CN115375924A - Bridge health monitoring method and system based on image recognition - Google Patents

Bridge health monitoring method and system based on image recognition Download PDF

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CN115375924A
CN115375924A CN202211118750.XA CN202211118750A CN115375924A CN 115375924 A CN115375924 A CN 115375924A CN 202211118750 A CN202211118750 A CN 202211118750A CN 115375924 A CN115375924 A CN 115375924A
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bridge
image
damage
health monitoring
modal
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孔烜
罗奎
易金鑫
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Hunan University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/54—Extraction of image or video features relating to texture
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H9/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0008—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings of bridges
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0066—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by exciting or detecting vibration or acceleration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/30—Noise filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/62—Extraction of image or video features relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking

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Abstract

The invention relates to the technical field of bridge health monitoring, and discloses a bridge health monitoring method and a bridge health monitoring system based on image recognition. Therefore, the damage position and the damage degree of the bridge can be accurately identified based on the image identification mode, and the potential safety hazard of the bridge can be timely found.

Description

Bridge health monitoring method and system based on image recognition
Technical Field
The invention relates to the technical field of bridge health monitoring, in particular to a bridge health monitoring method and system based on image recognition.
Background
Bridge Structure vibration measurement is the key to bridge Structure Health Monitoring (SHM). The modal parameters (natural frequency, vibration mode, damping ratio and the like) are important indexes reflecting the health condition of the structure, damage and state change of the bridge structure can be identified through the change of the modal parameters, and the service performance of the bridge structure is evaluated. During the operation of the bridge, the vibration phenomenon inevitably occurs, and how to rapidly measure the tiny vibration response of the bridge is the premise of ensuring the safe operation of the bridge, and is a powerful guarantee for realizing the real-time monitoring of the health condition of the bridge.
At present, a common instrument for measuring the vibration characteristics of the bridge is an acceleration sensor, but the acceleration sensor has the defects of high cost, difficulty in installation, limited measuring points, low measuring precision, poor real-time performance and the like, and the requirement for monitoring the dynamic response of the bridge in real time is difficult to meet. Other conventional measurement methods such as a level gauge, a dial gauge and a total station are difficult to perform dynamic measurement, and although a Global Positioning System (GPS) can achieve dynamic measurement, the debugging and installation are very complicated, and factors such as a complex bridge region working environment, a satellite and weather affect the measurement precision and the measurement time. Most of the existing non-contact vibration measurement methods based on computer vision technology are only suitable for scenes with large structural vibration displacement amplitude and are difficult to be suitable for micro-vibration measurement of structures, and bridge micro-vibration signals just contain important information. Therefore, the existing bridge monitoring mode has low precision, and potential safety hazards of the bridge cannot be found in time.
Disclosure of Invention
The invention provides a bridge health monitoring method and system based on image recognition, and aims to solve the problems that an existing bridge monitoring mode is low in precision and cannot find potential safety hazards in a bridge in time.
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, the invention provides a bridge health monitoring method based on image recognition, which comprises the following steps:
acquiring a first image of a bridge to be monitored, and preprocessing the first image to obtain a second image;
performing spatial domain decomposition on the second image to obtain a corresponding target image sequence;
calculating sub-pixel coordinates of the centroid positions of continuous image frames in the target image sequence, and determining real displacement time course response of the bridge to be monitored according to the sub-pixel coordinates of the centroid positions;
extracting bridge modal parameters from the real displacement time-course response by using a Hankel dynamic modal decomposition method, wherein the bridge modal parameters comprise natural frequency, vibration mode and damping ratio;
and establishing a modal shape calculation model according to the bridge modal parameters, and identifying the bridge damage position and the bridge damage degree according to the modal shape calculation model.
In a second aspect, the present invention provides a bridge health monitoring system based on image recognition, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method according to the first aspect.
Has the advantages that:
the bridge health monitoring method based on image recognition provided by the invention comprises the steps of firstly recognizing an image of a bridge to be monitored, extracting sub-pixel level displacement time-course response of bridge micro vibration from a continuous image frame by utilizing a discrete centroid search algorithm, obtaining modal parameters of the bridge micro vibration by a Hankel dynamic modal decomposition method, establishing a modal shape calculation model according to the modal parameters of the bridge, and recognizing the damage position and the damage degree of the bridge according to the modal shape calculation model. Therefore, the damage position and the damage degree of the bridge can be accurately identified based on the image identification mode, and the potential safety hazard of the bridge can be timely found.
