CN113567968B - Underwater target real-time segmentation method based on shallow water multi-beam water depth data and application thereof - Google Patents
Underwater target real-time segmentation method based on shallow water multi-beam water depth data and application thereof Download PDFInfo
- Publication number
- CN113567968B CN113567968B CN202110569967.1A CN202110569967A CN113567968B CN 113567968 B CN113567968 B CN 113567968B CN 202110569967 A CN202110569967 A CN 202110569967A CN 113567968 B CN113567968 B CN 113567968B
- Authority
- CN
- China
- Prior art keywords
- points
- water depth
- clustering
- point
- segmentation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 81
- 230000011218 segmentation Effects 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000001514 detection method Methods 0.000 claims abstract description 15
- 238000001914 filtration Methods 0.000 claims abstract description 12
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000005070 sampling Methods 0.000 claims description 4
- 238000012876 topography Methods 0.000 claims description 4
- 238000004140 cleaning Methods 0.000 claims description 2
- 230000000007 visual effect Effects 0.000 abstract description 4
- 230000007547 defect Effects 0.000 abstract description 3
- 239000002023 wood Substances 0.000 abstract description 2
- 238000005259 measurement Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010408 sweeping Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 102220335394 rs1554841994 Human genes 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
- G01S7/539—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
- G01S15/93—Sonar systems specially adapted for specific applications for anti-collision purposes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
- G01S7/523—Details of pulse systems
- G01S7/526—Receivers
- G01S7/527—Extracting wanted echo signals
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
- G01S7/534—Details of non-pulse systems
- G01S7/536—Extracting wanted echo signals
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
- G01S7/56—Display arrangements
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
Abstract
The invention discloses a real-time underwater target segmentation method based on shallow water multi-beam water depth data, which adopts RANSAC plane segmentation to remove topographic background and then combines Euclidean distance clustering to effectively segment underwater targets such as stones, sunken ships, wood and the like, filtering treatment is needed before and after segmentation, and reasonable threshold value setting according to water depth and multi-beam system parameters is the key of successful segmentation. The invention realizes the purpose of rapidly detecting the underwater obstacle 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, is basically based on image detection in the current detection about sonar, is directly based on water depth point data for detection and three-dimensional reconstruction, and is more visual in display than two-dimensional images. The invention overcomes the defect of manually looking for the target by visual observation, can accurately give out the three-dimensional attribute of the target, display and mark the target, and effectively improves the efficiency of underwater target detection.
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 thereof.
Background
Along with the rapid development of the river basin economy, the Yangtze river becomes the navigable river with the largest traffic volume and the busiest transportation in the world, and in order to maintain the navigability of the Yangtze river channel, how to quickly and efficiently detect and clear underwater navigation-obstacle is one of the guarantees for realizing the maximization of the navigability of the Yangtze river channel. The fifth generation shallow water multi-beam sounding system represented by Kongsberg EM2040 and Reson T50P, sonic2024 generally has the characteristics of wide coverage, high precision, high resolution and the like, not only can obtain fine underwater topography, but also can realize the purposes of navigation obstacle detection, underwater building monitoring, underwater object searching, underwater dredging and sweeping and the like, and the multi-beam underwater measurement technology has become a core measurement technology for the construction of a Yangtze river channel digital channel.
At present, the automatic detection of the underwater target based on multiple beams is mostly carried out by utilizing the back scattering image and the water body image data during the post-processing, the recognition rate is low, and no application of the multiple beam hardware and software for carrying out the real-time target detection by utilizing the water depth point data exists. Target recognition and reconstruction technology based on three-dimensional laser point cloud is widely applied to ground measurement, but a shallow water multi-beam system can also obtain high-density point cloud of an underwater target, the segmented target is usually single, the segmented scene is simple, but the method is only used for qualitative analysis at present, and a measurer can visually find out the target position by utilizing display software.
