CN113848209A - Dam crack detection method based on unmanned aerial vehicle and laser ranging - Google Patents
Dam crack detection method based on unmanned aerial vehicle and laser ranging Download PDFInfo
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Abstract
The invention discloses a dam crack detection method based on unmanned aerial vehicle and laser ranging, which comprises the following steps: preprocessing the image, carrying out image segmentation, identifying cracks and marking sequences; selecting a crack boundary point as a crack measuring point; suspending a measuring rope at the corresponding dam crest at the dam crack; selecting at least three unmanned aerial vehicle sampling points; measuring the distance between each sampling point of the unmanned aerial vehicle and a crack measuring point through laser ranging and recording measurement data; calculating a world coordinate position estimation value of a crack measuring point by adopting a space three-point intersection method; adopting unscented Kalman filtering to obtain the optimal estimation of the crack measuring point as the world coordinate of the crack measuring point; selecting the next crack boundary point as a crack measuring point until the world coordinate of each crack boundary point is solved; and detecting the crack development degree of the dam cracks every period T, and analyzing the development change condition of the dam cracks. The method improves the accuracy of positioning the dam crack coordinates and can master the crack development and extension conditions in time.
Description
Technical Field
The invention relates to the technical field of measurement, in particular to a dam crack detection method based on unmanned aerial vehicles and laser ranging.
Background
Cracks are a very common and important disease of the dam, and the development and extension of the cracks can not only cause structural damage and cracking to influence the integrity of the dam body, but also cause leakage to influence the stability of the dam body, and even derive the disaster of breaking the dam. Therefore, monitoring of cracks is of great significance to dam safety. Due to the space-time randomness of crack initiation, particularly the space randomness, the crack position is difficult to predict, and therefore great difficulty is caused to the arrangement of the contact type crack sensor. Therefore, most dams still adopt a manual visual crack detection mode at present, the detection mode has the problems of large workload, low efficiency, long time consumption, poor real-time performance, low safety and the like, the detection result has strong subjectivity, and different people can draw different conclusions mainly depending on the experience of engineering technicians. In recent years, new technologies such as an optical fiber sensor and a crack detection robot have been developed for detecting cracks in a dam. The optical fiber sensor can only monitor crack occurrence areas, cannot monitor crack opening degree, and has the problems of high installation difficulty, easy aging, easy damage and the like. The crack detection robot is easily limited by a detection route and a crack position, and has the problems of limited exploration force on the crack position, low positioning precision and the like.
For example, chinese patent CN111521619A, published as 2020, 8, 11, discloses a robot for detecting dam cracks based on ROV and a method for using the same, which relates to the field of underwater robots. The detection operation system comprises a mud flushing and sucking head, a pump set and a sewage discharge pipe. The mud flushing and sucking head is respectively connected with the pump set and the discharge pipe, the pump set is also connected with the control system, and the mud flushing and sucking head forms negative pressure through the pump set to suck the sediment on the dam surface and then discharge the sediment through the discharge pipe. And (3) processing the dam surface sludge by using a sludge flushing and sucking tool under the condition of not damaging the visibility of a water body, and providing conditions for crack detection. The method is suitable for preliminary detection of the crack position, cannot accurately position the specific position of the crack, and is not beneficial to timely mastering the crack development and extension condition.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the dam crack detection method based on unmanned aerial vehicle and laser ranging is provided for solving the technical problems of large workload, low efficiency, long time consumption, poor real-time performance, low safety and the like in the existing dam crack detection.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a dam crack detection method based on unmanned aerial vehicle and laser ranging comprises the following steps:
s1: arranging an unmanned aerial vehicle to fly in parallel to the axis of the dam in an aerial survey mode, and shooting images of the surface of the dam by using an onboard camera;
s2: preprocessing an image of the surface of the dam, segmenting the image by adopting a maximum inter-class variance method, separating out cracks, marking serial numbers on the cracks, and selecting the cracks with any serial number as the cracks to be detected;
