143] For the second formula:

[0144] Physical operation: Similarly, we rotate the calibration plate only around the x-axis by a known angle Δθ. y Meanwhile, the rotation around the y-axis remains 0.

[0145] Input: The amount of tilt change is .

[0146] Output: The measured phase change is .

[0147] Mathematical relationship:

[0148] Solution: From this, we can calculate c' 12 and c' 22 These two values. This is equivalent to us determining C. -1 The second column of the matrix.

[0149] Through these two steps, we have successfully determined C. -1 All four elements of the matrix. Finally, check C. -1 By finding the inverse once, we obtain the system transfer function matrix C that we ultimately need.

[0150] The self-calibration process is performed during the joint solution in step S4, utilizing the data redundancy provided by the two measurements (the first polarization grating and the second polarization grating) to compensate for systematic errors. The physical basis of this process is the two-dimensional tilting field (θ) generated by any continuous smooth surface. x ,θ y The integrability condition must be satisfied, that is, their mixed partial derivatives must be equal: ∂θ x / ∂y=∂θ y / ∂x. In actual measurements, the calculated tilt field caused by systematic errors (such as optical distortion or slight component misalignment) may not completely satisfy this condition. Therefore, when performing the solution, the data processing unit 30 can use the satisfaction of the integrability condition as a constraint, and adjust the solution result through an optimization algorithm (such as the least squares method), or fine-tune the transfer function matrix C(x,y), so as to minimize the integrability error of the final output two-dimensional tilt field. This process uses the measurement data itself to correct the system model in real time, thereby compensating for the small drift of the system state and further improving the accuracy and reliability of the measurement results.

[0151] Please refer to the appendix. Figure 5-7 The invention demonstrates a scenario and measurement results of testing a standard test sample using the system of this invention.

[0152] In this embodiment, the measurement target is a specially prepared standard test sample T, as shown in the attached figure. Figure 6 As shown, the sample consists of a flat metal substrate with a smooth cylindrical protrusion machined in its central region to simulate macroscopic geometry. Furthermore, to simulate microscopic surface anomalies, an extremely thin birefringent film with known optical anisotropy was pre-attached to a flat area of ​​the sample substrate.

[0153] The measurement system is placed approximately 1 meter directly in front of the standard test specimen T. The operator uses the control software interface of the data processing unit 30 to select the area to be measured, encompassing the entire specimen.

[0154] After the measurement begins, the system automatically executes the following process:

[0155] The projection unit 10 first projects the first polarization grating (the polarization direction changes periodically along the horizontal direction).

[0156] The polarization imaging unit 20 simultaneously acquires a raw image data.

[0157] Projection unit 10 switches and projects a second polarization grating (the polarization direction changes periodically along the vertical direction).

[0158] The polarization imaging unit 20 acquires the second raw image data. The entire data acquisition process takes less than 0.1 seconds. The data processing unit 30 then automatically processes the two acquired raw image data and simultaneously outputs the two measurement results.

[0159] a. Acquisition and Analysis of Inclination Measurement Results: The two-dimensional tilt field cloud map generated by the data processing unit 30 is displayed on the screen, as shown in the attached figure. Figure 7 As shown in the figure, this contour plot clearly reflects the macroscopic geometry of the standard test specimen T. It can be observed that the flat base region of the specimen exhibits a near-zero uniform inclination (shown in blue), while the cylindrical protrusion shows a smooth, gradient-radial change in inclination around its center, accurately reproducing the known geometric contours of the specimen.

[0160] b. Simultaneous detection and verification of surface anomalies: Simultaneously, the differential anomaly map generated by the data processing unit 30 is also output, as shown in the attached figure. Figure 7 As shown. In this Figure 7 In the image, the values ​​for most areas (corresponding to a uniform metal surface) are close to zero, appearing as a dark background. However, Figure 7 One location exhibits a bright dot-like signal, known as a bright anomaly signal. The location of this bright anomaly signal perfectly matches the position of the ultrathin birefringent film pre-attached to the sample surface. This result strongly demonstrates that the method of this invention has extremely high detection sensitivity for optical anisotropy caused by physical properties such as material birefringence, and can reliably identify microscopic surface anomalies.

[0161] This embodiment fully demonstrates that the measurement system and method provided by the present invention can simultaneously acquire macroscopic three-dimensional geometric information and microscopic physical anomaly information of the surface of the object under test through a single rapid, non-contact measurement, achieving efficient integration of multimodal detection functions and possessing extremely high engineering and scientific research application value.

Claims

1. A method for measuring surface anomalies of a suspension bridge based on images, characterized in that, Includes the following steps: S1. Project a first polarization grating and a second polarization grating onto the test area of ​​the suspension bridge in a time-division manner, wherein the polarization direction of the first polarization grating changes periodically along a first spatial direction, and the polarization direction of the second polarization grating changes periodically along a second spatial direction orthogonal to the first spatial direction. S2. Using a polarization state imaging unit, the polarization information of the first polarization grating and the second polarization grating after reflection from the area under test are acquired respectively, to obtain the first polarization information and the second polarization information. The acquisition of polarization information using the polarization state imaging unit specifically includes: The polarization state imaging unit can obtain intensity images in at least four polarization directions in a single exposure. Polarization information, including the Stokes vector, is calculated based on the intensity images of the at least four polarization directions. The linear polarization angle distribution is calculated from the Stokes vector to obtain the first polarization information and the second polarization information; S3. Generate a first moiré phase map and a second moiré phase map based on the first polarization information and the second polarization information, respectively. Specifically, generating the first moiré phase map and the second moiré phase map includes: The linear polarization angle distribution calculated from the first polarization information and the second polarization information is subjected to a difference operation with the ideal reference models of the first polarization grating and the second polarization grating, respectively, to generate the first moiré phase map and the second moiré phase map. S4. Process the first moiré phase map and the second moiré phase map to simultaneously acquire two measurement results, which are as follows: a. Calculate the two-dimensional tilt field that characterizes the tilt state of each point on the area to be measured, and determine the tilt measurement result of the area to be measured based on the two-dimensional tilt field; b. Based on the response difference between the first and second moiré phase maps, identify and locate surface anomalies in the area to be measured, and determine the measurement results of the surface anomalies.

2. The image-based method for measuring surface anomalies of a suspension bridge according to claim 1, characterized in that, In step S4, determining the tilt measurement result of the area to be measured specifically includes: Based on the pre-calibrated system transfer function matrix, the first and second moiré phase diagrams are jointly solved to obtain the two-dimensional tilted field; The two-dimensional tilt field is converted into a visual cloud map or vector arrow map, which serves as the tilt measurement result.

3. The image-based method for measuring surface anomalies of a suspension bridge according to claim 1, characterized in that, In step S4, determining the measurement results of the surface anomaly points specifically includes: A difference operation is performed on the first and second Mohr phase maps to generate a difference anomaly map, which represents the response difference. Points with values ​​higher than a preset threshold in the differential anomaly map are identified as surface anomaly points, and their location information is output as the measurement result of the surface anomaly points.

4. The image-based method for measuring surface anomalies of a suspension bridge according to claim 3, characterized in that, The difference operation specifically includes: The first and second moiré phase maps are normalized, and the difference between the normalized first and second moiré phase maps is calculated to generate the differential anomaly map.

5. The image-based method for measuring surface anomalies of a suspension bridge according to claim 2, characterized in that, The joint solution specifically refers to: The integrability condition that the two-dimensional tilted field must satisfy is used as an optimization constraint. The first and second moiré phase diagrams are solved by an optimization algorithm to obtain the two-dimensional tilted field. The calculation error caused by optical distortion or component misalignment is corrected based on the two-dimensional tilted field.

6. The image-based method for measuring surface anomalies of a suspension bridge according to claim 1, characterized in that, In step S1, the generation and switching of the first polarization grating and the second polarization grating are both performed using a spatial light modulator.

7. The image-based method for measuring surface anomalies of a suspension bridge according to claim 1, characterized in that, The first spatial direction is horizontal, and the second spatial direction is vertical.

8. A measurement system for implementing the image-based method for measuring surface anomalies of suspension bridges as described in claim 1, characterized in that, include: A projection unit is used to project a first polarization grating and a second polarization grating onto the test area of ​​the suspension bridge in a time-division manner; A polarization state imaging unit is used to acquire the first polarization information and the second polarization information after reflection from the region under test, respectively. A data processing unit, connected to the projection unit and the polarization state imaging unit, is used to generate a first moiré phase map and a second moiré phase map based on the first polarization information and the second polarization information, respectively, and to process the first moiré phase map and the second moiré phase map, and simultaneously acquire the tilt measurement results and surface anomaly point measurement results of the area to be measured.

Images