The point cloud segmentation is to divide the point cloud according to the characteristics of the point cloud, such as space, geometry, texture and the like, so that the same divided point cloud has similar characteristics, and the current point cloud segmentation method mainly comprises an edge-based method, a surface-based method, a clustering-based method and a hybrid segmentation method, wherein the edge-based segmentation method has the advantages of high speed, strong obvious boundary recognition capability, and is easily influenced by measurement noise, so that the method is not suitable for multi-beam point cloud segmentation; the surface-based segmentation method 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 value, the region growing method needs to set a normal angle difference threshold value and a curvature threshold value, and the RANSAC algorithm needs to set a tolerance distance from an outlier to a fitting plane; the regional growth method can divide a plurality of plane characteristics at one time, is better for dividing regular planes such as four-square buildings, but is difficult to treat the ground with more noise and uneven surface, so that the regional growth method is not suitable for multi-beam point cloud division. The face-based segmentation method has the disadvantage that non-planar features cannot be segmented, and for multi-beam data planar segmentation can distinguish between underwater flat terrain background and hybrid objects, but cannot segment multiple objects. Clustering-based methods utilize local geometric feature parameters of the point cloud, such as gaussian curvature, average curvature, normal vector, coordinates, distance, etc., to accelerate the search of neighbor points, usually index with KD-tree, but clustering algorithms are sensitive to noise.
Through the above analysis, the problems and defects existing in the prior art are as follows:
1. at present, a mode of automatically searching an underwater target by using a multi-beam sea sweeping technology mainly utilizes two-dimensional image data, and has not been applied to underwater target segmentation and detection by utilizing three-dimensional deep water point data.
2. The multi-beam water depth data mostly has noise points, and the segmentation of the underwater targets is greatly hindered.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art, provides a real-time underwater target segmentation method based on shallow water multi-beam water depth data and application thereof, and can solve the problem that the underwater target sweep test by utilizing multiple beams depends on manual visual detection.
The technical scheme adopted for solving the technical problems is as follows:
1. the invention provides a real-time underwater target segmentation method 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 beam spot homing calculation;
s2, adopting a radius filter, and carrying out large-scale noise point filtering on water depth points in the buffer zone by taking 3% -5% of the average water depth as a filtering radius;
s3, carrying out RANSAC plane segmentation on the filtered water depth points by taking 8% -10% of the average water depth as a plane distance threshold value, carrying out average depth comparison on the segmented external points and the segmented internal points, judging the concave-convex property of the target feature of the external points, and if the external points are concave, exchanging the external points and the internal points;
s4, performing Euclidean distance clustering on the segmented outliers to obtain single clustering targets, and eliminating the clustering targets with the single target clustering points less than 5;
s5, calculating the volume, the center position and other attributes of each clustering target, displaying in a three-dimensional mode, emptying the buffer area, and repeating the steps S1-S4.
Further, in the step S2, when filtering is performed by using a radius filter, if the number of the remaining beam points in the radius range is less than 2, the point is regarded as an outlier; if the quality of the collected multi-beam data is poor, the outlier value limit can be properly relaxed.
Further, in the step S3, the plane distance segmentation threshold is set to 8% -10% of the average water depth of all the filtered water depth points in the buffer area, if the local outer point accounts for more than 50%, the plane segmentation is performed by adopting a minimum median variance sampling consistency method, and after the plane segmentation is completed, the water depth points of the background topography are removed.
Further, in the step S3, a specific formula for calculating the plane parameter by using the RANSAC plane segmentation algorithm is as follows:
where a, b, and c are parameters of plane equation ax+by+cz+1=0, and x, y, and z are coordinates of water depth points.
Further, in the step S4, the clustering distance threshold is set to 8% -10% of the average water depth; if the clustering targets contain more noise points, the minimum clustering point number needs to be set, and the minimum clustering point number is generally set to be 5, and the clustering targets lower than the clustering point number are deleted.
Further, in the step S4, the euclidean distance clustering is a nearest neighbor clustering algorithm, and the euclidean distances between the adjacent points are calculated:
dividing a discrete point set into a series of point clusters according to a certain distance scale, and giving a clustering distance d (8% -10% of water depth), wherein if the Euclidean distance between two adjacent points is less than or equal to d, the two points are clustered 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 p i E, P is added with P i To the current queue Q, for each point p i E, Q: taking d as radius, searching point p i Is stored in the neighborhood point (according to the principle that the Euclidean distance is less than or equal to d)For each->Checking whether the point is processed or not, if not, adding Q, and when all the points in Q in the linked list are processed, adding Q into the linked list C, and emptying Q; when all points in P are processed, it terminates.
2. The invention further 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 target real-time segmentation method based on the shallow water multi-beam water depth data is used for detecting and cleaning underwater navigation-impaired objects.
Compared with the prior art, the underwater target real-time segmentation method and the application based on the shallow water multi-beam water depth data have the advantages that,
the invention aims at the high-density multi-beam deep water point data, adopts a mixed segmentation method based on RANSAC plane segmentation and Euclidean distance clustering to the processed multi-beam deep water point data through preprocessing such as homing calculation, automatic filtering and the like, and realizes real-time rapid segmentation and physical attribute and geographic attribute calibration of the underwater target. The method is realized without user intervention, and has the advantages of full-automatic treatment and good target segmentation effect.
