CN113567968A - Underwater target real-time segmentation method based on shallow water multi-beam water depth data and application - Google Patents
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- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
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Abstract
The invention discloses a real-time segmentation method for underwater targets based on shallow water multi-beam water depth data, wherein RANSAC plane segmentation is adopted to remove a terrain background, and Euclidean distance clustering is combined to effectively segment the underwater targets such as stones, sunken ships, wood and the like, filtering processing is required before and after segmentation, and the key for successful segmentation is to reasonably set a threshold value according to the water depth and multi-beam system parameters. The invention realizes the purpose of rapidly detecting underwater obstacles by utilizing the navigation multi-beam. The fifth generation shallow water multi-beam system has the characteristics of high resolution, high precision and the like, can detect centimeter-level underwater targets, and the detection about sonar is basically based on image detection at present. The invention overcomes the defect of manually and visually searching the target, can accurately give the three-dimensional attribute of the target, display and mark the target, and effectively improves the underwater target detection efficiency.
Description
Technical Field
The invention relates to the technical field of underwater topography observation and underwater target detection, in particular to an underwater target real-time segmentation method based on shallow water multi-beam water depth data and application.
Background
With the rapid development of the economy of the Yangtze river basin, the Yangtze river becomes a navigable river with the largest traffic volume and the busiest transportation in the world, and in order to maintain the navigation capacity of the Yangtze river channel, how to rapidly and efficiently detect and clean underwater obstructive objects is one of the guarantees for realizing the maximization of the transportation capacity of the Yangtze river channel. The fifth generation shallow water multi-beam sounding system represented by Kongsberg EM2040, Reson T50P and Sonic2024 generally has the characteristics of wide coverage, high precision, high resolution and the like, can obtain fine underwater topography, and can also achieve the purposes of detection of navigation obstacles, monitoring of underwater buildings, searching of underwater objects, dredging and scanning underwater and the like, and the multi-beam underwater measurement technology becomes a core measurement technology for the construction of a digital channel of a Yangtze river channel.
At present, most of multi-beam-based automatic underwater target detection is carried out by utilizing backscattering images and water body image data during post-processing, the recognition rate is low, and no multi-beam hardware and software is applied to real-time target detection by utilizing water depth point data. The target identification and reconstruction technology based on the three-dimensional laser point cloud is widely applied to ground measurement, a shallow water multi-beam system can also obtain high-density point cloud of an underwater target, the segmented target is generally single, the segmented scene is simple, but the method is only used for qualitative analysis at present, and the target position is mainly found out by a surveyor through visual display software.
The point cloud segmentation is to divide the point cloud according to the characteristics of the space, the geometry, the texture and the like of the point cloud so that the same divided point cloud has similar characteristics, and the current point cloud segmentation methods mainly comprise an edge-based method, a surface-based method, a clustering-based method and a mixed segmentation method, wherein the edge-based segmentation method has the advantages of high speed, strong identification capability on an obvious boundary, but is easily influenced by measurement noise, so the method is not suitable for the multi-beam point cloud segmentation; the segmentation method based on the surface mainly comprises a region growing method and a RANSAC random sampling consistency algorithm, wherein the two algorithms are relatively dependent on a given plane segmentation threshold, the region growing method needs to set two thresholds of a normal angle difference and a curvature, and the RANSAC algorithm needs to set a tolerance distance from an outlier to a fitting plane; the region growing method can segment a plurality of plane features at one time, is good for segmenting regular planes such as buildings in the square, but is difficult to process the uneven ground with more noise points, so that the method is not suitable for multi-beam point cloud segmentation. The surface-based segmentation method has the defects that non-planar features cannot be segmented, and for multi-beam data, planar segmentation can distinguish an underwater flat terrain background from a mixed target, but cannot segment a plurality of targets. The clustering-based method utilizes local geometric characteristic parameters of point cloud, such as gaussian curvature, average curvature, normal vector, coordinate, distance and the like, to perform clustering, and in order to accelerate the search of neighborhood points, a KD-tree is usually adopted for indexing, but the clustering algorithm is sensitive to noise.
Through the above analysis, the problems and defects of the prior art are as follows:
1. at present, the mode of automatically searching for the underwater target by using the multi-beam sea sweeping technology mainly uses two-dimensional image data, and the application of segmenting and detecting the underwater target by using three-dimensional water depth data is not available.
