CN115375924A - Bridge health monitoring method and system based on image recognition - Google Patents
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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
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.
Drawings
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:
the solid part is as follows:
the dotted portion is shown below;
in the formula,
a wavelength representing a sine function;
a phase offset representing a tuning function;
determining a spatial aspect ratio of the two-dimensional Gabor function;
determining the size of the acceptable area of the kernel of the two-dimensional Gabor filter for the standard deviation of the Gaussian function;
represents the direction of a two-dimensional Gabor wavelet filter kernel, and
；
and
is a spatial position variable; exp represents the exponential calculation;ithe number of the units of the imaginary number is expressed,
。
it should be noted that it is preferable to provide,
and
respectively representing directional information and spatial information of a sequence of video images, which satisfy the following relation:
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
And setting an appropriate amplification factor
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,
a low frequency cutoff frequency for the selected wideband frequency baseband;
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
，
Is composed of
Small vibration displacements occurring in the time domain, by presettingAmplification factor of
Hopefully, the amplified vertical vibration airspace signal of the bridge is
. For broadband phase motion amplification, firstly, a space domain signal of bridge vibration is obtained
Represented as a superposition of a series of complex sinusoidal signals, as shown in equation (5).
In the formula,
is the circular frequency of a certain sub-sinusoidal signal;
is the amplitude of a certain sub-sinusoidal signal;
is a spatial position variable.
For a circular frequency of
Has the following complex sinusoidal signals:
Using a broadband phase motion amplification algorithm pair
Amplification of
Adding after doubling
To obtain the result of formula (8)
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
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).
Or:
in the formula,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;
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
Time scale factorsAs shown in equation (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
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
Is the real displacement of the micro vibration of the bridge,
is the displacement amplitude of the micro vibration of the bridge,
errors are identified for displacements caused by video illumination variation noise. The displacement of the structure without amplification is
The displacement after the amplification treatment of the micro vibration of the bridge is
. For the amplified displacement
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.
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
Forming a Hankel matrix
As shown in formula (13).
In the formula,m、n、pand M are both positive integers, and M is a positive integer,
denotes the firstkIs spotted on
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
The calculation formula satisfies the following relation:
in the formula (I), wherein
Is a matrix
The last column of (a) is,
is that
The first sub-block of the last column of (a).
(2) The Hankel matrix is composed of equation (15).
In the formula,
forward in time for the same Hankel matrix;
and
two adjacent time data matrices are represented.
(3) XThe truncated Singular Value Decomposition (SVD) of (a) is calculated as shown in equation (16).
In the formula,
and
two unitary matrices obtained for singular value decomposition,
is composed of
The companion matrix of (a);
is a diagonal matrix with diagonal elements of
The number of the singular values is,
is a positive integer.
(4) Operator matrix
As shown in equation (17).
In the formula,
is composed of
The companion matrix of (a);
is composed of
A conjugate matrix of (a);
is composed of
The inverse matrix of (c).
(5)
The eigenvalue and eigenvector of
And
and is and
。
(6) The natural frequency and damping ratio of the bridge are calculated by equation (18).
In the formula,
the sampling time of the dynamic displacement time course response of the bridge is represented,
representing the natural frequency of each order of the bridge;
representing the damping ratio of each step of the bridge;
represents the modal order and is a positive integer.
(7) Then, the bridge mode shape can be identified by equation (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).
In the formula,
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).
In the formula,
is the front of a bridge in a damaged stateNStacking order mode vibration modes;
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).
In the formula,
when the bridge is not damagednThe average modal shape energy value of a point,
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).
By observing
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.
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| Publication number | Priority date | Publication date | Assignee | Title |
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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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| 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 |
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