The RANSAC plane segmentation is adopted to remove the topographic 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 treatment is needed before and after segmentation, and the reasonable setting of a threshold value according to the parameters of the water depth combined multi-beam system is the key of successful segmentation. The invention realizes the purpose of rapidly detecting the underwater obstacle 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, is basically based on image detection in the current detection about sonar, is directly based on water depth point data for detection and three-dimensional reconstruction, and is more visual in display than two-dimensional images.
Drawings
FIG. 1 is a flow chart of a preferred embodiment;
FIG. 2 is a schematic diagram of radius filtering according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a division result of a sunken ship according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a multi-set underwater target segmentation result provided by an embodiment of the present invention;
fig. 5 is a flowchart of a shallow water multi-beam underwater target detection system according to 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 following describes in detail a method for real-time segmentation of underwater targets based on shallow water multi-beam water depth data and application thereof with reference to fig. 1 to 6.
As shown in fig. 1-6, the method for real-time segmentation of underwater targets based on shallow water multi-beam water depth data and application of the method provided by the invention comprise the following steps:
s1, receiving 30-50 ping multi-beam original data to a buffer area in real time and carrying out beam spot homing calculation;
s2, adopting a radius filter, and carrying out large-scale noise filtering on water depth points in a buffer zone by taking 3% -5% of the average water depth (10 times of the nominal depth measurement precision of EM 2040) as a filtering radius; for example, the 300kHz short pulse width emission mode is 3cm in water depth precision of 20m, and the filter radius is set to be 0.1m.
S3, carrying out RANSAC plane segmentation on the filtered water depth points by taking 8% -10% of the average water depth as a plane distance threshold value, carrying out average depth comparison on the segmented external points and the segmented internal points, judging the concave-convex property of the target feature of the external points, and if the external points are concave, exchanging the external points and the internal points; the specific process is as follows:
s31: in the local area, it can be assumed that the water bottom is a plane:
ax+by+cz+1=0 (formula 1)
S32: the core of the segmentation is to determine the coefficients a, b and c of the plane model, and when n fitting points exist, the solving process is expressed in the form of a matrix as follows:
the method comprises the following steps of:
s33: determining a planar parametric model by repeated random sampling subsets, testing all other data by using the obtained model, if a certain point is suitable for the estimated model, then considering that the certain point is also an in-local point, and if enough points are classified as the assumed in-local point, then the estimated model is reasonable enough; finally, the model is recalculated through all the local points and the error rate of the local points and the model is estimated to evaluate the model.
S4, performing Euclidean distance clustering on the segmented outliers to obtain single clustering targets, and eliminating the clustering targets with the single target clustering points less than 5;
s41: dividing a discrete point set into a series of point clusters according to a certain distance scale, and giving a clustering distance d (8% -10% of water depth), wherein if the Euclidean distance between two adjacent points is less than or equal to d, the two points are clustered 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 p i E P, add pi to current queue Q, P for each point i E, Q: taking d as radius, storing the neighborhood point (according with the principle that Euclidean distance is less than or equal to d) of the search point piFor each->Checking whether the point is processed or not, if not, adding Q, and when all the points in Q in the linked list are processed, adding Q into the linked list C, and emptying Q; when all points in P are processed, it terminates.
S5, calculating the volume, the central position and other attributes of each clustering target, displaying in a three-dimensional mode, emptying the 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 for different sample targets provided by the embodiment of the invention
The embodiment disclosed in the present specification is merely an illustration of one-sided features of the present invention, and the protection scope of the present invention is not limited to this embodiment, and any other functionally equivalent embodiment falls within the protection scope of the present invention. Various other corresponding changes and modifications will occur to those skilled in the art from the foregoing description and the accompanying drawings, and all such changes and modifications are intended to be included within the scope of the present invention as defined in the appended claims.
Claims (7)
1. An underwater target real-time 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 beam spot homing calculation;
s2, adopting a radius filter, and carrying out large-scale noise point filtering on water depth points in the buffer zone by taking 3% -5% of the average water depth as a filtering radius;
s3, carrying out RANSAC plane segmentation on the filtered water depth points by taking 8% -10% of the average water depth as a plane distance threshold value, carrying out average depth comparison on the segmented external points and the segmented internal points, judging the concave-convex property of the target feature of the external points, and if the external points are concave, exchanging the external points and the internal points;
s4, performing Euclidean distance clustering on the segmented outliers to obtain single clustering targets, and eliminating the clustering targets with the single target clustering points less than 5;
and S5, calculating the volume and the central position attribute of each clustering target, displaying in a three-dimensional mode, emptying the buffer area, and repeating the steps S1 to S4.