2. Most of the multi-beam water depth data have noise points, which greatly hinders the segmentation of underwater targets.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a real-time segmentation method for an underwater target based on shallow water multi-beam water depth data and application thereof, and can solve the problem that scanning of the underwater target by using multi-beams depends on artificial visual detection.
The technical scheme adopted by the invention for solving the technical problems is as follows:
1. the invention provides a real-time segmentation method of an underwater target based on shallow water multi-beam water depth data and application thereof, comprising the following steps:
s1, receiving 30-50 ping multi-beam original data to a buffer area in real time and carrying out wave spot homing calculation;
s2, adopting a radius filter, and taking 3% -5% of the average water depth as a filtering radius to filter the large-scale noise points of the water depth points in the buffer area;
s3, RANSAC plane segmentation is carried out on the filtered water depth point by using 8% -10% of the average water depth as a plane distance threshold, average depth comparison is carried out on the segmented out-of-office point and in-office point, the concavity and convexity of the target characteristics of the out-of-office point are judged, and the out-of-office point and the in-office point are exchanged if the out-of-office point and the in-office point are sunken;
s4, carrying out Euclidean distance clustering on the divided local outer points to obtain a single clustering target, and removing the clustering target with the clustering point number of the single target being less than 5;
s5, calculating the attributes of the clustering targets such as the volume, the center position and the like, displaying the attributes in a three-dimensional mode, emptying a buffer area and repeating the steps S1-S4.
Further, in step S2, when the radius filter is used for filtering, if the number of remaining beam spots in the radius range is less than 2, the beam spot is regarded as an outlier; if the quality of the acquired multi-beam data is poor, the outlier value limit can be properly relaxed.
Further, in step S3, the plane distance segmentation threshold is set to be 8% to 10% of the average water depth of all filtered water depth points in the buffer area, if the percentage of outliers exceeds 50%, the plane segmentation is performed by using the minimum median variance sampling consistency method, and the water depth points of the background terrain are removed after the plane segmentation is completed.
Further, in step S3, the specific formula for solving the plane parameters by using the RANSAC plane segmentation algorithm is as follows:
in the formula, a, b, and c are parameters of a plane equation ax + by + cz +1 equal to 0, and x, y, and z are coordinates of a water depth point.
Further, in the step S4, the clustering distance threshold is set to 8% to 10% of the average water depth; if the clustering target contains more noise points, the minimum clustering point number needs to be set, generally set to be 5, and the clustering target with the point number lower than the clustering point number is deleted.
Further, in step S4, the euclidean distance clustering is a nearest neighbor clustering algorithm, and calculates the euclidean distance between neighboring points:
dividing a discrete point set into a series of point clusters according to a certain distance scale, giving a clustering distance d (8% -10% of water depth), and if the Euclidean distance between two adjacent points is less than or equal to d, then the two points are grouped into the same class; otherwise, the method is different, and the specific implementation flow is as follows:
establishing KD tree storage point cloud data P, and setting an empty point cluster linked list C and an empty queue Q; for each point piE.g. P, adding PiTo the current queue Q, for each point piE.g. Q: using d as radius, searching point piStore neighborhood points (in accordance with the principle that Euclidean distance is less than or equal to d) in Pi kFor each of
Checking whether the point is processed or not, if not, adding Q, when all the points in the chain table in the Q are processed, adding the Q into the chain table C, and enabling the Q to be empty; when all points in P are processed, it terminates.
2. The invention also provides an application of the underwater target real-time segmentation method based on the shallow water multi-beam water depth data, which is characterized in that the underwater obstructive object detection and cleaning are carried out based on the underwater target real-time segmentation method based on the shallow water multi-beam water depth data of the claim 1.
Compared with the prior art, the underwater target real-time segmentation method based on shallow water multi-beam water depth data and the application thereof have the advantages that,
aiming at high-density multi-beam water depth data, preprocessing such as homing calculation and automatic filtering is carried out, and mixed segmentation method based on RANSAC plane segmentation and Euclidean distance clustering is adopted for the processed multi-beam water depth data, so that real-time rapid segmentation and physical attribute and geographic attribute calibration of an underwater target are realized. The method is realized without user intervention, is processed automatically, and has good target segmentation effect.
