CN115830024B - Bridge guy cable micro-motion vibration detection method based on image segmentation - Google Patents
Bridge guy cable micro-motion vibration detection method based on image segmentation Download PDFInfo
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Abstract
The invention provides a bridge cable micro-motion vibration detection method based on image segmentation, which is a detection method based on machine vision, compared with the traditional sensor method, has the advantages of remarkably reduced cost and higher precision, realizes cable motion signal extraction and analysis through an image segmentation and gray mapping model, extracts cable fundamental frequency by a mathematical method to calculate cable force, can effectively capture real-time change of the cable, can shoot from a farther place, can shoot more cables from a remote visual angle, and simultaneously reduces the requirement of an erecting environment of a camera.
Description
Technical Field
The invention belongs to the technical field of bridge health monitoring, and particularly relates to a bridge guy cable micro-motion vibration detection method based on image segmentation.
Background
With the rapid growth of society and the rapid growth of urban areas and vehicle numbers, urban traffic systems are under increasing tremendous pressure, and bridges are an important component of traffic systems, and once the structure is unstable, are also the most dangerous components of traffic systems. In some cities with developed water systems, municipal bridges are in hub and core positions in municipal transportation, and once problems occur, huge direct losses can be caused, personal threats are more likely to be caused, and huge influences are generated. The bridge type inhaul cable (suspender) is used as one of main members of the bridge, the working state of the inhaul cable (suspender) can directly reflect the working state of the bridge, the inhaul cable force of the inhaul cable is monitored in real time, and the safety risk of the bridge can be effectively reduced.
The traditional inhaul cable force measuring method comprises, but is not limited to, an oil pressure meter method, a magnetic flux method and a vibration frequency method, wherein when the oil pressure meter method is generally used for bridge construction, the magnetic flux method is high in cost and high in installation and maintenance difficulty; the vibration frequency method is divided into two types: contact measurement and non-contact measurement, wherein the contact measurement has time dispersion, and real-time change of a guy cable (a hanger rod) cannot be effectively captured.
The existing non-contact measurement method is mostly based on the machine vision technology, the real-time state of the bridge can be analyzed in real time by erecting a camera and connecting a network, so that the real-time monitoring of the bridge state is achieved, and the existing measurement mode of the inhaul cable (suspender) inhaul cable force based on the machine vision technology is as follows: laying a mark point, capturing the mark point to obtain a time chart, and carrying out signal analysis on the time chart to obtain frequency. The method has the defects that the marking points are greatly influenced by environmental factors, only a single inhaul cable can be calculated, the marking points need to be captured during calculation, and the calculated amount is large.
Disclosure of Invention
The invention aims to provide a bridge guy cable micro-motion vibration detection method based on image segmentation.
In order to solve the technical problems, the invention adopts the following technical scheme: a bridge cable micro-motion vibration detection method based on image segmentation comprises the following specific steps: step S1, acquiring a vibration video of a bridge inhaul cable by using image acquisition equipment; in the step S2 of the method,intercepting a certain frame in a video as a first frame according to requirements, setting a target area on at least one inhaul cable in the first frame, wherein each target area only comprises one inhaul cable and a background, at least one measuring point area is selected in an inner frame of each target area, and the measuring point areas are selected at the edges of the corresponding inhaul cables in a frame mode; s3, carrying out foreground and background segmentation on each target area, extracting a foreground gray value and a background gray value respectively, substituting the foreground gray value and the background gray value into a gray mapping model, and realizing the positioning of the inhaul cable edge in the measuring point area in the corresponding target area; step S4, executing the step S3 on the video frame by frame according to the measuring point area and the target area selected by the first frame, and recording the inhaul cable edge positioning result of each frame measuring point area as a motion signal I n （t/Fs）,I n （t/Fs）=[i n (1/Fs),i n (2/Fs),…,i n (Ts/Fs)]T=1, 2, …, ts, where n is the measurement point region number, t is the frame number, ts is the total frame number of the video, fs is the video acquisition frequency, and fourier changes are performed on the motion signals of all the measurement point regions to obtain corresponding frequency-amplitude data; step S5, the fundamental frequency of the corresponding inhaul cable is obtained through calculation of the frequency-amplitude data, and according to the relation T=4ρL existing between the inhaul cable force and the self-vibration frequency of the corresponding inhaul cable 2 h 1 2 To obtain the cable force, wherein T is the cable force, ρ is the cable linear density, L is the calculated cable length, h 1 The fundamental frequency of the inhaul cable is the first-order frequency, the proportional relation between the measured inhaul cable force T and the designed inhaul cable force value B of the inhaul cable is set to be w, namely w=T/B, and the state of the inhaul cable is judged according to the w value.