2. The method for real-time segmentation of underwater targets based on shallow water multi-beam water depth data according to claim 1, wherein in the step S2, when filtering is performed by adopting a radius filter, if the number of remaining beam points in the radius range is less than 2, the point is regarded as an outlier; if the quality of the collected multi-beam data is poor, the outlier value limit can be properly relaxed.
3. The method for real-time segmentation of underwater targets based on shallow water multi-beam water depth data according to claim 1, wherein in the step S3, the plane distance segmentation threshold is set to 8% -10% of the average water depth of all filtered water depth points in the buffer zone, if the local outer point accounts for more than 50%, the plane segmentation is performed by adopting a minimum median variance sampling consistency method, and the water depth points of the background topography 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 the step S3, a specific formula for resolving plane parameters by adopting a RANSAC plane segmentation algorithm is as follows:
where a, b, and c are parameters of plane equation ax+by+cz+1=0, and x, y, and z are coordinates of water depth points.
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 the step S4, the clustering distance threshold is set to 8% -10% of the average water depth; if the clustering targets contain more noise points, the minimum clustering point number needs to be set, the minimum clustering point number is set to be 5, and the clustering targets lower than the clustering point number are 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 the step S4, the euclidean distance clustering is a nearest neighbor clustering algorithm, and the euclidean distance between adjacent points is calculated by:
dividing a discrete point set into a series of point clusters according to a certain distance scale, wherein the given clustering distance d is 8% -10% of the water depth, and if the Euclidean distance between two adjacent points is less than or equal to d, the two points are clustered 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 a pair of
Each point pi e P, add pi to the current queue Q, for each point pi e Q: taking d as radius, searching neighbor point of point pi and storing into P i The neighborhood points of k and pi conform to the principle that the Euclidean distance is smaller than or equal to d, and for each p i k∈P i k, checking whether the point is processed, if not, adding Q, and when all the points in Q in the linked list are processed, adding Q into the linked list C, and emptying Q; 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 performed based on the underwater target real-time segmentation method based on the shallow water multi-beam water depth data of claim 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110569967.1A CN113567968B (en) | 2021-05-25 | 2021-05-25 | Underwater target real-time segmentation method based on shallow water multi-beam water depth data and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110569967.1A CN113567968B (en) | 2021-05-25 | 2021-05-25 | Underwater target real-time segmentation method based on shallow water multi-beam water depth data and application thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113567968A CN113567968A (en) | 2021-10-29 |
| CN113567968B true CN113567968B (en) | 2024-04-16 |
Family
ID=78161557
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110569967.1A Active CN113567968B (en) | 2021-05-25 | 2021-05-25 | Underwater target real-time segmentation method based on shallow water multi-beam water depth data and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113567968B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2003102624A1 (en) * | 2002-05-31 | 2003-12-11 | Quester Tangent Corporation | Resampling sonar echo time series primarily for seabed sediment classification |
| JP2010190726A (en) * | 2009-02-18 | 2010-09-02 | Toa Harbor Works Co Ltd | Method and system for surveying terrain of bottom of water |
| CN103400405A (en) * | 2013-08-01 | 2013-11-20 | 国家海洋局第二海洋研究所 | Multi-beam bathymetric chart construction method based on seabed digital depth model feature extraction |
| CN107167810A (en) * | 2017-05-02 | 2017-09-15 | 河海大学常州校区 | A kind of submarine target rapid extracting method of side-scan sonar imaging |
| CN108957462A (en) * | 2018-05-22 | 2018-12-07 | 中国海洋大学 | A kind of multi-beam water body data processing method based on smooth bottom |
| CN109035224A (en) * | 2018-07-11 | 2018-12-18 | 哈尔滨工程大学 | A kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud |
| KR102186733B1 (en) * | 2019-09-27 | 2020-12-04 | 포항공과대학교 산학협력단 | 3D modeling method for undersea topography |