s3: suspending a measuring rope downwards at the corresponding dam crest of the crack to be measured, enabling measuring rope scales to be arranged around the crack to be measured to serve as reference points, selecting edge points of the crack to serve as crack measuring points according to crack skeleton information, and selecting one of the crack measuring points to perform subsequent laser alignment and calculation;
s4: determining at least three unmanned aerial vehicle sampling points by adopting a spiral track, wherein track parameters are determined according to terrain conditions and unmanned aerial vehicle camera parameters;
s5: measuring the distance between each unmanned aerial vehicle sampling point and the crack measuring point by adopting laser ranging, and recording the measurement data of the crack measuring point and the unmanned aerial vehicle sampling point;
s6: calculating an initial value of a coordinate of a crack measuring point by using distances between three unmanned aerial vehicle sampling points and the crack measuring point and adopting a space three-point intersection method;
s7: performing iterative optimization on an initial value of the coordinates of the crack measuring points by using unscented Kalman filtering to obtain optimal estimation of the coordinates of the crack measuring points as true values of the coordinates of the crack measuring points;
s8: selecting new crack measuring points, and repeating the steps S4 to S7 until the coordinates of all the crack measuring points are obtained;
s9; selecting the new serial number cracks as the cracks to be detected, and repeating the steps from S3 to S8 until the coordinates of all crack measuring points of the cracks with all serial numbers are obtained;
s10: and detecting the crack development degree of the dam cracks every period T, and calculating the development degree of each dam crack in the period by taking the coordinates of the crack measuring points obtained by the previous crack development degree detection as a reference.
The process in step S1 is as follows: a section of dam is selected as a detection dam section, the unmanned aerial vehicle flies in parallel to the axis of the dam, and the image of the surface of the dam is shot in an unmanned aerial vehicle oblique photography mode. The process in the step S2: and (3) performing image enhancement, smooth filtering and maximum inter-class variance threshold segmentation on the crack image, extracting skeleton information of the crack, performing primary detection on the crack and marking a serial number. In step S3, the principle of arranging the fracture measurement points is as follows: since the crack skeleton is identified through the step S2, first, crack trend information is determined from the crack skeleton, then the crack skeleton trend is perpendicular, the crack boundary is used as a measuring point, laser alignment is used, and the arrangement density of the measuring points is arranged according to the actual situation.
Preferably, the process of selecting sampling points of the drone in step S4 includes: the unmanned aerial vehicle adopts spiral ascension mode to encircle crack measuring point and fly above the crack measuring point, selects at least three shooting points as the unmanned aerial vehicle sampling point in flight process, records the GPS position and the photoelectric bin range finding distance of unmanned aerial vehicle in each shooting point as sampling point data. And selecting corresponding flight radius, the uniform ascending speed of the unmanned aerial vehicle, the uniform flying angular speed and the overlapping degree according to actual conditions such as terrain conditions and unmanned aerial vehicle camera parameters to ensure the measurement quality. The whole flight path can be assumed to be a space cylinder, the crack measuring point is located at the center of the bottom surface of the space cylinder, the flight path spirally rises around the side surface of the cylinder, at least three shooting points, namely unmanned aerial vehicle sampling points, are randomly selected according to the flight path, the GPS position of the unmanned aerial vehicle at each shooting point is recorded, and the distance measuring distance of the photoelectric bin is recorded.
Preferably, in step S6, the sampling points of the unmanned aerial vehicle include a sampling point a, a sampling point B, and a sampling point C, and a formula for expressing a distance between the crack measurement point and the sampling point of the unmanned aerial vehicle by using a space three-point intersection method is as follows:
wherein: x is the number ofA、yAAnd zAIs the spatial coordinate, x, of sample point AB、yBAnd zBIs the spatial coordinate, x, of sample point BC、yCAnd zCIs the spatial coordinate, x, of the sample point CR、yRAnd zRAs spatial coordinates of the crack measuring points, dAR、dBRAnd dCRThe distances from the three sampling points to the crack measuring point are respectively. The unmanned aerial vehicle flies spirally around a ground target in the sky along a flight path, three times of laser is continuously shot on a point to be positioned at A, B, C three points of the flight path, three times of relative distance measurement is carried out, and an equation set is constructed.