Preferably, in step S3, the foreground-background segmentation is to use edge detection to make an edge mask and multiply the image to obtain gray distribution at the edge, and calculate a threshold segmentation image based on the gray distribution.
Preferably, in step S3, the gray mapping model refers to a mapping relationship between a position of an edge of the cable in the image and an average gray value in the region, and the coverage area of the foreground is obtained by combining the foreground brightness and the background brightness obtained by dividing the target region and the average brightness in the target region, and the positioning of the cable is realized by using a geometric relationship between the coverage area and the edge position.
Preferably, in step S3, the average gray value in the target area is I, and the foreground gray value is I 1 The foreground coverage area is S 1 The background gray value is i 0 The background coverage area is S 0 S can be obtained according to the proportional relation when the total area is S 1 =S·（I-i 0 ）/(i 1 -i 0 ) The foreground coverage area S 1 When the foreground is known as a rectangle, the edge position is positioned through the angle of the edge, the included angle between the edge and the area is set as alpha, when alpha=0, the shape of the coverage area is a rectangle, and the formula of the guy cable position d is d=s 1 Width, wherein width represents the width of the coverage area; when α is not equal to 0, the mapping relation between the coverage area and the cable position is divided into three types of triangle, trapezoid and pentagon according to the difference of the coverage shape, and if stanα/2=k, then S 1 The mapping relation with d is as follows:
preferably, in step S4, fourier transformation is performed on the motion signal of the nth measurement point region to obtain the amplitude a of the nth measurement point region n (f p )，A n (f p )=[α n (f 1 ),α n (f 2 ),…,α n (f p )]P=1, 2, …, P, where P represents the number of points of the FFT, f p Representing frequency components of the fourier transform, f p =Fs*p/2*P，α n Represents a frequency-dependent variable, alpha n (f 1 ),α n (f 2 ),…,α n (f p ) Fourier signal a representing the motion signal of the n-numbered measurement point regions, respectively n Frequency f of (f) 1 、f 2 、…、f p Corresponding amplitude, frequency-amplitude data is obtained.
Preferably, in step S5, calculating the fundamental frequency of the corresponding cable according to the frequency-amplitude data specifically includes: step S51, selecting the frequency corresponding to the maximum value from the frequency amplitude data segment of each measuring point area, wherein the specific formula is that
f n1 =Max{α n (f a1 ),α n (f a1+1 ),…,α n (f b1 )},0.5≤f a1 ∩f b1 ≤0.5+d，
f n2 =Max{α n (f a2 ),α n (f a2+1 ),…,α n (f b2 )},f n1 ＜f a2 ∩f b2 ≤f n1 +d，
…,
f nq =Max{α n (f aq ),α n (f aq+1 ),…,α n (f bq )},f nq-1 ＜f aq ∩f bq ≤f nq-1 +d, where d is the step size, q is the frequency order, f nq Is the natural frequency of the q-order of the n-number measuring point area, and is in the interval [0.5,0.5+d ]]Fundamental frequency h of inhaul cable in n number measuring point area n1 The q-th order frequency is the interval (f nq-1 ,f nq-1 +d]The frequency corresponding to the peak value in (a); step S52, differentiating the q-order natural frequency of the measuring point region n, wherein the differential formula is% n (e)={(f n2 -f n1 ),(f n3 -f n2 ),…,(f ne+1 -f ne ) E is less than or equal to q-1, wherein e is a positive integer; step S53, the average value of the difference is approximated to the fundamental frequency of the inhaul cable,
the method comprises the steps of carrying out a first treatment on the surface of the Step S54, the fundamental frequency of the cable in the other measuring point area of the same cable is used as a comparison group for matching, and the relative error delta of the fundamental frequencies of the cables in the two measuring point areas is set to be +.>
Wherein c is the guy cable number.