| CN112558077A (en) * | 2020-12-08 | 2021-03-26 | 广州海洋地质调查局 | Multi-beam filtering method and processing terminal based on adjacent banners |
-
2021
- 2021-05-25 CN CN202110569967.1A patent/CN113567968B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2003102624A1 (en) * | 2002-05-31 | 2003-12-11 | Quester Tangent Corporation | Resampling sonar echo time series primarily for seabed sediment classification |
| JP2010190726A (en) * | 2009-02-18 | 2010-09-02 | Toa Harbor Works Co Ltd | Method and system for surveying terrain of bottom of water |
| CN103400405A (en) * | 2013-08-01 | 2013-11-20 | 国家海洋局第二海洋研究所 | Multi-beam bathymetric chart construction method based on seabed digital depth model feature extraction |
| CN107167810A (en) * | 2017-05-02 | 2017-09-15 | 河海大学常州校区 | A kind of submarine target rapid extracting method of side-scan sonar imaging |
| CN108957462A (en) * | 2018-05-22 | 2018-12-07 | 中国海洋大学 | A kind of multi-beam water body data processing method based on smooth bottom |
| CN109035224A (en) * | 2018-07-11 | 2018-12-18 | 哈尔滨工程大学 | A kind of Technique of Subsea Pipeline Inspection and three-dimensional rebuilding method based on multi-beam point cloud |
| KR102186733B1 (en) * | 2019-09-27 | 2020-12-04 | 포항공과대학교 산학협력단 | 3D modeling method for undersea topography |
| CN112558077A (en) * | 2020-12-08 | 2021-03-26 | 广州海洋地质调查局 | Multi-beam filtering method and processing terminal based on adjacent banners |
Non-Patent Citations (3)
| Title |
|---|
| Clustering Statistic Hough Transform Based Estimation Method for Motion Elements of Multiple Underwater Targets;ZHEPING YAN等;《IEEE Access》;全文 * |
| GeoSwath多波束系统在海底目标探测中的应用;吴利红;许文海;刘维;臧力龙;;大连海事大学学报(第01期);全文 * |
| SeaBeam全海深多波束测深系统及应用;李治远等;《海岸工程》;第40卷(第1期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113567968A (en) | 2021-10-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110969624B (en) | Laser radar three-dimensional point cloud segmentation method | |
| CN106780524B (en) | Automatic extraction method for three-dimensional point cloud road boundary | |
| CN106599129B (en) | A kind of multi-beam point cloud data denoising method for taking lineament into account | |
| CN110807781B (en) | Point cloud simplifying method for retaining details and boundary characteristics | |
| CN107644452A (en) | Airborne LiDAR point cloud roof dough sheet dividing method and system | |
| CN111047704B (en) | Multi-beam sounding data rough error automatic clearing method for improving region growing algorithm | |
| Wawrzyniak et al. | MSIS image postioning in port areas with the aid of comparative navigation methods | |
| CN109900338A (en) | A kind of road surface pit slot volume measuring method and device | |
| CN102201125A (en) | Method for visualizing three-dimensional imaging sonar data | |
| CN115840205B (en) | Terrain area metering method and system based on laser radar technology | |
| CN113567968B (en) | Underwater target real-time segmentation method based on shallow water multi-beam water depth data and application thereof | |
| CN114417951A (en) | Unsupervised machine learning-based automatic subdivision optimization method for ocean unstructured grid | |
| Sinai et al. | Mine-Like Objects detection in Side-Scan Sonar images using a shadows-highlights geometrical features space | |
| CN113570005A (en) | Long-distance ship type identification method based on airborne photon radar | |
| CN117115012A (en) | Road surface point cloud marking segmentation denoising method | |
| CN112907615B (en) | Submarine landform unit contour and detail identification method based on region growing | |
| CN105844644B (en) | Extra large land clutter Scene Segmentation based on morphology intermediate value derivative | |
| CN111881964A (en) | Linear building mode identification method and system based on Delaunay triangulation network | |
| CN116012613B (en) | Method and system for measuring and calculating earthwork variation of strip mine based on laser point cloud | |
| CN114814796B (en) | Method, device and equipment for extracting water surface travelable area based on high-precision map | |
| Wang et al. | Bottom Tracking Method Based on LOG/Canny and the Threshold Method for Side-scan Sonar. | |
| CN117253205A (en) | Road surface point cloud rapid extraction method based on mobile measurement system | |
| CN113763280A (en) | Region growing algorithm based on spatial hierarchical topological relation for point cloud denoising | |
| Wang et al. | Study on edge detection of LIDAR point cloud | |
| Zhang et al. | Bridge substructure feature extraction based on the underwater sonar point cloud data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |