CN116363121A - Computer vision-based inhaul cable force detection method, system and device - Google Patents
Computer vision-based inhaul cable force detection method, system and device Download PDFInfo
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Abstract
The invention discloses a method, a system and a device for detecting inhaul cable force based on computer vision, wherein the method comprises the following steps: acquiring relevant information of a cable to be detected; collecting a tiny vibration video of a cable to be detected, and preprocessing a frame-by-frame image of the video to obtain a target image; acquiring an amplified image sequence from the target image by adopting a Euler motion amplification algorithm based on phase, and reconstructing to obtain an amplified video; determining displacement time-course response data corresponding to the video after the motion amplification by using a sub-pixel template matching algorithm; and acquiring the fundamental frequency corresponding to the cable to be detected based on the displacement time response data, and selecting a proper cable force-frequency relation to calculate the cable force of the cable according to the actual boundary condition of the cable. Therefore, the cable force detection device and the cable force detection method realize the detection of the cable force with very small amplitude of the bridge cable, do not need to additionally arrange a sensor, reduce the cost and have wide application prospect.
Description
Technical Field
The invention relates to the technical field of bridge health monitoring, in particular to a method, a system and a device for detecting inhaul cable force based on computer vision.
Background
The cable-stayed bridge is widely applied to large-span bridges by virtue of the advantages of strong spanning capability, attractive appearance, high cost performance and the like. The cable-stayed bridge is used as a hyperstatic structure system of a main beam bending bearing, a bridge tower bearing and a guy cable tensioning combined stress. The cable of the cable-stayed bridge is easy to be damaged and loosened due to corrosion, fatigue and the like, and is used as an important stress member of the cable-stayed bridge, and larger displacement is generated due to smaller stress and strain change, so that the cable is loosened and stress loss is caused, and the damage of the cable possibly brings disastrous results to the whole structure. Because of the characteristics, the cable force detection of the cable-stayed bridge has important significance in the structural construction and use stages, and the safety and durability evaluation of the bridge structure needs to take the cable force of the cable as an important measurement and evaluation index, so that a basis is provided for the bridge condition evaluation. Therefore, how to quickly and accurately measure and evaluate the cable force of the bridge cable is very important, and is a powerful guarantee for guaranteeing the safe operation of the bridge and realizing the real-time monitoring of the health condition of the bridge.
The most commonly used cable force detection methods at present mainly comprise an oil pressure gauge measurement method, a pressure sensor measurement method, a frequency method, a magnetic flux method, a resistance card measurement method, a sag method, an elongation method and the like. The oil pressure gauge measurement method, the pressure sensor measurement method and the magnetic flux method are not suitable for cable force detection in the operation stage. The frequency method usually adopts a contact sensor such as an acceleration sensor to pick up the vibration signal of the inhaul cable, and the measurement result is reliable, but the method has the defects of difficult instrument installation, higher cost, additional mass introduction and the like. In recent years, with the continuous development of computer vision technology and image acquisition equipment, a structural displacement monitoring method based on computer vision is continuously emerging and verified in practical engineering application. At present, a cable force identification method based on computer vision mainly tracks a structure surface artificial marker or a natural marker through a target tracking algorithm to obtain displacement time-course response data, however, the vibration amplitude of a cable under environmental excitation is tiny, and high-precision cable displacement time-course data is difficult to obtain based on a general moving target tracking algorithm, so that a cable force detection result is influenced; and when the inhaul cable vibration displacement is extracted, the result obtained by adopting the template matching related to the digital image is pixel-level displacement, but in practical application, higher measurement precision is required, so that further optimized search is required, and the sub-pixel level is achieved. Therefore, it is necessary to research how to apply the high-precision and high-speed developed photogrammetry technology and equipment to the traditional bridge engineering field, and a non-contact, convenient, economical and efficient cable force detection method is established by combining the Euler motion amplification algorithm based on the phase with sub-pixel template matching, so that the method has important significance for guaranteeing the safe operation of the bridge and realizing the long-term stable use of the bridge.