Preferably, the step S3 of arranging the measuring string near the dam crack includes: placing a measuring rope downwards from the top of the dam above the crack, marking the position of the dam where the measuring rope is hung, and arranging scales on the measuring rope; the heavy hammer is hung at the bottom of the measuring rope, so that the measuring rope cannot be blown along with wind. And the specific scale on the measuring rope is used as a control point in the crack measurement, and subsequent measurement and analysis are carried out according to the scale on the measuring rope.
Preferably, the step S7 includes the following steps:
the step S7 includes the following steps:
s71: the unmanned aerial vehicle carries out sampling ranging on the same crack measuring point for multiple times and records the position of the unmanned aerial vehicle GPS and the distance between the unmanned aerial vehicle sampling point and the crack measuring point;
s72: let the k-th unmanned position be (x)k,yk,zkAnd the position of a crack measuring point is (x)R，yR，zR). Setting a quantity of state
Expressing the k-th estimated value of a crack measuring point, and obtaining a state equation and an observation equation of a discrete system as follows:
Mk+1＝φk+1,kMk+wk
Jk+1＝h(Mk)+vk
wherein phik+1,kIs the state transition matrix of the system, wkIs the noise matrix of the system, vkIs the measurement noise;
s73: and solving the optimal estimation as the specific coordinate of the crack measuring point by using unscented Kalman filtering according to the state equation and the observation equation of the discrete system.
The three-point space intersection ranging positioning method only avoids the introduction of the attitude angle error of the unmanned aerial vehicle, but the ranging error is not compensated. The method can utilize redundant observation quantities on the basis of a three-point ranging space intersection positioning method to observe the relative distance of the same crack measuring point for multiple times to obtain an optimal estimation value, namely, the unmanned aerial vehicle aligns the laser of a crack target in the flight process, measures the relative distance between the crack measuring point and an unmanned aerial vehicle sampling point by adopting laser ranging at different positions of a flight path, and establishes a state equation and an observation equation by taking the distance as the observation quantity and the spatial rectangular coordinate of the crack measuring point as a state quantity. And solving the optimal estimation value of the target position by using unscented Kalman filtering.
Preferably, the measurement data of the crack measuring point and the unmanned aerial vehicle sampling point in step S5 includes a rope measuring scale value near the crack measuring point and the position of the unmanned aerial vehicle in the differential GPS recorded by each corresponding unmanned aerial vehicle sampling point. The measured data is parameters required by the laser range finder for ranging, and is a comparison basis for subsequently judging the extension and development conditions of the crack after a period of time, and meanwhile, flight tracks of the unmanned aerial vehicle are kept consistent all the time, and the positions of sampling points are kept consistent all the time in the comparison of the extension conditions of the crack during measurement, so that the measurement error is reduced, and the measurement and analysis accuracy is improved.
The substantial effects of the invention are as follows: an unmanned aerial vehicle carries a visible light camera to shoot a dam video, the images are subjected to enhancement, filtering and segmentation, and the cracks are identified and marked in sequence through image segmentation; selecting a crack boundary point as a crack measuring point; suspending a measuring rope at the corresponding dam crest at the dam crack; selecting at least three unmanned aerial vehicle sampling points through an unmanned aerial vehicle; measuring the distance between each sampling point of the unmanned aerial vehicle and a crack measuring point through laser ranging, and recording measurement data; calculating a world coordinate position estimation value of a crack measuring point by adopting a space three-point intersection method; adopting unscented Kalman filtering to obtain the optimal estimation of the crack measuring point as the world coordinate of the crack measuring point; selecting the next crack boundary point as a crack measuring point, and repeating the steps S4 to S7 until the world coordinates of each crack boundary point of the dam crack are obtained; and detecting the crack development degree of the dam cracks every period T, and analyzing the development change condition of the dam cracks. The accuracy of positioning the position coordinates of the dam cracks is improved, and the crack development and extension conditions can be mastered in time.