In the preferred scheme, the final target image sequence is obtained through the noise reduction processing of the initial target image sequence, so that the measurement precision is enhanced, and the accurate real displacement time-course response and modal parameters are conveniently obtained.
In a preferred scheme, each image frame in the amplified target image sequence is divided into a plurality of grids of the cutting area, the centroid of the discretization object is calculated in each grid by using an Otsu threshold segmentation algorithm, and the sub-pixel-level coordinates of the centroid positions of all continuous image frames are calculated, so that more accurate displacement time-course response can be obtained.
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FIG. 1 is a flowchart of a bridge monitoring method based on image recognition according to a preferred embodiment of the present invention;
FIG. 2 is a second flowchart of a bridge monitoring method based on image recognition according to a preferred embodiment of the present invention;
FIG. 3 is a schematic diagram of the centroid positions of two consecutive images according to the preferred embodiment of the present invention;
fig. 4 is a schematic diagram illustrating the real dynamic displacement time-course response obtaining principle of the bridge micro-vibration according to the preferred embodiment of the present invention.
Detailed Description
The technical solutions of the present invention are described clearly and completely below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The use of "first," "second," and the like, herein does not denote any order, quantity, or importance, but rather the terms "first," "second," and the like are used to distinguish one element from another. Also, the use of the terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used only to indicate relative positional relationships, and when the absolute position of the object to be described is changed, the relative positional relationships are changed accordingly.
Referring to fig. 1-2, the present application provides a bridge health monitoring method based on image recognition, including:
acquiring a first image of a bridge to be monitored, and preprocessing the first image to obtain a second image;
performing spatial domain decomposition on the second image to obtain a corresponding target image sequence;
calculating sub-pixel coordinates of the centroid positions of continuous image frames in the target image sequence, and determining real displacement time-course response of the bridge to be monitored according to the sub-pixel coordinates of the centroid positions;
extracting bridge modal parameters from the real displacement time-course response by using a Hankel dynamic modal decomposition method, wherein the bridge modal parameters comprise natural frequency, vibration mode and damping ratio;
and establishing a modal shape calculation model according to the modal parameters of the bridge, and identifying the damage position and the damage degree of the bridge according to the modal shape calculation model.
In this embodiment, the obtaining of the first image of the bridge to be monitored may be by shooting with a high-speed camera.
The bridge health monitoring method based on image identification comprises the steps of firstly identifying an image of a bridge to be monitored, extracting sub-pixel-level displacement time-course response of bridge micro vibration from a continuous image frame by utilizing a discrete centroid search algorithm, obtaining modal parameters of the bridge micro vibration by a Hankel dynamic modal decomposition method, establishing a modal shape calculation model according to the modal parameters of the bridge, and identifying a bridge damage position and a bridge damage degree according to the modal shape calculation model. Therefore, the damage position and the damage degree of the bridge can be accurately identified based on the image identification mode, and the potential safety hazard of the bridge can be timely found.
Optionally, the preprocessing the first image to obtain a second image includes:
and rotating, cutting and scaling the first image to obtain a second image.
In this optional embodiment, the digital image processing software may be used to perform preprocessing such as rotation, cropping, and scaling on the first image, so that the noise in the image sequence may be removed primarily through the preprocessing, thereby avoiding that the noise is amplified in equal proportion when the amplification processing is performed by using the wideband phase motion amplification algorithm, and eliminating the influence of the noise on the image amplification result.
Optionally, performing spatial decomposition on the second image to obtain a corresponding target image sequence, including:
performing spatial domain decomposition on the second image to obtain an initial target image sequence, wherein the initial target image sequence comprises a first residual error part, different-frequency baseband phase information and a second residual error part;
and extracting the texture features of the initial target image sequence by using a two-dimensional Gabor wavelet filter, and performing translation processing on image information except the texture features in the target image sequence to finish the denoising processing on the initial target image sequence to obtain a final target image sequence.