Disclosure of Invention
The invention aims to: aiming at the defects existing in the background technology, the invention provides a method, a system and a device for detecting the cable force of a cable based on computer vision, which are used for realizing quick and accurate detection of the cable force of the cable.
The technical scheme is as follows: in order to achieve the aim of the invention, the invention provides a computer vision-based inhaul cable force detection method, a computer vision-based inhaul cable force detection system and a computer vision-based inhaul cable force detection device, which have the following specific scheme:
first aspect: the application discloses a inhaul cable force detection method based on computer vision, which specifically comprises the following steps:
step 1: acquiring relevant information of a cable to be detected;
step 2: collecting a tiny vibration video of a cable to be detected, and preprocessing a frame-by-frame image of the video to obtain a target image;
step 3: acquiring an amplified image sequence from the target image by adopting a Euler motion amplification algorithm based on phase, and reconstructing to obtain an amplified video;
step 4: determining displacement time-course response data corresponding to the video after the motion amplification by using a sub-pixel template matching algorithm;
step 5: acquiring the fundamental frequency corresponding to the cable to be detected based on the displacement time-interval response data, and selecting a proper cable force-frequency relation to calculate the cable force of the cable according to the actual boundary condition of the cable;
further, in the step 1, the related information includes a model, a material, a diameter, a calculated length, a linear density, an inclination angle, a boundary condition, a use time, a number or a position of the cable, and the like.
Further, the specific process of the step 2 is as follows:
step 2.1: selecting 1/4 of the inhaul cable as a detection area, observing whether natural markers exist in the inhaul cable or not, and setting a target point in the inhaul cable if the natural markers do not exist in the inhaul cable; fixing the high-precision industrial camera at a proper position, inspecting the field illumination condition, judging whether light supplementing is needed, and if the light supplementing is needed, turning on a self-powered light supplementing lamp to light; the position of a camera is adjusted, so that a guy cable target can be completely shot by the camera, the guy cable target can not exceed a video range during vibration, the focal length and the aperture of a lens are adjusted, and the guy cable in the field of view of the camera is enabled to be imaged clearly and visually; setting a sampling frequency of a camera, and collecting a guy cable micro-vibration video;
step 2.2: disassembling a guy cable micro-vibration video shot by a camera into continuous frame images, and setting the same region of interest for each frame image; the region of interest comprises a target spot and a inhaul cable movement region to be detected;
step 2.3: and carrying out frame-by-frame image preprocessing on the continuous frame images obtained by disassembly, wherein the preprocessing comprises cutting, rotating and scaling the target image sequence, and removing larger noise in the target image so as to highlight the inhaul cable structure concerned and reduce the influence of other environmental factors.
Further, the specific method of the step 3 is as follows:
step 3.1, spatial domain filtering: carrying out spatial domain filtering on an input target image by using a complex controllable pyramid to obtain a high-pass residual error, a low-pass residual error and image sequences with different scales and different directions, namely a local amplitude spectrum and a local phase spectrum;
step 3.2, time domain filtering: calculating phase difference according to the obtained local phase spectrum, manually setting a frequency range and a filter, and then performing time domain band-pass filtering to extract phase difference signals in the interested frequency band;
step 3.3, amplifying the motion signal: manually setting an amplification coefficient, and linearly amplifying the phase difference of the interesting motion signal extracted by the time filtering to obtain an amplified motion signal;
step 3.4, video reconstruction and synthesis: and reconstructing the high-pass residual error and the low-pass residual error obtained by utilizing an image sequence synthesized by the amplified motion signals and the spatial domain filtering to obtain digital image data after motion amplification, and finally synthesizing an amplified video.
Further, in the step 4, determining displacement time-course response data corresponding to the video after motion amplification by using a subpixel template matching algorithm includes:
and carrying out framing treatment on the amplified video to obtain amplified video images, respectively calculating sub-pixel displacement between the amplified video images of the first frame and the amplified video images of other frames by using a sub-pixel template matching algorithm by using a zero mean value normalization cross-correlation theory in statistics, and then connecting the treatment results of the frames of pictures in series by taking time as a base line to obtain vibration displacement time-course response data of a target object inhaul cable.