The RANSAC plane segmentation is adopted to remove the terrain background, and then the Euclidean distance clustering is combined, so that underwater targets such as stones, sunken ships, wood and the like can be effectively segmented, filtering processing is required before and after segmentation, and the key for successful segmentation is to reasonably set a threshold value according to the water depth and the multi-beam system parameters. The invention realizes the purpose of rapidly detecting underwater obstacles by utilizing the navigation multi-beam. The fifth generation shallow water multi-beam system has the characteristics of high resolution, high precision and the like, can detect centimeter-level underwater targets, and the detection about sonar is basically based on image detection at present.
Drawings
FIG. 1 is a schematic flow chart of a preferred embodiment;
FIG. 2 is a schematic diagram of radius filtering provided by an embodiment of the present invention;
fig. 3 is a schematic diagram of a segmentation result of a sunken ship provided in an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a segmentation result of a plurality of groups of underwater targets according to an embodiment of the present invention;
FIG. 5 is a flow chart of a shallow water multi-beam underwater target detection system provided by an embodiment of the present invention;
fig. 6 is a schematic diagram of a shallow water multi-beam underwater target real-time segmentation system according to an embodiment of the present invention.
Detailed Description
The method for real-time segmentation of underwater targets based on shallow water multi-beam water depth data and the application thereof according to the present invention are described in detail below with reference to fig. 1 to 6.
As shown in the attached figures 1-6, the underwater target real-time segmentation method based on shallow water multi-beam water depth data and the application thereof comprise the following steps:
s1, receiving 30-50 ping multi-beam original data to a buffer area in real time and carrying out wave spot homing calculation;
s2, adopting a radius filter, and taking 3% -5% of the average water depth (10 times of the EM2040 nominal depth measurement precision) as a filtering radius to filter large-scale noise points of the water depth points in the buffer area; for example, the 300kHz short pulse width transmit mode has an accuracy of 3cm at 20m water depth and a filter radius of 0.1 m.
S3, RANSAC plane segmentation is carried out on the filtered water depth point by using 8% -10% of the average water depth as a plane distance threshold, average depth comparison is carried out on the segmented out-of-office point and in-office point, the concavity and convexity of the target characteristics of the out-of-office point are judged, and the out-of-office point and the in-office point are exchanged if the out-of-office point and the in-office point are sunken; the specific process is as follows:
s31: in the local scope, the water bottom can be assumed to be a plane:
ax + by + cz +1 ═ 0 (equation 1)
S32: the core of the segmentation is to determine the coefficients a, b and c of the plane model, and when there are n fitting points, the solving process is expressed as a matrix form as follows:
obtaining by transformation:
s33: determining a planar parametric model by repeated random sampling subsets, testing all other data with the obtained model, if a certain point is suitable for the estimated model, considering it to be a local point, if enough points are classified as the assumed local points, the estimated model is reasonable enough; and finally, recalculating the model through all the local interior points and estimating the error rate of the local interior points and the model to evaluate the model.
S4, carrying out Euclidean distance clustering on the divided local outer points to obtain a single clustering target, and removing the clustering target with the clustering point number of the single target being less than 5;
s41: dividing a discrete point set into a series of point clusters according to a certain distance scale, giving a clustering distance d (8% -10% of water depth), and if the Euclidean distance between two adjacent points is less than or equal to d, then the two points are grouped into the same class; otherwise, the method is different, and the specific implementation flow is as follows:
establishing KD tree storage point cloud data P, and setting an empty point cluster linked list C and an empty queue Q; for each point piE.g. P, add pi to the current queue Q, for each point PiE.g. Q: with d as the radius, the neighborhood point (conforming to the principle that Euclidean distance is less than or equal to d) of the search point pi is stored in Pi kFor each of
Checking whether the point is processed or not, if not, adding Q, when all the points in the chain table in the Q are processed, adding the Q into the chain table C, and enabling the Q to be empty; when all points in P are processed, it terminates.
And S5, calculating the attributes of the volume, the center position and the like of each clustering target, displaying the attributes in a three-dimensional mode, emptying a buffer area, and repeating the steps S1-S4 until all data are segmented.