In this optional embodiment, the Complex Steerable Pyramid (CSP) is used to perform spatial domain decomposition on the second image to obtain image sequences with different scales, different directions and different positions, so that the image sequences realize the expression of the local relative motion of the local position in the micro-vibration video of the bridge structure to be measured. It should be noted that the initial target image sequence obtained after decomposition includes a first residual, different-frequency baseband phase information, and a second residual, where the first residual is a high-pass residual, the second residual is a low-pass residual, and the different-frequency baseband phase information is middle different-frequency baseband phase information.
In this embodiment of the present application, after an initial target image sequence is obtained, it is necessary to further perform denoising processing on the initial target image sequence to obtain an image sequence to be measured, specifically, a two-dimensional Gabor wavelet filter is used to extract texture features of the initial target image sequence, and image information in the initial target image sequence except the texture features is subjected to translation processing to complete denoising processing on the initial target image sequence, so as to obtain a final target image sequence. It should be noted that the two-dimensional Gabor wavelet filter is sensitive to the edge information of the image, and can provide good direction selection and scale selection characteristics, and in addition, the two-dimensional Gabor wavelet filter can be used for spatial filtering. It can be understood that the two-dimensional Gabor wavelet filter is used for noise reduction, and artifacts generated by noise during motion amplification are effectively avoided.
In this embodiment, the two-dimensional Gabor wavelet filter is a sine function modulated by a gaussian function, and the complex expression thereof is as follows:
Figure 900128DEST_PATH_IMAGE001
；（1）
the solid part is as follows:
Figure 817268DEST_PATH_IMAGE002
；（2）
the dotted portion is shown below;
Figure 393743DEST_PATH_IMAGE003
；（3）
in the formula (I), the compound is shown in the specification,
Figure 788952DEST_PATH_IMAGE004
a wavelength representing a sine function;
Figure 134483DEST_PATH_IMAGE005
a phase offset representing a tuning function;
Figure 956945DEST_PATH_IMAGE006
determining a spatial aspect ratio of the two-dimensional Gabor function;
Figure 20716DEST_PATH_IMAGE007
determining the size of the acceptable area of the kernel of the two-dimensional Gabor filter for the standard deviation of the Gaussian function;
Figure 219616DEST_PATH_IMAGE008
represents the direction of a two-dimensional Gabor wavelet filter kernel, and
Figure 419654DEST_PATH_IMAGE009
；
Figure 413017DEST_PATH_IMAGE010
and
Figure 964084DEST_PATH_IMAGE011
is a spatial position variable; exp represents the exponential calculation;ithe number of the units of the imaginary number is expressed,
Figure 701096DEST_PATH_IMAGE012
。
it should be noted that it is preferable to provide,
Figure 755640DEST_PATH_IMAGE013
and
Figure 185484DEST_PATH_IMAGE014
respectively representing directional information and spatial information of a sequence of video images, which satisfy the following relation:
Figure 223847DEST_PATH_IMAGE015
。（4）
optionally, before calculating the sub-pixel level coordinates of the centroid position of consecutive image frames in the target image sequence, the method further comprises:
amplifying the target image sequence based on different frequency baseband phase information and a preset amplification factor;
calculating the sub-pixel-level coordinates of the centroid positions of continuous image frames in the target image sequence, and determining the real displacement time-course response of the bridge to be monitored according to the sub-pixel-level coordinates of the centroid positions, wherein the method comprises the following steps:
dividing each image frame in the amplified target image sequence into a plurality of grids of a cutting area, calculating the centroid of a discretized object in each grid by using an Otsu threshold segmentation algorithm, calculating the subpixel level coordinates of the centroid positions of all continuous image frames, subtracting the centroid coordinates of the subsequent image frames starting from the 2 nd image frame from the centroid coordinates of the 1 st image frame to obtain the subpixel level displacement time course response of the bridge to be monitored, and converting the subpixel level displacement time course response of the bridge to be monitored into a real displacement time course response according to a scale factor.
In this embodiment, the specific steps of the amplification process are as follows:
selecting a wideband frequency baseband of a region of interest (ROI) in intermediate different frequency baseband phase information
Figure 498971DEST_PATH_IMAGE016
And setting an appropriate amplification factor
Figure 142442DEST_PATH_IMAGE017
And as a preset amplification factor, amplifying the selected broadband frequency baseband by adopting the preset amplification factor to realize the amplification of the tiny vibration amplitude of the frequency baseband of interest. And adding the amplified broadband frequency baseband back to the high-pass residual error part and the low-pass residual error part image sequence, and reconstructing the amplified image sequence by using a complex-direction controllable pyramid to output the amplified video. Wherein the content of the first and second substances,
Figure 274346DEST_PATH_IMAGE018
a low frequency cutoff frequency for the selected wideband frequency baseband;
Figure 3268DEST_PATH_IMAGE019
the high frequency cutoff frequency of the selected wideband frequency baseband.
The principle of the broadband phase motion amplification algorithm is described as follows, and the spatial domain signal of the bridge vibration is assumed to be
Figure 613240DEST_PATH_IMAGE020
，
Figure 111218DEST_PATH_IMAGE021
Is composed of
Figure 679603DEST_PATH_IMAGE022
Small vibration displacements occurring in the time domain, by presettingAmplification factor of
Figure 630241DEST_PATH_IMAGE017
Hopefully, the amplified vertical vibration airspace signal of the bridge is
Figure 43905DEST_PATH_IMAGE023
. For broadband phase motion amplification, firstly, a space domain signal of bridge vibration is obtained
Figure 396389DEST_PATH_IMAGE024
Represented as a superposition of a series of complex sinusoidal signals, as shown in equation (5).
Figure 338937DEST_PATH_IMAGE025
； （5）
In the formula (I), the compound is shown in the specification,
Figure 573609DEST_PATH_IMAGE026
is the circular frequency of a certain sub-sinusoidal signal;
Figure 994226DEST_PATH_IMAGE027
is the amplitude of a certain sub-sinusoidal signal;
Figure 997954DEST_PATH_IMAGE028
is a spatial position variable.
For a circular frequency of
Figure 111404DEST_PATH_IMAGE029
Has the following complex sinusoidal signals:
Figure 833372DEST_PATH_IMAGE030
； （6）
Figure 57680DEST_PATH_IMAGE031
representing a complex sinusoidal signal having a phase of
Figure 915915DEST_PATH_IMAGE032
Performing time-domain filtering on the signal to remove
Figure 200266DEST_PATH_IMAGE033
Thus obtaining the following components:
Figure 143951DEST_PATH_IMAGE034
； （7）
Figure 171950DEST_PATH_IMAGE035
indicating the phase difference.
Using a broadband phase motion amplification algorithm pair
Figure 353532DEST_PATH_IMAGE036
Amplification of
Figure 339943DEST_PATH_IMAGE017
Adding after doubling
Figure 239766DEST_PATH_IMAGE037
To obtain the result of formula (8)
Figure 337035DEST_PATH_IMAGE038
； （8）
Figure 638703DEST_PATH_IMAGE039
Representing the amplified complex sinusoidal signal, amplifying the signal
Figure 796015DEST_PATH_IMAGE040
Adding back the original signal, i.e. obtaining a broadband frequency base band
Figure 183134DEST_PATH_IMAGE016
The broadband phase motion below amplifies the result. Thereby obtaining an enlarged sequence of target images.
Further, a discrete centroid search algorithm is used for extracting dynamic displacement time-course response of the micro vibration of the bridge structure from the amplified target image sequence, and the steps are as follows:
dividing the enlarged bridge vibration image frame into a plurality of grids of cutting areas, and calculating a discretization object in each grid by using an Otsu threshold segmentation algorithm
Figure 84094DEST_PATH_IMAGE041
The center of mass of the lens. Calculating the sub-pixel level coordinates of the centroid positions of all the continuous image frames, subtracting the centroid coordinates of the subsequent frame images starting from the 2 nd frame image from the centroid coordinates of the 1 st frame image to obtain the sub-pixel level displacement time course response of the bridge vibration, wherein the centroid positions of the continuous two frame images are shown in figure 3.
It is worth pointing out that compared with Digital Image Correlation (DIC), an optical flow method, an edge detection algorithm and a target tracking algorithm based on deep learning, the discrete centroid search algorithm in the present application has the advantages of small interference from Image background noise, strong real-time performance, high displacement precision and the like.
Further, after the sub-pixel level dynamic displacement time course response of the bridge micro vibration is obtained, the sub-pixel level dynamic displacement time course can be converted into the physical displacement time course response according to the scale factor.
In particular, the scale factor is calculated in different ways under different conditions, when the optical axis of the camera is perpendicular to the plane of the bridge structure, i.e. the optical axis is collinear with the normal of the plane of the structure,sthe scale factor is shown in formula (9) or formula (10).
Figure 974689DEST_PATH_IMAGE042
； (9)
Or:
Figure 568482DEST_PATH_IMAGE043
； (10)
in the formula (I), the compound is shown in the specification,Dselecting the size of an object in the structural plane;dfor its corresponding number of pixels in the image plane;fis the focal length of the lens;Zis the distance of the camera to the plane of the structure;
Figure 177318DEST_PATH_IMAGE044
is the pixel size.
When the optical axis of the camera is not perpendicular to the plane of the bridge structure, that is, the optical axis forms an included angle with the normal of the plane of the bridge structure
Figure 881968DEST_PATH_IMAGE008
Time scale factorsAs shown in equation (11).
Figure 892650DEST_PATH_IMAGE045
； (11)
When the broadband phase motion amplification algorithm is used for amplifying the micro vibration of the bridge structure, the micro vibration displacement amplitude of the bridge is amplified
Figure 657344DEST_PATH_IMAGE017
And the physical displacement time-course response extracted by utilizing the discrete centroid search algorithm is not the real displacement time-course response of the micro vibration of the bridge. Suppose that
Figure 753475DEST_PATH_IMAGE046
Is the real displacement of the micro vibration of the bridge,
Figure 465080DEST_PATH_IMAGE047
is the displacement amplitude of the micro vibration of the bridge,
Figure 127005DEST_PATH_IMAGE048
errors are identified for displacements caused by video illumination variation noise. The displacement of the structure without amplification is
Figure 265862DEST_PATH_IMAGE049
The displacement after the amplification treatment of the micro vibration of the bridge is
Figure 380449DEST_PATH_IMAGE050
. For the amplified displacement
Figure 630165DEST_PATH_IMAGE051
The real dynamic displacement time-course response of the bridge micro-vibration can be obtained by performing the motion normalization processing, and the principle of obtaining the real dynamic displacement time-course response of the bridge micro-vibration is shown in fig. 4. From the equation (12), it can be seen that the influence of noise in the video on the identification of the bridge minute vibration displacement can be effectively reduced based on the broadband phase motion amplification processing.
Figure 412176DEST_PATH_IMAGE052
； （12）
Further, the Hankel dynamic modal decomposition method is used for extracting bridge modal parameters (natural frequency, vibration mode and damping ratio) from the real dynamic displacement time-course response of the bridge, and particularly, the Hankel dynamic modal decomposition method can be used for extracting the multipoint dynamic displacement time-course response of the bridge
Figure 721934DEST_PATH_IMAGE053
Forming a Hankel matrix
Figure 323817DEST_PATH_IMAGE054
As shown in formula (13).
Figure 377224DEST_PATH_IMAGE055
； （13）
In the formula (I), the compound is shown in the specification,m、n、pand M are both positive integers, and M is a positive integer,
Figure 13741DEST_PATH_IMAGE056
denotes the firstkIs spotted on
Figure 494401DEST_PATH_IMAGE057
The moment of the dynamic displacement.
The specific steps of extracting the modal parameters of the bridge by using a Hankel dynamic modal decomposition method are as follows:
(1) Calculating a scale factor
Figure 318001DEST_PATH_IMAGE058
The calculation formula satisfies the following relation:
Figure 175098DEST_PATH_IMAGE059
； （14）
in the formula (I), wherein
Figure 869385DEST_PATH_IMAGE060
Is a matrix
Figure 583263DEST_PATH_IMAGE061
The last column of (a) is,
Figure 894159DEST_PATH_IMAGE062
is that
Figure 554947DEST_PATH_IMAGE061
The first sub-block of the last column of (a).
(2) The Hankel matrix is composed of equation (15).
Figure 369319DEST_PATH_IMAGE063
；（15）
In the formula (I), the compound is shown in the specification,
Figure 988520DEST_PATH_IMAGE064
forward in time for the same Hankel matrix;
Figure 255553DEST_PATH_IMAGE065
and
Figure 454453DEST_PATH_IMAGE066
two adjacent time data matrices are represented.
(3) XThe truncated Singular Value Decomposition (SVD) of (a) is calculated as shown in equation (16).
Figure 388911DEST_PATH_IMAGE067
； （16）
In the formula (I), the compound is shown in the specification,
Figure 647854DEST_PATH_IMAGE068
and
Figure 198921DEST_PATH_IMAGE069
two unitary matrices obtained for singular value decomposition,
Figure 935933DEST_PATH_IMAGE070
is composed of
Figure 990477DEST_PATH_IMAGE069
The companion matrix of (a);
Figure 420321DEST_PATH_IMAGE071
is a diagonal matrix with diagonal elements of
Figure 193105DEST_PATH_IMAGE072
The number of the singular values is,
Figure 733808DEST_PATH_IMAGE072
is a positive integer.
(4) Operator matrix
Figure 174016DEST_PATH_IMAGE073
As shown in equation (17).
Figure 509183DEST_PATH_IMAGE074
； （17）
In the formula (I), the compound is shown in the specification,
Figure 238104DEST_PATH_IMAGE075
is composed of
Figure 848077DEST_PATH_IMAGE076
The companion matrix of (a);
Figure 346054DEST_PATH_IMAGE077
is composed of
Figure 648860DEST_PATH_IMAGE069
A conjugate matrix of (a);
Figure 865078DEST_PATH_IMAGE078
is composed of
Figure 278741DEST_PATH_IMAGE071
The inverse matrix of (c).
(5)
Figure 631225DEST_PATH_IMAGE073
The eigenvalue and eigenvector of
Figure 370511DEST_PATH_IMAGE079
And
Figure 808446DEST_PATH_IMAGE080
and is and
Figure 760221DEST_PATH_IMAGE081
。
(6) The natural frequency and damping ratio of the bridge are calculated by equation (18).
Figure 232791DEST_PATH_IMAGE082
；（18）
In the formula (I), the compound is shown in the specification,
Figure 80661DEST_PATH_IMAGE083
the sampling time of the dynamic displacement time course response of the bridge is represented,
Figure 802630DEST_PATH_IMAGE084
representing the natural frequency of each order of the bridge;
Figure 26937DEST_PATH_IMAGE085
representing the damping ratio of each step of the bridge;
Figure 150751DEST_PATH_IMAGE086
represents the modal order and is a positive integer.
(7) Then, the bridge mode shape can be identified by equation (19).
Figure 435102DEST_PATH_IMAGE087
； （19）
Optionally, identifying the position and the degree of the bridge damage according to the modal shape calculation model includes:
and (3) performing mode normalization processing on each order of modal shape of the bridge, superposing absolute values of the modal shape, calculating average modal shape energy of the bridge when the bridge is not damaged and damaged respectively, calculating average energy difference of each point on the bridge according to the average modal shape energy, and identifying the damage position and the damage degree of the bridge according to the average energy.
Optionally, identifying the bridge damage location and the bridge damage level according to the average energy comprises:
generating a fitting curve according to the average energy;
and determining the mutation position of the fitting curve as the damage position of the bridge, and determining the damage degree of the bridge structure by judging the singular point amplitude of the fitting curve.
In this optional embodiment, the damage position and the damage degree of the bridge are identified by using a modal shape superposition energy algorithm, and first, the modal shapes of each order of the bridge are normalized by the shape normalization, and the absolute values of the modal shapes are superposed, as shown in formula (20).
Figure 378787DEST_PATH_IMAGE088
； （20）
In the formula (I), the compound is shown in the specification,
Figure 141207DEST_PATH_IMAGE089
before the bridge is in a non-damage stateNSuperposition of order mode shapes to avoid amplitude due to singularityThe difference of (2) causes normalization confusion, and when the mode shape normalization is performed on each order mode shape under the bridge damage state, the maximum value of the same order mode shape under the bridge damage state is used, as shown in formula (21).