Further, in the step 5, the fundamental frequency corresponding to the cable to be detected is obtained based on the displacement time response data, and a suitable cable force-frequency relation is selected to calculate the cable force according to the actual boundary condition of the cable, including:
acquiring the fundamental frequency corresponding to the inhaul cable to be detected in the micro vibration video by utilizing the obtained displacement time response data through fast Fourier transform;
the practical formulas for calculating the cable force by the vibration method are simplified and deduced by various theoretical models, have different application ranges, and need to select proper cable force-frequency relation according to the actual condition of the cable to calculate the cable force of the cable.
Second aspect: the application discloses cable force detecting system of stay cable based on computer vision includes:
the cable related information acquisition module is used for acquiring related information of the cable to be detected;
the image acquisition module is used for acquiring a micro vibration video of the inhaul cable to be detected and acquiring a target image based on the micro vibration video;
the image preprocessing module is used for preprocessing the target image, including cutting, rotating and zooming the target image, and removing larger noise in the target image so as to highlight a cable structure concerned and reduce the influence of other environmental factors;
the video acquisition module after the motion amplification is used for carrying out spatial domain filtering on an input target image by using a complex adjustable pyramid so as to acquire image sequences with different dimensions in different directions, carrying out time domain filtering on the image sequences by manually setting a frequency range to extract phase difference information of a motion signal of interest, amplifying the phase difference by a preset amplification coefficient so as to obtain an amplified image sequence, and then reconstructing the amplified image sequence by using a pyramid inverse process so as to obtain an amplified video;
the displacement time-course response data determining module is used for determining displacement time-course response data corresponding to the video after the motion amplification by utilizing a sub-pixel template matching algorithm;
and the cable force calculation module is used for acquiring the fundamental frequency corresponding to the cable to be detected based on the displacement time-course response data, and selecting a proper cable force-frequency relation to calculate the cable force of the cable according to the actual boundary condition of the cable.
Third aspect: the application discloses cable force detection device of stay cable based on computer vision, a serial communication port includes: the computer system comprises a memory and a processor, wherein the memory is used for storing a computer program executable by the processor, and the processor is used for realizing the steps of the computer vision-based inhaul cable force detection method when the computer program is executed.
Fourth aspect: the application discloses a computer readable storage medium, which is characterized in that the computer readable storage medium is stored with a computer program, and the computer program realizes the steps of the cable force detection method based on computer vision when being executed by a processor.
The beneficial effects are that: compared with the prior art, the invention has the following beneficial effects:
the method, the system and the device for detecting the cable force of the inhaul cable based on computer vision overcome the defects of difficult sensor installation, high equipment cost, low test efficiency and the like of the traditional cable force test frequency method. Acquiring relevant information of a cable to be detected; collecting a tiny vibration video of a cable to be detected, and preprocessing a frame-by-frame image of the video to obtain a target image; acquiring an amplified image sequence from the target image by adopting a Euler motion amplification algorithm based on phase, and reconstructing to obtain an amplified video; determining displacement time-course response data corresponding to the video after the motion amplification by using a sub-pixel template matching algorithm; and acquiring the fundamental frequency corresponding to the cable to be detected based on the displacement time-course response data, selecting a proper cable force-frequency relation to calculate the cable force according to the actual boundary condition of the cable, and providing a new technical approach for cable force testing of the cable-stayed bridge. The cable force detection device has the advantages of low cost, convenience, quickness, easiness in operation, high expansibility, accuracy in cable force detection and the like, and has a wide application prospect.
Drawings
Fig. 1 is a flowchart of a method for detecting a cable force of a cable based on computer vision;
fig. 2 is a schematic diagram of a computer vision-based cable force detection.
Fig. 3 is a schematic structural diagram of a computer vision-based cable force detection system.
Detailed Description
The invention will now be described in further detail with reference to specific examples thereof in connection with the accompanying drawings.