Preferably, when the relative error δ in step S54 is less than or equal to 0.1, the fundamental frequency h of the measuring point region 2c of the cable c 2c Fundamental frequency h as output 1 The method comprises the steps of carrying out a first treatment on the surface of the When delta less than 0.1 is less than or equal to 0.3, taking the average value of two groups of fundamental frequencies of the measuring point area 2c and the measuring point area 2c-1 on the c-type inhaul cable as the output fundamental frequency h 1 I.e. h 1 =(h 2c +h 2c-1 ) 2; when delta>0.3, the measurement on the cable of the cable C is calculatedDifferential value v of two sets of fundamental frequencies of the point region 2c and the measurement point region 2c-1 2c (e) And 2c-1 (e) And calculate the variance of the two differential values, variance sigma 2 The formula of (2) is
Evaluating the accuracy of the two groups of fundamental frequencies, and selecting the variance sigma 2 The fundamental frequency corresponding to the small difference value is taken as the fundamental frequency h of the output 1 。
Preferably, in step S5, determining the state of the cable according to the w value specifically includes: when w is less than or equal to 0.8, the inhaul cable is seriously damaged, or an abnormal working state suggests a management and maintenance department to check the reasons and maintain; when w is less than or equal to 0.8 and less than or equal to 0.9, the inhaul cable is damaged or the working state is abnormal, and the management and maintenance department is recommended to pay close attention to checking the reasons; when w is less than 0.9 and less than or equal to 1.05, the inhaul cable is in a normal working state; when w is less than or equal to 1.05 and less than or equal to 1.15, the stay rope is high in load, overweight vehicles or bridge congestion can be serious, and the management and maintenance departments are recommended to pay close attention to the stay rope; when the tension cable load is extremely large and the bridge safety is threatened, the management and maintenance department is recommended to determine whether the bridge working state is a serious overweight vehicle or not, and the management and maintenance department is connected with the traffic department for controlling or bad weather for evacuating after recording.
Preferably, the video format of the cable vibration video in step S1 is one of avi, mov, mp.
Preferably, in step S2, the target area and the measurement point area are manually selected and set.
The scope of the present invention is not limited to the specific combination of the above technical features, but also covers other technical features formed by any combination of the above technical features or their equivalents. Such as those described above, and those disclosed in the present application (but not limited to) having similar functions, are replaced with each other.
Due to the application of the technical scheme, compared with the prior art, the invention has the following advantages:
1. compared with the traditional sensor method, the method for detecting the small movement vibration of the bridge guy cable based on image segmentation has the advantages that the cost is remarkably reduced, the precision is higher, the real-time change of the guy cable (suspender) can be effectively captured, shooting can be carried out from a farther place, more guy cables can be shot from a remote visual angle, and meanwhile, the requirement on the erection environment of a camera is reduced; different from most machine vision-based methods, a large number of lines and marking points are not required to be laid, and vibration data of a plurality of inhaul cables are acquired through a small number of cameras;
2. the invention is a non-contact measuring means, which does not hurt the measuring target;
3. according to the invention, the computer is enabled to recognize the frequency spectrum to solve the fundamental frequency, the cable force of each target cable can be directly obtained by the video data, and the cable force state can be monitored in real time by using the 5G technology to transmit video on the premise that the camera position is relatively stable.
Drawings
FIG. 1 is a flow diagram of a bridge cable micro-motion vibration detection method based on image segmentation;
FIG. 2 is a schematic diagram of a cable positioning method;
fig. 3 is a schematic flow chart of step S5 of the method for detecting small movement vibration of a bridge cable based on image segmentation.
Detailed Description
The present invention will be further described with reference to the accompanying drawings, and the following examples are only for more clearly illustrating the technical aspects of the present invention, and are not to be construed as limiting the scope of the present invention.
The bridge guy cable micro-motion vibration detection method based on image segmentation shown in fig. 1 specifically comprises the following steps.
Step S1, a plurality of inhaul cables are shot by using image acquisition equipment to acquire inhaul cable vibration videos with target inhaul cables to be detected, in the embodiment, the inhaul cable vibration videos can be shot by using a civil-grade camera, the video formats of the inhaul cable vibration videos are avi, mov, mp and other common video formats, mark points are not required to be paved on the inhaul cables when the videos are acquired, and the plurality of inhaul cables should be shot at a slightly far position.