Table 1 shows the segmentation effect, threshold setting and time-consuming comparison results of different sample targets provided in the embodiment of the present invention
The embodiments disclosed in this description are only an exemplification of the single-sided characteristics of the invention, and the scope of protection of the invention is not limited to these embodiments, and any other functionally equivalent embodiments fall within the scope of protection of the invention. Various other changes and modifications may be made by those skilled in the art based on the above-described technical solutions and concepts, and all such changes and modifications are intended to fall within the scope of the present invention.
Claims (7)
1. A real-time underwater target segmentation method based on shallow water multi-beam water depth data is characterized by comprising the following steps:
s1, receiving 30-50 ping multi-beam original data to a buffer area in real time and carrying out wave spot homing calculation;
s2, adopting a radius filter, and taking 3% -5% of the average water depth as a filtering radius to filter the large-scale noise points of the water depth points in the buffer area;
s3, RANSAC plane segmentation is carried out on the filtered water depth point by using 8% -10% of the average water depth as a plane distance threshold, average depth comparison is carried out on the segmented out-of-office point and in-office point, the concavity and convexity of the target characteristics of the out-of-office point are judged, and the out-of-office point and the in-office point are exchanged if the out-of-office point and the in-office point are sunken;
s4, carrying out Euclidean distance clustering on the divided local outer points to obtain a single clustering target, and removing the clustering target with the clustering point number of the single target being less than 5;
s5, calculating the attributes of the clustering targets such as the volume, the center position and the like, displaying the attributes in a three-dimensional mode, emptying a buffer area and repeating the steps S1-S4.
2. The method according to claim 1, wherein in step S2, when the filtering is performed by using a radius filter, if the number of remaining beam spots within the radius range is less than 2, the beam spot is regarded as an outlier; if the quality of the acquired multi-beam data is poor, the outlier value limit can be properly relaxed.
3. The method according to claim 1, wherein in step S3, the plane distance segmentation threshold is set to 8% to 10% of the average water depth of all filtered water depth points in the buffer, if the percentage of outliers exceeds 50%, the plane segmentation is performed by using a minimum median variance sampling consistency method, and the water depth points of the background terrain are removed after the plane segmentation is completed.
4. The method for real-time segmentation of underwater targets based on shallow water multi-beam water depth data according to claim 1 or 3, wherein in step S3, the RANSAC plane segmentation algorithm is used to solve the specific formula of the plane parameters as follows:
in the formula, a, b, and c are parameters of a plane equation ax + by + cz +1 equal to 0, and x, y, and z are coordinates of a water depth point.
5. The method for real-time segmentation of underwater targets based on shallow water multi-beam water depth data according to claim 1, wherein in step S4, the clustering distance threshold is set to 8% -10% of the average water depth; if the clustering target contains more noise points, the minimum clustering point number needs to be set, generally set to be 5, and the clustering target with the point number lower than the clustering point number is deleted.
6. The method for real-time segmentation of underwater targets based on shallow water multi-beam water depth data according to claim 1 or 5, wherein in step S4, euclidean distance clustering is a nearest neighbor clustering algorithm, which is performed by calculating euclidean distances between neighboring points:
dividing a discrete point set into a series of point clusters according to a certain distance scale, giving a clustering distance d (8% -10% of water depth), and if the Euclidean distance between two adjacent points is less than or equal to d, then the two points are grouped into the same class; otherwise, the method is different, and the specific implementation flow is as follows:
establishing KD tree storage point cloud data P, and setting an empty point cluster linked list C and an empty queue Q; for each point piE.g. P, adding PiTo the current queue Q, for each point piE.g. Q: using d as radius, searching point piStore neighborhood points (in accordance with the principle that Euclidean distance is less than or equal to d) in Pi kFor each of
Checking whether the point is processed or not, if not, adding Q, when all the points in the chain table in the Q are processed, adding the Q into the chain table C, and enabling the Q to be empty; when all points in P are processed, it terminates.
7. The application of the underwater target real-time segmentation method based on the shallow water multi-beam water depth data is characterized in that the underwater obstacle detection and cleaning are carried out based on the underwater target real-time segmentation method based on the shallow water multi-beam water depth data of claim 1.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117538881A (en) * | 2024-01-10 | 2024-02-09 | 海底鹰深海科技股份有限公司 | Sonar water imaging beam forming method, system, equipment and medium |
| CN117538881B (en) * | 2024-01-10 | 2024-05-07 | 海底鹰深海科技股份有限公司 | Sonar water imaging beam forming method, system, equipment and medium |
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