The invention relates to a computer vision-based inhaul cable force detection method, which specifically comprises the following steps with reference to fig. 1:
s1: acquiring relevant information of a cable to be detected;
s2: collecting a tiny vibration video of a cable to be detected, and preprocessing a frame-by-frame image of the video to obtain a target image;
s3: acquiring an amplified image sequence from the target image by adopting a Euler motion amplification algorithm based on phase, and reconstructing to obtain an amplified video;
s4: determining displacement time-course response data corresponding to the video after the motion amplification by using a sub-pixel template matching algorithm;
s5: and acquiring the fundamental frequency corresponding to the cable to be detected based on the displacement time response data, and selecting a proper cable force-frequency relation to calculate the cable force of the cable according to the actual boundary condition of the cable.
Further, in the step S1, the related information includes a model, a material, a diameter, a calculated length, a linear density, an inclination angle, a boundary condition, a use time, a number, a position, or the like of the cable. The obtained relevant information of the inhaul cable is basic information for calculating the cable force later.
Further, the specific process of step S2 is as follows:
s2.1: selecting 1/4 of the inhaul cable as a detection area, observing whether natural markers exist in the inhaul cable or not, and setting a target point in the inhaul cable if the natural markers do not exist in the inhaul cable; fixing the high-precision industrial camera at a proper position, inspecting the field illumination condition, judging whether light supplementing is needed, and if the light supplementing is needed, turning on a self-powered light supplementing lamp to light; the position of a camera is adjusted, so that a guy cable target can be completely shot by the camera, the guy cable target can not exceed a video range during vibration, the focal length and the aperture of a lens are adjusted, and the guy cable in the field of view of the camera is enabled to be imaged clearly and visually; setting a sampling frequency of a camera, and collecting a guy cable micro-vibration video;
s2.2: disassembling a guy cable micro-vibration video shot by a camera into continuous frame images, and setting the same region of interest for each frame image; the region of interest comprises a target spot and a inhaul cable movement region to be detected;
s2.3: the continuous frame images obtained by disassembly are subjected to frame-by-frame image preprocessing, including digital image compression, cutting, rotation and scaling of a target video image sequence, and larger noise in the target image is removed, so that the cable structure concerned is highlighted, and the influence of other environmental factors is reduced.
Specifically, compressing a digital image means that, in order to reduce the calculation amount of a computer processing image, three primary color information is often converted into gray value information according to a certain rule, that is, gray conversion is performed on original image data, so that the calculation amount can be obviously reduced by obtaining gray image data. The calculation formula for converting the three primary color images into gray level images according to different weight coefficients is as follows:
H(x,y)＝0.299R(x,y)+0.587G(x,y)+0.114B(x,y)
further, the specific method of step S3 is as follows:
s3.1, spatial domain filtering: carrying out spatial domain filtering on an input target image by using a complex controllable pyramid to obtain a high-pass residual error, a low-pass residual error and image sequences with different scales and different directions, namely a local amplitude spectrum and a local phase spectrum; the complex steerable pyramid is a multi-resolution image decomposition algorithm with multidirectional, multi-scale and self-conversion capability, has the advantages of directional maneuverability, translational invariance and the like, and the basis function of the complex steerable pyramid is similar to a sine function formed by a Gaussian function envelope. It is noted that the high-pass residual and the low-pass residual output in this step are not subjected to temporal filtering and motion signal amplification processing, but are directly used for pyramid reconstruction of the motion amplified image data.
S3.2, time domain filtering: calculating phase difference according to the obtained local phase spectrum, manually setting a frequency range and selecting a filter, and then performing time domain band-pass filtering to extract phase difference signals in the frequency band of interest;
s3.3, amplifying the motion signal: setting an amplification coefficient alpha manually, and linearly amplifying the phase difference of the interesting motion signal extracted by time filtering to obtain an amplified motion signal;
s3.4, video reconstruction and synthesis: and reconstructing the high-pass residual error and the low-pass residual error obtained by utilizing an image sequence synthesized by the amplified motion signals and the spatial domain filtering to obtain digital image data after motion amplification, and finally synthesizing an amplified video.