According to the Nyquist sampling theorem, the sampling frequency should be greater than twice the highest frequency component of interest, which is the q-order frequency of the cable in this embodiment, taking the cable fundamental frequency h 1 The frame rate of the camera should be greater than 10 xqxh 1 In this example q=5, h is taken 1 ≈1,10×q×h 1 Approximately 50, the usual 60 frames/second is taken as the sampling frequency.
Step S2, a certain frame is cut out from a video as a first frame according to requirements, a target area is set on at least one inhaul cable in the first frame, each target area only comprises one inhaul cable and a background, at least one measuring point area is selected in an inner frame of each target area, the measuring point area is selected at the edge of the corresponding inhaul cable in a frame mode, the middle part of an image is selected, a larger area is selected in the frame mode, the background is a solid background, sky is selected as the background in the embodiment mode, the background which is nearly solid and exists in a shooting environment can be selected, and contrast exists between the background and the inhaul cable; and selecting a smaller measuring point area in the target area at the edge frame of the inhaul cable, wherein the measuring point area is smaller than the target area, the measuring point area comprises the inhaul cable, and the completed inhaul cable vibration process can be captured.
In this embodiment, the selection is performed according to the above selection manner, and in order to ensure the validity of the data, two target areas ROI1 and ROI2 are taken at similar positions on the cable, and two measurement point areas are drawn in each target area.
And S3, carrying out foreground and background segmentation on each target area, extracting a foreground gray value and a background gray value respectively, substituting the foreground gray value and the background gray value into a gray mapping model, and realizing the positioning of the inhaul cable edge in the measuring point area in the corresponding target area.
In this embodiment, a classical edge detection canny operator is used as an auxiliary to realize separation of foreground and background by a dual-threshold segmentation method, so as to meet the model requirement in S3, and the dual-threshold segmentation method is specifically shown in fig. 2: identifying the image edge by using a canny operator, and multiplying the image edge by an image point to obtain gray distribution at the edge; determining a threshold value according to the gray distribution at the edge and the overall gray distribution of the image; dividing the image according to the threshold value, wherein the image is larger than the large threshold value and is used as a foreground, and the image is used as a background; and multiplying the threshold separation mask by the image to obtain foreground and background brightness distribution of the image respectively.
Substituting the front Jing Huidu value and the background gray value into a gray mapping model, combining a segmented image, obtaining foreground brightness and background brightness, combining the average brightness of the target area, obtaining the coverage area of the foreground, and realizing the positioning of the edge of the cable in the area by the positioning of the target cable to be tested through the geometric relationship between the coverage area and the position at the edge, wherein the gray mapping model is the mapping relationship between the position of the edge of the cable in the image and the average gray value in the area.
The foreground gray value and the background gray value in the image are assumed to be uniform, the average gray value in the target area is assumed to be I, and the foreground gray value is assumed to be I 1 The foreground coverage area is S 1 The background gray value is i 0 The background coverage area is S 0 S can be obtained according to the proportional relation when the total area is S 1 =S·（I-i 0 ）/(i 1 -i 0 ) The foreground coverage area S 1 When the foreground is known as a rectangle, the edge position is positioned through the angle of the edge, the included angle between the edge and the area is set as alpha, when alpha=0, the shape of the coverage area is a rectangle, and the formula of the guy cable position d is d=s 1 Width, wherein width represents the width of the coverage area; when α is not equal to 0, the mapping relation between the coverage area and the cable position is divided into three types of triangle, trapezoid and pentagon according to the difference of the coverage shape, and if stanα/2=k, then S 1 The mapping relation with d is as follows:
。
step S4, executing the step S3 on the video frame by frame according to the measuring point area and the target area selected by the first frame, and recording the inhaul cable edge positioning result of each frame measuring point area as a motion signal I n （t/Fs）,I n （t/Fs）=[i n (1/Fs),i n (2/Fs),…,i n (Ts/Fs)]T=1, 2, …, ts, where n is the station area number, t is the frame number, ts is the total frame of the videoFs is the video acquisition frequency, and the motion signals of all measuring point areas are respectively subjected to Fourier change to obtain corresponding frequency-amplitude data, I n (t/Fs) represents a set of one-dimensional signal sets, i n (1/Fs),i n (2/Fs),…,i n Each of (Ts/Fs) represents a single value, represents a detected motion signal of the n-number measuring point region at the time of 1/Fs, 2/Fs, …, ts/Fs, respectively, and performs fourier transform on the n-group motion signals to obtain amplitude data of the n-group motion signals, where the formula is: a is that n (f p )，A n (f p )=[α n (f 1 ),α n (f 2 ),…,α n (f p )]P=1, 2, …, P, where P represents the number of points of the FFT, f p Representing frequency components of the fourier transform, f p =Fs*p/2*P，α n Represents a frequency-dependent variable, alpha n (f 1 ),α n (f 2 ),…,α n (f p ) Fourier signal a representing the motion signal of the n-numbered measurement point regions, respectively n Frequency f of (f) 1 、f 2 、…、f p Corresponding amplitude, frequency-amplitude data is obtained.
S5, carrying out fundamental frequency identification on the amplitude data to obtain fundamental frequency of the inhaul cable, wherein fundamental frequency is taken as a difference, linear distribution is formed, and frequency-amplitude spectrum searching peaks can be analyzed when the fundamental frequency of the inhaul cable is identified; the principle is that the amplitude is maximum when the inhaul cable vibrates at the natural frequency, the representation on the frequency amplitude graph is obvious peak, the mathematical representation is that the corresponding frequency is the natural frequency when the amplitude takes a large value in a certain range, the invention uses the two characteristics of the inhaul cable to calculate, match and screen the fundamental frequency, and the relation T=4ρL is adopted 2 h 1 2 To obtain the cable force, wherein T is the cable force, ρ is the cable linear density, L is the calculated cable length, h 1 Is the fundamental frequency of the inhaul cable and is the first order frequency.
In step S5, performing fundamental frequency identification on the amplitude data to obtain a fundamental frequency of the cable specifically includes: step S51, selecting the frequency corresponding to the maximum value from the amplitude data according to the frequency amplitude data segment of each measuring point area, wherein the specific formula is that
f n1 =Max{α n (f a1 ),α n (f a1+1 ),…,α n (f b1 )},0.5≤f a1 ∩f b1 ≤0.5+d，
f n2 =Max{α n (f a2 ),α n (f a2+1 ),…,α n (f b2 )},f n1 ＜f a2 ∩f b2 ≤f n1 +d，
…,
f nq =Max{α n (f aq ),α n (f aq+1 ),…,α n (f bq )},f nq-1 ＜f aq ∩f bq ≤f nq-1 +d, where d is the step size, q is the frequency order, f nq Is the natural frequency of the q-order of the n-number measuring point area, and is in the interval [0.5,0.5+d ]]Fundamental frequency h of inhaul cable in n number measuring point area n1 The q-th order frequency is the interval (f nq-1 ,f nq-1 +d]The frequency corresponding to the peak value in (a); the q-order natural frequency of the measuring point region n is differentiated, and the differential formula is # n (e)={(f n2 -f n1 ),(f n3 -f n2 ),…,(f ne+1 -f ne ) E is less than or equal to q-1, wherein e is a positive integer; step S53, the average value of the difference is approximated to the fundamental frequency of the inhaul cable,
。
step S54, the fundamental frequency of the cable in the other measuring point area of the same cable is used as a comparison group for matching, and if the relative error delta of the fundamental frequencies of the cables in the two measuring point areas is set
Wherein c is the guy cable number.
When the relative error delta in the step S54 is less than or equal to 0.1, the fundamental frequency h of the measuring point area 2c of the cable of the number c 2c Fundamental frequency h as output 1 The method comprises the steps of carrying out a first treatment on the surface of the When delta is more than 0.1 and less than or equal to 0.3, taking the average value of two groups of fundamental frequencies of the measuring point area 2c and the measuring point area 2c-1 on the cable of the number c as the output fundamental frequency h 1 I.e. h 1 =(h 2c +h 2c-1 ) 2; when delta is larger than 0.3, the differential value V of two groups of fundamental frequencies of the measuring point area 2c and the measuring point area 2c-1 on the cable of the number c is calculated 2c (e) And 2c-1 (e) And calculate the variance of the two differential values, variance sigma 2 The formula of (2) is
Evaluating the accuracy of the two groups of fundamental frequencies, and selecting the variance sigma 2 The fundamental frequency corresponding to the small difference value is taken as the fundamental frequency h of the output 1 。
In this embodiment, taking the analysis process of the cable No. 1, i.e. the ROI1 region as an example, with reference to fig. 3, the specific process in this embodiment is as follows: the frequency amplitude data of the amplitude corresponding to the maximum amplitude value is selected in segments, the step length is 1, the maximum order is 5,
f 1 =Max{α 1 (f a1 ),α 1 (f a1+1 ),…,α 1 (f b1 )},0.5≤f a1 ∩f b1 ≤1.5，
f 2 =Max{α 1 (f a2 ),α 1 (f a2+1 ),…,α 1 (f b2 )},f 1 ＜f a2 ∩f b2 ≤f 1 +1，
…,
f 5 =Max{α 1 (f a5 ),α 1 (f a5+1 ),…,α 1 (f b5 )},f 4 ＜f a5 ∩f b5 ≤f 4 +1, first in interval [0.5,0.5+1 ]]Interval finding temporary fundamental frequency f 1 The 5 th order frequency is the interval (f 4 ,f 4 +1]The frequency corresponding to the peak value in (a); the frequencies of each order of each group of signals are differentiated, e is taken to be c, c is a positive integer, and the formula is
▽ 1 (c)={(f 2 -f 1 ),(f 3 -f 2 ),(f 4 -f 3 ),(f 5 -f 4 )},
▽ 2 (c)={(f 2 -f 1 ),(f 3 -f 2 ),(f 4 -f 3 ),(f 5 -f 4 ) Approximating the average value of the difference to the fundamental frequency of the inhaul cable
The method comprises the steps of carrying out a first treatment on the surface of the Matching h 11 ，h 21 Obtain the fundamental frequency of the No. 1 inhaul cable with high reliability, < ->
When delta is less than or equal to 0.1, directly outputting the fundamental frequency h of the No. 1 inhaul cable 21 When delta is more than 0.1 and less than or equal to 0.3, directly taking the average value of two groups of fundamental frequencies of the No. 1 inhaul cable as an output value h 1 =(h 21 +h 11 )/2;
When delta is more than 0.3, two groups of difference values V of the No. 1 inhaul cable are calculated 1 、▽ 2 The variance of (a) estimates the accuracy of the two groups of fundamental frequencies, and the variance sigma is selected 2 The fundamental frequency corresponding to the small differential value is taken as the output value,
，/>
by the relation t=4ρl 2 h 1 2 To obtain the cable force, wherein T is the cable force, ρ is the cable linear density, L is the calculated cable length, h 1 Is the fundamental frequency of the cable.
Judging the working state of the inhaul cable according to the proportional relation w of the measured inhaul cable force T and the bridge design value obtained from the bridge design scheme, and when w < = 0.8, seriously damaging the inhaul cable, or suggesting a management and maintenance department to check the reasons and maintain the inhaul cable in an abnormal working state; when 0.8< w < = 0.9, cable damage or abnormal working state is suggested to the management and maintenance department to pay close attention to view the reasons; when 0.9< w < = 1.05, the inhaul cable is normal in working state; when w is 1.05< = 1.15, the cable load is larger, overweight vehicles or bridge congestion is possibly caused, and the management and maintenance department is recommended to pay close attention to; when the weight of the cable is 1.5< w, the cable load is extremely large, the bridge safety is threatened, the management and maintenance department is recommended to determine whether the bridge working state is a serious overweight vehicle or not, and the traffic department is contacted after recording.
The above embodiments are provided to illustrate the technical concept and features of the present invention and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, and are not intended to limit the scope of the present invention. All equivalent changes or modifications made in accordance with the spirit of the present invention should be construed to be included in the scope of the present invention.