Figure 119527DEST_PATH_IMAGE090
； （21）
In the formula (I), the compound is shown in the specification,
Figure 574779DEST_PATH_IMAGE091
is the front of a bridge in a damaged stateNStacking order mode vibration modes;
Figure 5761DEST_PATH_IMAGE092
the mode shape of each order under the damage state of the bridge.
And secondly, calculating the average modal shape energy of the bridge without damage and with damage respectively as shown in formulas (22) to (23).
Figure 571871DEST_PATH_IMAGE093
； （22）
Figure 404698DEST_PATH_IMAGE094
；（23）
In the formula (I), the compound is shown in the specification,
Figure 30851DEST_PATH_IMAGE095
when the bridge is not damagednThe average modal shape energy value of a point,
Figure 949129DEST_PATH_IMAGE096
when the bridge is damagednAverage modal shape energy value of the point.
And finally, calculating the average energy difference of each point on the bridge, as shown in the formula (24).
Figure 53351DEST_PATH_IMAGE097
； （24）
By observing
Figure 209526DEST_PATH_IMAGE098
The position of the sudden change of the fitting curve is the damage position of the bridge. And determining the damage degree of the bridge structure by judging the magnitude of the singular point amplitude. Therefore, the damage position and the damage degree of the bridge can be quickly identified through the fitting curve.
Optionally, after identifying the position and the degree of the bridge damage according to the modal shape calculation model, the method further includes:
and performing safety assessment on the bridge to be monitored according to the bridge damage position and the bridge damage degree, and generating alarm information when the damage position of the bridge is determined or the bridge damage degree exceeds a damage threshold value.
In the optional embodiment, the operation safety of the bridge is evaluated in real time by combining the bridge damage position and the damage degree acquired by the image processing technology and the historical data of the bridge health condition. The micro vibration of the bridge can be monitored in real time based on a broadband phase motion amplification algorithm and a discrete centroid search algorithm, all the bridge operation safety, degradation behaviors and residual service life in the whole area can be evaluated through real-time monitoring data, and data support can be provided for health monitoring, maintenance and daily maintenance of the bridge.
The embodiment of the application further provides a bridge monitoring system based on image recognition, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the method. The bridge monitoring system based on image recognition can realize the embodiments of the method and achieve the same beneficial effects, and the detailed description is omitted here.
Embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the method steps as described above. The readable storage medium can implement the embodiments of the method described above, and can achieve the same beneficial effects, which are not described herein again.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (8)

1. A bridge health monitoring method based on image recognition is characterized by comprising the following steps:
acquiring a first image of a bridge to be monitored, and preprocessing the first image to obtain a second image;
performing spatial domain decomposition on the second image to obtain a corresponding target image sequence;
calculating sub-pixel coordinates of the centroid positions of continuous image frames in the target image sequence, and determining real displacement time course response of the bridge to be monitored according to the sub-pixel coordinates of the centroid positions;
extracting bridge modal parameters from the real displacement time-course response by using a Hankel dynamic modal decomposition method, wherein the bridge modal parameters comprise natural frequency, vibration mode and damping ratio;
and establishing a modal shape calculation model according to the bridge modal parameters, and identifying the damage position and the damage degree of the bridge according to the modal shape calculation model.
2. The bridge health monitoring method based on image recognition of claim 1, wherein the preprocessing the first image to obtain a second image comprises:
and rotating, cutting and scaling the first image to obtain a second image.
3. The bridge health monitoring method based on image recognition according to claim 1, wherein the performing spatial decomposition on the second image to obtain a corresponding target image sequence comprises:
performing spatial domain decomposition on the second image to obtain an initial target image sequence, wherein the initial target image sequence comprises a first residual error part, different-frequency baseband phase information and a second residual error part;
and extracting the texture features of the initial target image sequence by using a two-dimensional Gabor wavelet filter, and performing translation processing on the image information except the texture features in the target image sequence to finish the noise reduction processing on the initial target image sequence to obtain a final target image sequence.
4. The image recognition-based bridge health monitoring method of claim 3, wherein prior to the calculating the centroid position sub-pixel level coordinates of successive image frames in the sequence of target images, the method further comprises:
amplifying the target image sequence based on the different-frequency baseband phase information and a preset amplification factor;
the calculating the sub-pixel-level coordinates of the centroid positions of the continuous image frames in the target image sequence, and determining the real displacement time-course response of the bridge to be monitored according to the sub-pixel-level coordinates of the centroid positions comprises the following steps:
dividing each image frame in the amplified target image sequence into a plurality of grids of cutting areas, calculating the centroid of a discretized object in each grid by using an Otsu threshold segmentation algorithm, calculating the subpixel level coordinates of the centroid positions of all continuous image frames, subtracting the centroid coordinates of a subsequent image frame starting from a 2 nd image frame from the centroid coordinates of a 1 st image frame to obtain the subpixel level displacement time course response of the bridge to be monitored, and converting the subpixel level displacement time course response of the bridge to be monitored into a real displacement time course response according to a scale factor.
5. The bridge health monitoring method based on image recognition according to claim 1, wherein the recognizing a bridge damage position and a bridge damage degree according to the modal shape calculation model comprises:
and (3) performing mode normalization processing on each order of modal shape of the bridge, superposing absolute values of the modal shape, calculating average modal shape energy of the bridge when the bridge is not damaged and damaged respectively, calculating average energy difference of each point on the bridge according to the average modal shape energy, and identifying the damage position and the damage degree of the bridge according to the average energy.
6. The bridge health monitoring method based on image recognition of claim 5, wherein the identifying the bridge damage location and the bridge damage level according to the average energy comprises:
generating a fitting curve according to the average energy;
and determining the mutation position of the fitting curve as the damage position of the bridge, and determining the damage degree of the bridge structure by judging the singular point amplitude of the fitting curve.
7. The bridge health monitoring method based on image recognition according to claim 1, wherein after the bridge damage position and the bridge damage degree are recognized according to the modal shape calculation model, the method further comprises:
and performing safety assessment on the bridge to be monitored according to the bridge damage position and the bridge damage degree, and generating alarm information when the damage position of the bridge is determined or the bridge damage degree exceeds a damage threshold value.
8. A bridge health monitoring system based on image recognition, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method according to any one of the preceding claims 1 to 7.
CN202211118750.XA 2022-09-14 2022-09-14 Bridge health monitoring method and system based on image recognition Pending CN115375924A (en)

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CN115830024A (en) * 2023-02-16 2023-03-21 江苏博宇鑫信息科技股份有限公司 Bridge inhaul cable micro-motion vibration detection method based on image segmentation
CN116295790A (en) * 2023-05-22 2023-06-23 合肥工业大学 Frequency detection method and system based on inter-frame phase difference of bridge inhaul cable characteristic region
CN117237832A (en) * 2023-11-15 2023-12-15 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) Bridge vibration mode identification method, unmanned aerial vehicle and computer readable storage medium
CN117392106A (en) * 2023-11-07 2024-01-12 中交公路长大桥建设国家工程研究中心有限公司 Bridge vibration visual detection method and system based on visual enhancement

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Publication number Priority date Publication date Assignee Title
CN115830024A (en) * 2023-02-16 2023-03-21 江苏博宇鑫信息科技股份有限公司 Bridge inhaul cable micro-motion vibration detection method based on image segmentation
CN115830024B (en) * 2023-02-16 2023-05-02 江苏博宇鑫信息科技股份有限公司 Bridge guy cable micro-motion vibration detection method based on image segmentation
CN116295790A (en) * 2023-05-22 2023-06-23 合肥工业大学 Frequency detection method and system based on inter-frame phase difference of bridge inhaul cable characteristic region
CN116295790B (en) * 2023-05-22 2023-09-05 合肥工业大学 Frequency detection method and system based on inter-frame phase difference of bridge inhaul cable characteristic region
CN117392106A (en) * 2023-11-07 2024-01-12 中交公路长大桥建设国家工程研究中心有限公司 Bridge vibration visual detection method and system based on visual enhancement
CN117237832A (en) * 2023-11-15 2023-12-15 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) Bridge vibration mode identification method, unmanned aerial vehicle and computer readable storage medium
CN117237832B (en) * 2023-11-15 2024-02-09 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) Bridge vibration mode identification method, unmanned aerial vehicle and computer readable storage medium

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