Drawings
FIG. 1 is a flow chart of the main steps of the present embodiment;
Detailed Description
The following provides a more detailed description of the present invention, with reference to the accompanying drawings.
A dam crack detection method based on unmanned aerial vehicle and laser ranging is shown in figure 1 and comprises the following steps:
s1: a section of dam is selected as a detection dam section, the flying height of the unmanned aerial vehicle and the inclination angle of the camera holder are adjusted according to actual conditions, the unmanned aerial vehicle flies along the axis of the parallel dam, and dam face videos are shot through oblique photography.
Step S2 includes the following steps:
s21: a series of images are cut out from the shot video, and the work of image enhancement and smooth filtering are firstly carried out on the crack images. The image enhancement is mainly to carry out graying processing on the image, improve the visual effect of the image and highlight the difference between different object characteristics in the image. In step S1, the crack image may be processed using Gamma. The Gamma enhancement well keeps the geometrical shape of the crack while improving the background brightness of the image, and enhances the contrast ratio of the target image and the background image. The reason for the smooth filtering is mainly that due to partial noise possibly generated in the image acquisition process, the noise may affect the threshold segmentation in the later stage, which is specifically shown in that a large number of black points appear in the threshold segmentation effect graph, which affects the measurement accuracy. In step S1, mixed filtering (including gaussian filtering and median filtering) may be used to denoise the fracture image without affecting the fracture geometry.
S22: and after image preprocessing, segmenting the image by adopting a maximum inter-class variance method. The maximum inter-class variance method divides an image into a background part and a foreground part according to the gray characteristic of the image. Since the variance is a measure of the uniformity of the gray distribution, the larger the inter-class variance between the background and the foreground is, the larger the difference between the two parts constituting the image is, and the smaller the difference between the two parts is when part of the foreground is mistaken for the background or part of the background is mistaken for the foreground. Therefore, the segmentation approach that maximizes the inter-class variance means that the probability of false positives is minimized. The method is adopted to segment the crack.
The method is adopted to identify the crack image, initially extract the crack skeleton information and mark the crack with a serial number.
Step S3 includes the following steps:
s31: selecting a crack boundary point as a crack measuring point; for the principle of the arrangement of the crack measuring points: since the crack skeleton is identified through the step s1, firstly, the crack trend information is determined according to the crack skeleton, then the crack trend is perpendicular to the crack skeleton trend, the crack boundary is used as a measuring point, laser alignment is used, the crack boundary is used as a crack boundary measuring point, and the arrangement density of the measuring point is determined according to the actual situation.
S32: arranging a measuring rope at a dam crack; arranging a measuring rope and selecting a measured control point in the step S3: the measuring rope is placed downwards from the top of the dam above the crack, the position of the dam for hanging the measuring rope is marked, scales are arranged on the measuring rope, and the heavy hammer is hung at the bottom of the measuring rope, so that the measuring rope cannot generate large errors along with wind blowing. And a certain specific scale on the measuring rope is a control point in the crack measurement, and subsequent measurement and analysis can be carried out according to the scale on the measuring rope in the later period.
S4: selecting at least three unmanned aerial vehicle sampling points through an unmanned aerial vehicle; the process that the unmanned aerial vehicle selects the sampling point of the unmanned aerial vehicle in the step S4 includes: the unmanned aerial vehicle flies above the crack measuring points in a spiral ascending mode around the crack measuring points, at least three shooting points are selected as unmanned aerial vehicle sampling points in the flying process, and the GPS position and the ranging distance of the photoelectric bin of the unmanned aerial vehicle in each shooting point are recorded as sampling point data. The flight path of the unmanned aerial vehicle flies spirally above a target point (namely a crack measuring point) according to the actual situation, and the corresponding flight radius, the uniform ascending speed of the unmanned aerial vehicle, the uniform flying angular speed and the overlapping degree are selected according to the actual situations such as the actual terrain situation, the camera parameters of the unmanned aerial vehicle and the like to ensure the measurement quality. The whole flight path can be assumed to be a space cylinder, the crack measuring point is located at the center of the bottom surface of the space cylinder, the flight path spirally rises around the side surface of the cylinder, N shooting points (namely unmanned aerial vehicle sampling points) are randomly selected according to the flight path, the GPS position in each unmanned aerial vehicle shooting point is recorded, and the distance measuring distance of the photoelectric bin is obtained.