Claims (10)
1. A bridge cable micro-motion vibration detection method based on image segmentation is characterized by comprising the following steps of: the method comprises the following specific steps: step S1, acquiring a vibration video of a bridge inhaul cable by using image acquisition equipment; s2, intercepting a certain frame in a video as a first frame according to requirements, setting a target area on at least one inhaul cable in the first frame, wherein each target area only comprises one inhaul cable and a background, selecting at least one measuring point area in each target area, and selecting the measuring point area at the edge of the corresponding inhaul cable in a frame mode; s3, carrying out foreground and background segmentation on each target area, extracting a foreground gray value and a background gray value respectively, substituting the foreground gray value and the background gray value into a gray mapping model, and realizing the positioning of the inhaul cable edge in the measuring point area in the corresponding target area; step S4, executing the step S3 on the video frame by frame according to the measuring point area and the target area selected by the first frame, and recording the inhaul cable edge positioning result of each frame measuring point area as a motion signal I n （t/Fs）,I n （t/Fs）=[i n (1/Fs),i n (2/Fs),…,i n (Ts/Fs)]T=1, 2, …, ts, where n is the measurement point region number, t is the frame number, ts is the total frame number of the video, fs is the video acquisition frequency, and fourier changes are performed on the motion signals of all the measurement point regions to obtain corresponding frequency-amplitude data; step S5, the fundamental frequency of the corresponding inhaul cable is obtained through calculation of the frequency-amplitude data, and according to the relation T=4ρL existing between the inhaul cable force and the self-vibration frequency of the corresponding inhaul cable 2 h 1 2 To obtain the cable force, wherein T is the cable force, ρ is the cable linear density, L is the calculated cable length, h 1 The fundamental frequency of the inhaul cable is the first-order frequency, the proportional relation between the measured inhaul cable force T and the designed inhaul cable force value B of the inhaul cable is set to be w, namely w=T/B, and the state of the inhaul cable is judged according to the w value.
2. The bridge cable micro-motion vibration detection method based on image segmentation according to claim 1, wherein the method is characterized in that: in step S3, the foreground and background segmentation is to use edge detection to make an edge mask and multiply the image to obtain gray distribution at the edge, and calculate a threshold segmentation image based on the gray distribution.
3. The bridge cable micro-motion vibration detection method based on image segmentation according to claim 1, wherein the method is characterized in that: in step S3, the gray mapping model refers to a mapping relationship between a position of an edge of the cable in the image and an average gray value in the region, and the foreground brightness and the background brightness obtained by dividing the target region are combined with the average brightness in the target region to obtain a coverage area of the foreground, and the positioning of the cable is realized by a geometric relationship between the coverage area and the edge position.
4. The bridge cable micro-motion vibration detection method based on image segmentation according to claim 1, wherein the method is characterized in that: in step S3, the average gray value in the target area is I, and the foreground gray value is I 1 The foreground coverage area is S 1 The background gray value is i 0 The background coverage area is S 0 S can be obtained according to the proportional relation when the total area is S 1 =S·（I-i 0 ）/(i 1 -i 0 ) The foreground coverage area S 1 When the foreground is known as a rectangle, the edge position is positioned through the angle of the edge, the included angle between the edge and the area is set as alpha, when alpha=0, the shape of the coverage area is a rectangle, and the formula of the guy cable position d is d=s 1 Width, wherein width represents the width of the coverage area; when α is not equal to 0, the mapping relation between the coverage area and the cable position is divided into three types of triangle, trapezoid and pentagon according to the difference of the coverage shape, and if stanα/2=k, then S 1 The mapping relation with d is as follows:
5. the bridge cable micro-motion vibration detection method based on image segmentation according to claim 1, wherein the method is characterized in that: in step S4, the motion signal of the nth measuring point area is subjected to Fourier transformation to obtain the amplitude A of the nth measuring point area n (f p )，A n (f p )=[α n (f 1 ),α n (f 2 ),…,α n (f p )]P=1, 2, …, P, where P represents the number of points of the FFT, f p Representing frequency components of the fourier transform, f p =Fs*p/2*P，α n Represents a frequency-dependent variable, alpha n (f 1 ),α n (f 2 ),…,α n (f p ) Fourier signal a representing the motion signal of the n-numbered measurement point regions, respectively n Frequency f of (f) 1 、f 2 、…、f p Corresponding amplitude, frequency-amplitude data is obtained.
6. The bridge cable micro-motion vibration detection method based on image segmentation according to claim 1, wherein the method is characterized in that: in step S5, the calculating the fundamental frequency of the corresponding cable according to the frequency-amplitude data specifically includes: step S51, selecting the frequency corresponding to the maximum value from the frequency amplitude data segment of each measuring point area, wherein the specific formula is that
f n1 =Max{α n (f a1 ),α n (f a1+1 ),…,α n (f b1 )},0.5≤f a1 ∩f b1 ≤0.5+d，
f n2 =Max{α n (f a2 ),α n (f a2+1 ),…,α n (f b2 )},f n1 ＜f a2 ∩f b2 ≤f n1 +d，
…,
f nq =Max{α n (f aq ),α n (f aq+1 ),…,α n (f bq )},f nq-1 ＜f aq ∩f bq ≤f nq-1 +d, where d is the step size, q is the frequency order, f nq Is the n number measuring point areaThe natural frequency of the q-order of the domain is within the interval [0.5,0.5+d ]]Fundamental frequency h of inhaul cable in n number measuring point area n1 The q-th order frequency is the interval (f nq-1 ,f nq-1 +d]The frequency corresponding to the peak value in (a); step S52, differentiating the q-order natural frequency of the measuring point region n, wherein the differential formula is% n (e)={(f n2 -f n1 ),(f n3 -f n2 ),…,(f ne+1 -f ne ) E is less than or equal to q-1, wherein e is a positive integer; step S53, the average value of the difference is approximated to the fundamental frequency of the inhaul cable,
the method comprises the steps of carrying out a first treatment on the surface of the Step S54, the fundamental frequency of the cable in the other measuring point area of the same cable is used as a comparison group for matching, and the relative error delta of the fundamental frequencies of the cables in the two measuring point areas is set to be +.>
Wherein c is the guy cable number.
7. The bridge cable micro-motion vibration detection method based on image segmentation according to claim 6, wherein the method is characterized in that: when the relative error delta in the step S54 is less than or equal to 0.1, the fundamental frequency h of the measuring point area 2c of the cable of the number c 2c Fundamental frequency h as output 1 The method comprises the steps of carrying out a first treatment on the surface of the When delta is more than 0.1 and less than or equal to 0.3, taking the average value of two groups of fundamental frequencies of the measuring point area 2c and the measuring point area 2c-1 on the cable of the number c as the output fundamental frequency h 1 I.e. h 1 =(h 2c +h 2c-1 ) 2; when delta is larger than 0.3, the differential value V of two groups of fundamental frequencies of the measuring point area 2c and the measuring point area 2c-1 on the cable of the number c is calculated 2c (e) And 2c-1 (e) And calculate the variance of the two differential values, variance sigma 2 The formula of (2) is
Evaluating the accuracy of the two groups of fundamental frequencies, and selecting the variance sigma 2 The fundamental frequency corresponding to the small difference value is taken as the fundamental frequency h of the output 1 。
8. The bridge cable micro-motion vibration detection method based on image segmentation according to claim 1, wherein the method is characterized in that: in step S5, determining the state of the cable according to the w value specifically includes: when w is less than or equal to 0.8, the inhaul cable is seriously damaged, or an abnormal working state suggests a management and maintenance department to check the reasons and maintain; when w is more than 0.8 and less than or equal to 0.9, the inhaul cable is damaged or the working state is abnormal, and the management and maintenance department is recommended to pay close attention to view the reasons; when w is more than 0.9 and less than or equal to 1.05, the inhaul cable is in a normal working state; when w is more than 1.05 and less than or equal to 1.15, the inhaul cable load is larger, overweight vehicles or bridge congestion can be serious, and the management and maintenance department is recommended to pay close attention to the inhaul cable; when the weight of the inhaul cable is less than 1.5 and w, the inhaul cable is extremely heavy, the bridge safety is threatened, the management and maintenance department is recommended to determine whether the bridge working state is seriously overweight vehicles or not, and the bridge working state is recorded and then is connected with the traffic department for controlling or is in bad weather and is connected with the traffic department for evacuating.
9. The bridge cable micro-motion vibration detection method based on image segmentation according to claim 1, wherein the method is characterized in that: the video format of the cable vibration video in step S1 is one of avi, mov, mp.
10. The bridge cable micro-motion vibration detection method based on image segmentation according to claim 1, wherein the method is characterized in that: in step S2, the target area and the measurement point area are manually selected and set.
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