In particular, motion amplification is the process of amplifying small motions present in a video sequence or image sequence such that the motion amplitude of the small motions is increased in order to extract valuable information from the small motions. The phase-based euler motion amplification algorithm is proposed based on the fourier shift theorem. In the two-dimensional image, the phase corresponds to the motion of the object, and the image is subjected to fourier transform to perform certain processing on the phase included, thereby realizing the motion amplification of the object. The method directly operates the phase information contained in the image, only translates noise but does not amplify the noise in the aspect of noise processing, reduces the occurrence of motion artifacts, and supports larger amplification factor.
In order to intuitively explain the principle of the algorithm, a one-dimensional image will be described as an example. Assuming that f (x) is a one-dimensional image luminance function, x is the image pixel coordinates, and assuming that the object is translated by δ (t) over the image after time t, the image luminance at time t is f (x+δ (t)). Fourier transforming f (x) and f (x+δ (t)) to obtain
Wherein ω is harmonic frequency, A ω Is the harmonic amplitude.
The phase difference of the harmonic components corresponding to a certain harmonic frequency ω, f (x) and f (x+δ (t)) is
Obviously, this phase difference is directly related to the signal δ (t), containing motion information.
Amplifying the phase by alpha times, adjusting the corresponding harmonic component to reconstruct the brightness function of the image as
By comparing the image luminance functions f (x) and f (x+δ (t)), an amplified motion signal (1+αδ (t)) can be obtained. Notably, delta (t) is actually time-domain filtered in order to amplify the motion signal over a certain frequency range.
Further, in the step 4, determining displacement time-course response data corresponding to the video after motion amplification by using a subpixel template matching algorithm includes:
and carrying out framing treatment on the amplified video to obtain amplified video images, respectively calculating sub-pixel displacement between the amplified video images of the first frame and the amplified video images of other frames by using a sub-pixel template matching algorithm by using a zero mean value normalization cross-correlation theory in statistics, and then connecting the treatment results of the frames of pictures in series by taking time as a base line to obtain vibration displacement time-course response data of a target object inhaul cable. And take a single stay cable as an example, the vibration of the stay cable in space mainly occurs in three directions: a chordal direction in the vertical plane, a vertical chordal direction in the vertical plane, and an out-of-plane direction. In general, the amplitude of vibration of the inhaul cable in the string direction is much smaller than that of the other two directions. Considering that the aerodynamic damping of a bridge stay cable is generally about half of the lateral direction in the vertical chord direction, the main vibration direction of the bridge stay cable is considered to be the vertical chord direction in the vertical plane. Therefore, the invention mainly researches and adopts a computer vision method to test the vibration characteristics of the stay cable in the vertical plane in the vertical string direction.
Specifically, the displacement process of sub-pixel template matching calculation is as follows: assuming that two images f (x, y) and h (x, y) having the same size (mxn) are provided, where h (x, y) has a relative translation with respect to the reference image f (x, y), the cross-correlation relationship between f (x, y) and h (x, y) after fourier transformation can be defined as:
wherein: m and N are the dimensions of the image; (x) 0 ,y 0 ) Is the amount of coordinate shift; ". X" represents complex conjugation; f (u, v) and H * (u, v) each represents f (x, y)And h (x, y).
Expression of F (u, v):
by R hf Is a pixel level displacement of the peak extraction structure vibration. Then, at R hf Sub-pixel level displacement based on time-lapse matrix multiplication discrete fourier transform cross correlation to extract structural vibration is performed in the field around the initial peak of (c).
It is particularly pointed out that only frequency information is needed for estimating the cable force. In other words, no scaling factor needs to be determined to convert the pixel coordinate vibratory displacement to a physical coordinate vibratory displacement, which would make the vision-based measurement process more efficient and practical.