S5: measuring the distance between each sampling point of the unmanned aerial vehicle and a crack measuring point through laser ranging, and recording measurement data; the measurement data in the step S5 includes the rope measurement scale values near the crack measurement point and the position of the drone in the differential GPS recorded at each corresponding drone sampling point. The measured data is a parameter required by the laser range finder for ranging, and is a comparison basis for the extension condition of the crack after a period of follow-up judgment, and meanwhile, the sampling point of the unmanned aerial vehicle is kept consistent before and after the extension condition of the crack is measured, so that the measurement error is reduced, and the measurement and analysis accuracy is improved.
S6: calculating a world coordinate position estimation value of a crack measuring point by adopting a space three-point intersection method; in the step S5, the sampling points of the unmanned aerial vehicle include a sampling point a, a sampling point B, and a sampling point C, and a calculation formula for calculating a world coordinate position estimation value of the crack measuring point by using a space three-point intersection method is as follows:
wherein (x)A，yA，zA) Is the spatial coordinate of sample point A, (x)B，yB，zB) Is the spatial coordinate of sample point B, (x)C，yC，zC) Is the spatial coordinate of sample point C, (x)R，yR，zR) As spatial coordinates of the crack measuring points, dAR、dBRAnd dCRThe distances from the three sampling points to the crack measuring point are respectively. The unmanned aerial vehicle spirally flies around a ground target in the sky along a flight track, three times of continuous laser is conducted on the points to be positioned at three points of the flight track A, B and C, three times of relative distance measurement is conducted, an equation set (6-1) is constructed, and the space rectangular coordinate of the crack measuring point can be obtained by solving the ternary equation set.
Can be obtained by finishing
Wherein
Subtracting (6-2) from (6-3) and (6-4) to obtain
2(xB-xA)xR+2(yB-yA)yR+2(zB-zA)zR-n1＝0 (6-5)
2(xC-xA)xR+2(yC-yA)yR+2(zC-zA)zR-n2＝0 (6-6)
Wherein
Combining (6-5) with (6-6), eliminating zTAvailable (6-7) elimination of yTA binary linear equation (6-8) can be obtained:
yT＝jxT+k (6-7)
zT＝pxT+q (6-8)
wherein
Can be found with respect to xTA quadratic equation of one unit of (c). Because the crack measuring point and the A, B, C points on the track are not coplanar, two solutions can be solved, and x can be determined according to the near-sighted elevation of a straight crack measuring pointRA value of (a) xTThe position coordinates (x) of the crack measuring points can be obtained by substituting the formulas (6-7) and (6-8)R，yR，zR)。
S7: adopting unscented Kalman filtering to obtain the optimal estimation of the crack measuring point as the world coordinate of the crack measuring point; step S6 includes the following steps:
s71: the unmanned aerial vehicle carries out sampling ranging on the same crack measuring point for a plurality of times and records the position of the unmanned aerial vehicle GPS and the distance between the unmanned aerial vehicle sampling point and the crack measuring point;
s72: let the k-th unmanned position be (x)k,yk,zkAnd the position of a crack measuring point is (x)R，yR，zR) Setting a quantity of state
Expressing the k-th estimated value of a crack measuring point, and obtaining a state equation and an observation equation of a discrete system as follows:
Mk+1＝φk+1,kMk+wk
Jk+1＝h(Mk)+vk
wherein phik+1,kIs the state transition matrix of the system, wkIs the noise matrix of the system, vkIs the measurement noise;
s73: and solving the optimal estimation point as a specific space coordinate of the crack measuring point by using unscented Kalman filtering according to a state equation and an observation equation of the discrete system. Although the three-point space intersection ranging positioning method avoids the introduction of the attitude angle error of the unmanned aerial vehicle, the ranging error is not compensated, and the three-point space intersection positioning method can not fully utilize multiple observed quantities, so that a method for observing the relative distance of the same crack measuring point for multiple times to obtain the optimal estimated value is provided: the unmanned aerial vehicle tracks the target in the flight process, measures the relative distance between the observed target and the unmanned aerial vehicle at a plurality of observation points, and establishes a state equation and an observation equation by taking the distance as an observed quantity and taking a crack measurement point space rectangular coordinate as a state quantity. And solving the optimal estimation value of the target position by using unscented Kalman filtering. The process is as follows: the unmanned aerial vehicle carries out N times of sampling ranging on the same observation point and records the position of the unmanned aerial vehicle GPS, N measuring distances can be obtained, and the k-th unmanned aerial vehicle position is (x)k，yk，zk) The position of the crack measuring point is (x)R，yR，zR). Setting a quantity of state
The k-th estimate of the crack station is shown.
The state equation and observation equation of the discrete system are:
Mk+1＝φk+1,kMk+wk (7-1)
Jk+1＝h(Mk)+vk (7-2)
wherein phik+1,kIs the state transition matrix of the system, wkIs the noise matrix of the system, vkIs to measure the noise, the system noise andthe measurement noises are not interfered with each other and are all white Gaussian noises with the mean value of zero. The fracture site may be assumed to be approximately stationary when measured.
And (4) converting target positioning into multiple observations to solve the optimal estimation problem by using the state equation and the observation equation of the system established in the step (7-1) and the step (7-2).
The UKF process using unscented Kalman filtering is as follows, using deterministic sampling in conjunction with the system equation: the first step is as follows: and initializing, namely taking 2n +1 sigma points, and symmetrically selecting the sigma points.
The second step is that: with (7-1), for one prediction of sigma point:
χi(k+1|k)＝φk+1,kχi(k|k)。
the third step: calculating a primary predicted value and a prediction covariance of the state:
χi(k+1|k)＝φk+1,kχi(k|k)
wherein
WiIs the corresponding weight value of the sigma point,
the superscripted m represents the mean value,
the superscript c denotes covariance.
The fourth step: according to the system measurement equation: calculating a predicted value of the sigma point:
δi(k+1|k)＝h(χi(k+1|k))。
the fifth step: calculating a measurement predictor and corresponding covariance:
in the formula
And a sixth step: calculating the cross covariance of the observed quantity and the state vector:
the seventh step: calculating a Kalman gain factor:
K(k+1)＝PMJPJJ -1
eighth step: calculating the state update covariance and state update:
P(k+1)＝P(k+1|k)-K(k+1)PJJKT(k+1)
and (5) solving an optimal estimation point, namely a concrete space coordinate of the crack measuring point.
S8: selecting the next crack boundary point as a crack measuring point, and repeating the steps S4 to S7 until the specific world coordinates of each crack boundary point of the dam crack are obtained;
s9: and detecting the cracks of the dam every period T, and analyzing the crack development and extension conditions of the dam within a period of time. After each interval, the same flight path was taken and the same calibrated calibration line was hung at the same location before. Firstly, laser three-point intersection operation is carried out on the same scale position of the marked rope (the rope is vertically hung downwards from the top of the dam, and large displacement cannot be generated along with concrete cracking of the dam). And recording the coordinate information of the same identification point of the marker rope at the moment. Recording the same scale deviation position information of the measuring rope, and performing subsequent difference making to eliminate errors caused by rope carving deviation. After three-dimensional position information with the same scale as the marking rope is determined, measuring the distance of a series of boundary point positions of the crack, calculating the boundary position of the crack by a space three-point intersection method, comparing the boundary position of the crack with the coordinate information of the crack at the previous moment, and solving the development condition of the crack in the period of time;
the present embodiment may include the following steps:
(1) arranging an unmanned aerial vehicle to fly in parallel to the axis of the dam in an aerial survey mode, and shooting a dam surface image by using an onboard visible light camera;
(2) preprocessing the image on the surface of the dam such as image enhancement, smooth filtering and the like, segmenting the image by adopting a maximum inter-class variance method, separating out cracks, and marking serial numbers on the cracks;
(3) selecting a crack with a certain sequence number as a crack to be measured, suspending a measuring rope downwards at the corresponding dam crest, enabling measuring rope scales to be arranged around the crack to be measured as reference points, and selecting edge points as crack measuring points according to known skeleton information of the crack;
(4) selecting a crack measuring point, determining at least three unmanned aerial vehicle sampling points by adopting a spiral track, and determining flight path parameters according to terrain conditions, unmanned aerial vehicle camera parameters and the like;
(5) measuring the distance between each sampling point of the unmanned aerial vehicle and the crack measuring point by adopting laser ranging, and recording information such as measuring rope scales near the crack measuring point, coordinates of the sampling points of the unmanned aerial vehicle and the like;
(6) calculating an initial value of a coordinate of a crack measuring point by using distances between three unmanned aerial vehicle sampling points and the crack measuring point and adopting a space three-point intersection method;
(7) performing iterative optimization on an initial value of a crack measuring point coordinate by using unscented Kalman filtering to obtain an optimal estimation of the crack measuring point coordinate as a true value of the crack measuring point coordinate;
(8) selecting the next crack measuring point, and repeating the steps (4) to (7) until the coordinates of all crack measuring points are obtained;
(9) and detecting the crack development degree of the dam cracks every period T, and calculating the development degree of the cracks in the period by taking the coordinates of the crack measuring points at the previous time as reference.
And (4) detecting cracks of other serial numbers, and repeating the steps (2) to (9).
The embodiment can obtain the position three-dimensional coordinate information of a series of measuring points according to the measurement of the unmanned aerial vehicle. The three-dimensional coordinate information of the crack boundary points obtained by operation can be input in Pro/E, SolidWorks, UG and other software, a corresponding coordinate system is established, and the measuring line scale points are led in to be used as reference point positions to establish a model.
Utilize unmanned aerial vehicle to gather dykes and dams data, can realize detecting dykes and dams crack under remote, the non-contact condition. The space three-point intersection method is a multipoint distance measurement positioning scheme only using distance information between an unmanned aerial vehicle and a ground target to be positioned as observed quantity, and is a method for positioning the target by multipoint space intersection and relative distance measurement. In the embodiment, after the crack is preliminarily identified by the visible light camera, the three-dimensional position of the boundary point of the crack and the three-dimensional position of the scale point of the scale rope are compared with each other by adopting a space three-point intersection method based on laser ranging and unmanned aerial vehicle positioning, so that the displacement condition of the same measuring point position of the crack within the time before and after can be directly solved, and the crack development and extension conditions can be obtained. The method not only improves the detection efficiency of the dam cracks, enlarges the detection area, is not interfered by the detection terrain factors, but also increases the precision of crack coordinate positioning and is convenient for grasping the crack extension and development degree in time.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (6)
1. A dam crack detection method based on unmanned aerial vehicle and laser ranging is characterized by comprising the following steps:
s1: arranging an unmanned aerial vehicle to fly in parallel to the axis of the dam in an aerial survey mode, and shooting images of the surface of the dam by using an onboard camera;
s2: preprocessing an image of the surface of the dam, segmenting the image by adopting a maximum inter-class variance method, separating out cracks, marking serial numbers on the cracks, and selecting the cracks with any serial number as the cracks to be detected;
s3: suspending a measuring rope downwards at the corresponding dam crest of the crack to be measured, enabling measuring rope scales to be arranged around the crack to be measured to serve as reference points, selecting edge points of the crack to serve as crack measuring points according to crack skeleton information, and selecting one of the crack measuring points to perform subsequent laser alignment and calculation;
s4: determining at least three unmanned aerial vehicle sampling points by adopting a spiral track, wherein track parameters are determined according to terrain conditions and unmanned aerial vehicle camera parameters;
s5: measuring the distance between each unmanned aerial vehicle sampling point and the crack measuring point by adopting laser ranging, and recording the measurement data of the crack measuring point and the unmanned aerial vehicle sampling point;
s6: calculating an initial value of a coordinate of a crack measuring point by using distances between three unmanned aerial vehicle sampling points and the crack measuring point and adopting a space three-point intersection method;
s7: performing iterative optimization on an initial value of the coordinates of the crack measuring points by using unscented Kalman filtering to obtain optimal estimation of the coordinates of the crack measuring points as true values of the coordinates of the crack measuring points;
s8: selecting new crack measuring points, and repeating the steps S4 to S7 until the coordinates of all the crack measuring points are obtained;
s9; selecting the new serial number cracks as the cracks to be detected, and repeating the steps from S3 to S8 until the coordinates of all crack measuring points of the cracks with all serial numbers are obtained;
s10: and detecting the crack development degree of the dam cracks every period T, and calculating the development degree of each dam crack in the period by taking the coordinates of the crack measuring points obtained by the previous crack development degree detection as a reference.
2. The dam crack detection method based on unmanned aerial vehicle and laser ranging as claimed in claim 1, wherein the process of selecting the sampling point of the unmanned aerial vehicle in step S4 comprises: the unmanned aerial vehicle adopts spiral ascension mode to encircle crack measuring point and fly above the crack measuring point, selects at least three shooting points as the unmanned aerial vehicle sampling point in flight process, records the GPS position and the photoelectric bin range finding distance of unmanned aerial vehicle in each shooting point as sampling point data.
3. A dam crack detection method based on unmanned aerial vehicle and laser ranging according to claim 1 or 2, characterized in that sampling points of the unmanned aerial vehicle in step S6 include a sampling point a, a sampling point B and a sampling point C, and a formula for expressing a distance between a crack measurement point and the sampling point of the unmanned aerial vehicle by using a space three-point intersection method is as follows:
wherein: x is the number ofA、yA、zAIs the spatial coordinate, x, of sample point AB、yBAnd zBIs the spatial coordinate, x, of sample point BC、yC、zCIs the spatial coordinate, x, of the sample point CR、yRAnd zRAs spatial coordinates of the crack measuring points, dAR、dBRAnd dCRThe distances from the three sampling points to the crack measuring point are respectively.
4. The dam crack detection method based on unmanned aerial vehicle and laser ranging as claimed in claim 1 or 2, wherein the step S3 of arranging the measuring rope near the dam crack comprises: placing a measuring rope downwards from the top of the dam above the crack, marking the position of the dam where the measuring rope is hung, and arranging scales on the measuring rope; the heavy hammer is hung at the bottom of the measuring rope, so that the measuring rope cannot be blown along with wind.
5. The dam crack detection method based on unmanned aerial vehicle and laser ranging as claimed in claim 1, wherein the step S7 comprises the steps of:
s71: the unmanned aerial vehicle carries out sampling ranging on the same crack measuring point for multiple times and records the position of the unmanned aerial vehicle GPS and the distance between the unmanned aerial vehicle sampling point and the crack measuring point;
s72: let the k-th unmanned position be (x)k,yk,zkAnd the position of a crack measuring point is (x)R，yR，zR) Setting a quantity of state
Expressing the k-th estimated value of a crack measuring point, and obtaining a state equation and an observation equation of a discrete system as follows:
Mk+1＝φk+1,kMk+wk
Jk+1＝h(Mk)+vk
wherein phik+1,kIs the state transition matrix of the system, wkIs the noise matrix of the system, vkIs the measurement noise;
s73: and solving the optimal estimation as the specific coordinate of the crack measuring point by using unscented Kalman filtering according to the state equation and the observation equation of the discrete system.
6. A dam crack detection method based on unmanned aerial vehicle and laser ranging as claimed in claim 1, 2 or 5, characterized in that the measured data of the crack measuring point and unmanned aerial vehicle sampling point in step S5 includes the rope-measuring scale value near the crack measuring point and the corresponding position of unmanned aerial vehicle in the differential GPS recorded at each unmanned aerial vehicle sampling point.
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