Further, in the step 5, the fundamental frequency corresponding to the cable to be detected is obtained based on the displacement time response data, and a suitable cable force-frequency relation is selected to calculate the cable force according to the actual boundary condition of the cable, including:
acquiring the fundamental frequency corresponding to the inhaul cable to be detected in the micro vibration video by utilizing the obtained displacement time response data through fast Fourier transform; the first-order vibration frequency of the inhaul cable is the fundamental frequency, which is an important dynamic characteristic of the inhaul cable; at the same time, engineers testing the cable force in the field are accustomed to calculating the cable force using the fundamental frequency, so the present invention calculates the cable force by obtaining an accurate fundamental frequency.
The practical formulas for calculating the cable force by the vibration method are simplified and deduced by various theoretical models, have different application ranges, and need to select proper cable force-frequency relation according to the actual condition of the cable to calculate the cable force of the cable. At present, the cable force calculation model based on vibration is mainly divided into four types: tension chord model theory, simple beam model theory, clamped beam theory and complex boundary model theory.
Tension string model theory: the tension string theory simplifies the stay cable to be a tension string without rigidity and sagging, and the bending rigidity is ignored.
F＝4ml 2 f 2
Simply supported beam model theory: the simple support beam model is a horizontal cross beam with a simplified stay cable hinged at one end and a simple support at one end.
Clamped beam model theory: the clamped beam model adopts clamped constraint at two ends, and compared with the simply supported beam model, the clamped beam model has different boundary conditions. Considering the influence of bending stiffness, the calculation formula is as follows:
F＝4ml 2 f 2 (210≤ξ)
considering the influence of sagging, the calculation formula is as follows:
F＝4ml 2 f 2 (λ 2 ≤0.17,4π 2 ≤λ 2 )
complex boundary model theory: in the practical application process, the boundary condition of the stay cable is complex, and the stay cable may be between a simple support and a solid support, so that an elastic boundary exists.
Wherein F is the mass of unit length of m stay cables; l is the length of the stay cable; f is the first order vibration frequency of the inhaul cable, namely the fundamental frequency; EI represents bending stiffness of the cable; EA is the axial rigidity of the inhaul cable; ζ is the dimensionless bending stiffnessThe amount of the product is calculated,
λ 2 is a dimensionless quantity of sagging, lambda 2 ＝[mgl/F]·(EAl/FL e )；L e ＝l[(1+(mglcosθ/F 2 /8))]；
Acquiring relevant information of a cable to be detected; collecting a tiny vibration video of a cable to be detected, and preprocessing a frame-by-frame image of the video to obtain a target image; acquiring an amplified image sequence from the target image by adopting a Euler motion amplification algorithm based on phase, and reconstructing to obtain an amplified video; determining displacement time-course response data corresponding to the video after the motion amplification by using a sub-pixel template matching algorithm; and acquiring the fundamental frequency corresponding to the cable to be detected based on the displacement time-course response data, selecting a proper cable force-frequency relation to calculate the cable force according to the actual boundary condition of the cable, and providing a new technical approach for cable force testing of the cable-stayed bridge.
The invention relates to a inhaul cable force detection system based on computer vision, referring to fig. 2, comprising:
the cable related information acquisition module is used for acquiring related information of the cable to be detected;
the image acquisition module is used for acquiring a micro vibration video of the inhaul cable to be detected and acquiring a target image based on the micro vibration video;
the image preprocessing module is used for preprocessing the target image, including cutting, rotating and zooming the target image, and removing larger noise in the target image so as to highlight a cable structure concerned and reduce the influence of other environmental factors;
the video acquisition module after the motion amplification is used for carrying out spatial domain filtering on an input target image by using a complex adjustable pyramid so as to acquire image sequences with different dimensions in different directions, carrying out time domain filtering on the image sequences by manually setting a frequency range to extract phase difference information of a motion signal of interest, amplifying the phase difference by a preset amplification coefficient so as to obtain an amplified image sequence, and then reconstructing the amplified image sequence by using a pyramid inverse process so as to obtain an amplified video;
the displacement time-course response data determining module is used for determining displacement time-course response data corresponding to the video after the motion amplification by utilizing a sub-pixel template matching algorithm;
and the cable force calculation module is used for acquiring the fundamental frequency corresponding to the cable to be detected based on the displacement time-course response data, and selecting a proper cable force-frequency relation to calculate the cable force of the cable according to the actual boundary condition of the cable.
It should be noted that, for more specific working procedures of the above modules, reference may be made to the corresponding contents disclosed in the foregoing embodiments, and no further description is given here.
The application discloses cable force detection device of stay cable based on computer vision, a serial communication port includes: the computer system comprises a memory and a processor, wherein the memory is used for storing a computer program executable by the processor, and the processor is used for realizing the steps of the computer vision-based inhaul cable force detection method when the computer program is executed.
The application discloses a computer readable storage medium, which is characterized in that the computer readable storage medium is stored with a computer program, and the computer program realizes the steps of the cable force detection method based on computer vision when being executed by a processor.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the invention as claimed and their equivalents, the present invention is intended to include such modifications and variations as well.
Claims (9)
1. The computer vision-based inhaul cable force detection method is characterized by comprising the following steps of:
step 1: acquiring relevant information of a cable to be detected;
step 2: collecting a tiny vibration video of a cable to be detected, and preprocessing a frame-by-frame image of the video to obtain a target image;
step 3: acquiring an amplified image sequence from the target image by adopting a Euler motion amplification algorithm based on phase, and reconstructing to obtain an amplified video;
step 4: determining displacement time-course response data corresponding to the video after the motion amplification by using a sub-pixel template matching algorithm;
step 5: and acquiring the fundamental frequency corresponding to the cable to be detected based on the displacement time response data, and selecting a proper cable force-frequency relation to calculate the cable force of the cable according to the actual boundary condition of the cable.
2. The computer vision-based inhaul cable force detection method according to claim 1, wherein the method comprises the steps of: in the step 1, the related information includes a model, a material, a diameter, a calculated length, a linear density, an inclination angle, a boundary condition, a service time, a number or a position of the inhaul cable, and the like.
3. The computer vision-based inhaul cable force detection method according to claim 1, wherein the method comprises the steps of: the specific process of the step 2 is as follows:
step 2.1: selecting 1/4 of the inhaul cable as a detection area, observing whether natural markers exist in the inhaul cable or not, and setting a target point in the inhaul cable if the natural markers do not exist in the inhaul cable; fixing the high-precision industrial camera at a proper position, inspecting the field illumination condition, judging whether light supplementing is needed, and if the light supplementing is needed, turning on a self-powered light supplementing lamp to light; the position of a camera is adjusted, so that a guy cable target can be completely shot by the camera, the guy cable target can not exceed a video range during vibration, the focal length and the aperture of a lens are adjusted, and the guy cable in the field of view of the camera is enabled to be imaged clearly and visually; setting a sampling frequency of a camera, and collecting a guy cable micro-vibration video;
step 2.2: disassembling a guy cable micro-vibration video shot by a camera into continuous frame images, and setting the same region of interest for each frame image; the region of interest comprises a target spot and a inhaul cable movement region to be detected;
step 2.3: and carrying out frame-by-frame image preprocessing on the continuous frame images obtained by disassembly, wherein the preprocessing comprises cutting, rotating and scaling the target image sequence, and removing larger noise in the target image so as to highlight the inhaul cable structure concerned and reduce the influence of other environmental factors.
4. The computer vision-based inhaul cable force detection method according to claim 1, wherein the method comprises the steps of: the specific method of the step 3 is as follows:
step 3.1, spatial domain filtering: carrying out spatial domain filtering on an input target image by using a complex controllable pyramid to obtain a high-pass residual error, a low-pass residual error and image sequences with different scales and different directions, namely a local amplitude spectrum and a local phase spectrum;
step 3.2, time domain filtering: calculating phase difference according to the obtained local phase spectrum, manually setting a frequency range and a filter, and then performing time domain band-pass filtering to extract phase difference signals in the interested frequency band;
step 3.3, amplifying the motion signal: manually setting an amplification coefficient, and linearly amplifying the phase difference of the interesting motion signal extracted by the time filtering to obtain an amplified motion signal;
step 3.4, video reconstruction and synthesis: and reconstructing the high-pass residual error and the low-pass residual error obtained by utilizing the image sequence synthesized by the amplified motion signals and the spatial domain filtering to obtain the image sequence amplified by the motion, and finally synthesizing the amplified video.
5. The computer vision-based inhaul cable force detection method according to claim 1, wherein the method comprises the steps of: in the step 4, a sub-pixel template matching algorithm is used to determine displacement time-course response data corresponding to the video after the motion amplification, including:
and carrying out framing treatment on the amplified video to obtain an amplified image sequence, respectively calculating sub-pixel displacement between the amplified target image of the first frame and the amplified target images of other frames by using a sub-pixel template matching algorithm by using a zero mean value normalization cross-correlation theory in statistics, and then connecting the frame image treatment results in series by taking time as a base line to obtain vibration displacement time-course response data of the inhaul cable of the target object.
6. The computer vision-based inhaul cable force detection method according to claim 1, wherein the method comprises the steps of: in the step 5, the fundamental frequency corresponding to the cable to be detected is obtained based on the displacement time response data, and a proper cable force-frequency relation is selected to calculate the cable force according to the actual boundary condition of the cable, including:
acquiring the fundamental frequency corresponding to the inhaul cable to be detected in the micro vibration video by utilizing the obtained displacement time response data through fast Fourier transform;
the practical formulas for calculating the cable force by the vibration method are simplified and deduced by various theoretical models, have different application ranges, and need to select proper cable force-frequency relation according to the actual condition of the cable to calculate the cable force of the cable.
7. A computer vision-based guy cable force detection system, comprising:
the cable related information acquisition module is used for acquiring related information of the cable to be detected;
the image acquisition module is used for acquiring a micro vibration video of the inhaul cable to be detected and acquiring a target image based on the micro vibration video;
the image preprocessing module is used for preprocessing the target image, including cutting, rotating and zooming the target image, and removing larger noise in the target image so as to highlight a cable structure concerned and reduce the influence of other environmental factors;
the video acquisition module after the motion amplification is used for carrying out spatial domain filtering on an input target image by using a complex adjustable pyramid so as to acquire image sequences with different dimensions in different directions, carrying out time domain filtering on the image sequences by manually setting a frequency range to extract phase difference information of a motion signal of interest, amplifying the phase difference by a preset amplification coefficient so as to obtain an amplified image sequence, and then reconstructing the amplified image sequence by using a pyramid inverse process so as to obtain an amplified video;
the displacement time-course response data determining module is used for determining displacement time-course response data corresponding to the video after the motion amplification by utilizing a sub-pixel template matching algorithm;
and the cable force calculation module is used for acquiring the fundamental frequency corresponding to the cable to be detected based on the displacement time-course response data, and selecting a proper cable force-frequency relation to calculate the cable force of the cable according to the actual boundary condition of the cable.
8. Computer vision-based inhaul cable force detection device is characterized by comprising: a memory for storing a computer program executable by the processor, and a processor for implementing the steps of a computer vision-based cable force detection method according to any one of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the steps of a computer vision-based cable force detection method according to any one of claims 1-6 are implemented.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117115708A (en) * | 2023-08-24 | 2023-11-24 | 中海建筑有限公司 | Foggy environment bridge cable force identification method based on deep learning micro-motion amplification technology |
| 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 |
| CN118230225A (en) * | 2024-05-22 | 2024-06-21 | 中铁大桥局集团有限公司 | Inhaul cable multi-scale vibration visual monitoring method and system |
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2023
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117115708A (en) * | 2023-08-24 | 2023-11-24 | 中海建筑有限公司 | Foggy environment bridge cable force identification method based on deep learning micro-motion amplification technology |
| 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 |
| CN118230225A (en) * | 2024-05-22 | 2024-06-21 | 中铁大桥局集团有限公司 | Inhaul cable multi-scale vibration visual monitoring method and system |
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