CN114627367A - Sea bottom line detection method for side-scan sonar image - Google Patents

Sea bottom line detection method for side-scan sonar image Download PDF

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CN114627367A
CN114627367A CN202210531542.6A CN202210531542A CN114627367A CN 114627367 A CN114627367 A CN 114627367A CN 202210531542 A CN202210531542 A CN 202210531542A CN 114627367 A CN114627367 A CN 114627367A
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image
line
scan sonar
sea bottom
obtaining
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CN114627367B (en
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刘大川
何冬晓
侯健
马治忠
孙元宏
任峰
王芳
宋晓晓
杨德鹏
陈默
王茂旭
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Qingdao Zhonghai Jiye Marine Technology Co ltd
Rizhao Marine And Fishery Research Institute Rizhao Sea Area Use Dynamic Monitoring Center Rizhao Aquatic Wildlife Rescue Station
Beihai Ocean Technology Assurance Center Of State Oceanic Administration People's Republic Of China
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Qingdao Zhonghai Jiye Marine Technology Co ltd
Rizhao Marine And Fishery Research Institute Rizhao Sea Area Use Dynamic Monitoring Center Rizhao Aquatic Wildlife Rescue Station
Beihai Ocean Technology Assurance Center Of State Oceanic Administration People's Republic Of China
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques

Abstract

The invention relates to the technical field of side-scan sonar image analysis, in particular to a submarine line detection method of a side-scan sonar image, which analyzes by using a mode of combining K-Means and average peak detection and comprises the following steps: s1, obtaining the image amplitude of the original side-scan sonar image; s2, performing K-means clustering on the original image; s3, obtaining a boundary line; s4, obtaining an average gray scale statistical curve; s5, obtaining the average pixel position of the sea bottom line; s6, extracting the sea bottom line; and S7, drawing a seabed line. The invention can detect the submarine line of the side-scan sonar image containing the target object, accurately display the submarine line and avoid the defect that the traditional maximum amplitude method or maximum gradient method cannot accurately display.

Description

Submarine line detection method of side-scan sonar image
Technical Field
The invention relates to the technical field of side-scan sonar image analysis, in particular to a sea bottom line detection method for a side-scan sonar image.
Background
The characteristic target information in the side-scan sonar image has important application value in the aspects of ocean scientific research (such as seabed sediment distribution and deep-sea hydrothermal solution forming mechanism), ocean engineering (such as submarine pipeline site selection, submarine landform acquisition and sunken ship discovery), ocean military and the like. The sea bottom line in the side-scan sonar waterfall layout represents the distance from the towed fish to the sea bottom, is the boundary line between a water drop area and a substrate area, and is also an important parameter for target measurement, slant distance correction and image gray level equalization. The high-quality side-scan sonar image which can efficiently and accurately track the submarine line has very important significance for national defense activities such as underwater torpedo detection, enemy underwater investigation equipment identification and the like. At present, side-scan Sonar processing software (such as Triton, Sonar Web, Discovery and the like) at home and abroad adopts a traditional maximum amplitude method or a maximum gradient method as a semi-automatic undersea line tracking method. However, because the reflection intensity of the target object is generally higher compared with the seabed reflection of the natural substrate, and the target area generally contains the shadow, the two aspects affect the target area, so that the seabed line tracking accuracy of the traditional maximum amplitude method or the maximum gradient method in the target area is lower, and the effective tracking of the seabed line in the target area by the traditional maximum amplitude method or the maximum gradient method is difficult to realize effectively.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a sea bottom line detection method for a side-scan sonar image.
In order to achieve the purpose, the invention adopts the following technical scheme:
a submarine line detection method of a side-scan sonar image utilizes a mode of combining K-Means and average peak detection to carry out analysis, and comprises the following steps:
s1, obtaining the image amplitude of the side scan sonar original image: reading an original image of a side scan sonar containing a target object to obtain an overall image frame containing pixel points, and expressing the overall image frame by m rows and n columns;
s2, performing K-means clustering on the original images: selecting a K value of 2, dividing pixel points of an original image into a water column region and a substrate region, and classifying to obtain a clustering image;
s3, obtaining boundary line: carrying out binarization processing on the clustered image to obtain a binarized image comprising a plurality of boundary lines;
s4, obtaining an average gray scale statistical curve: carrying out gray level analysis on the original image, and calculating the average gray level value of each column of the original image to obtain an average gray level statistical curve;
s5, obtaining the average pixel position of the sea bottom line: carrying out maximum amplitude detection on the average gray scale statistical curve obtained in S4 to obtain the average pixel where the sea bottom line is locatedPosition ofRepresented by pn；
S6, extracting the sea bottom line: the method for extracting the sea bottom line from the binarized image obtained in S3 comprises the following steps:
s6.1, from the first line of the binary imagepnAs a center, selectingdFor searching for distances, in [ QUOTE
Figure 100002_DEST_PATH_IMAGE002A
Figure 100002_DEST_PATH_IMAGE002AA
]In the search interval of (2), the image point in the first line is extracted and QUOTE is used
Figure 100002_DEST_PATH_IMAGE004A
Figure 100002_DEST_PATH_IMAGE004AA
Represents;
s6.2, QUOTE from the second line of the binarized image
Figure 100002_DEST_PATH_IMAGE004AAA
Figure 100002_DEST_PATH_IMAGE004AAAA
As a center, selectingdFor searching for distances, in [ QUOTE
Figure 100002_DEST_PATH_IMAGE006A
Figure 100002_DEST_PATH_IMAGE006AA
]In the search interval of (2), image points in the second line are extracted using QUOTE
Figure 100002_DEST_PATH_IMAGE008A
Figure 100002_DEST_PATH_IMAGE008AA
Represents;
s6.3, repeating the steps until the extraction of the image points of all the rows of the binary image is finished;
all image points finally extracted by S6 constitute the scanning seafloor line;
s7, drawing a sea bottom line: the scanned sea-bottom line obtained in S6 is drawn onto the original image to obtain a final sea-bottom line.
Preferably, in said S6,dhas a value range of QUOTE
Figure 100002_DEST_PATH_IMAGE010A
Figure 100002_DEST_PATH_IMAGE010AA
。
Preferably, in S6, if the search space exceeds the map width, the part of the search space exceeding the map width is removed.
The invention has the beneficial effects that:
1. the submarine line detection method can be used for carrying out submarine line detection on the side-scan sonar image containing the target object, and precisely detecting the submarine line.
2. The water column region and the substrate region are rapidly divided through the clustering image and the binarization image, so that the position of the sea bottom line is conveniently analyzed.
3. The submarine line interval is positioned through the average pixel position, and the submarine line detection efficiency is improved.
Drawings
FIG. 1 is an overall step diagram of the method for detecting sea bottom lines of side-scan sonar images according to the present invention;
FIG. 2 is an original image of a first side scan sonar;
FIG. 3 is the sea-bottom line tracking result of the original image maximum amplitude method of the first side-scan sonar;
FIG. 4 is a clustered image of a first side scan sonar;
FIG. 5 is a binarized image of a first side scan sonar;
FIG. 6 is a statistical plot of the mean gray scale of a first side-scan sonar;
FIG. 7 is a scanned sea bottom line obtained after the search of the first side-scan sonar binarized image;
FIG. 8 is a first raw image with the final seafloor line;
FIG. 9 is an original image of a second side scan sonar;
FIG. 10 is the sea bottom line tracking result of the maximum amplitude method of the original image of the second side scan sonar;
FIG. 11 is a clustered image of a second side scan sonar;
FIG. 12 is a binarized image of a second side scan sonar;
FIG. 13 is a statistical plot of the average gray scale of the second side scan sonar;
FIG. 14 is the scanned sea bottom line obtained after the search of the binarized image of the second side scan sonar;
FIG. 15 is a second original image with the final seafloor line plotted.
In the figure, 1, water column region; 2. a substrate region; 3. a target object; 4. averaging pixel positions; 5. scanning the sea bottom line; 6. and finally, the sea bottom line.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1, a submarine line detection method for a side-scan sonar image uses a combination of K-Means and average peak detection for analysis, where K-Means is also called K-Means clustering algorithm (K-Means clustering algorithm), which is an iterative solution clustering analysis algorithm. The seabed line detection method integrally comprises the following steps:
s1, obtaining the image amplitude of the original image of the side-scan sonar: and reading an original image of the side scan sonar containing the target object 3, and obtaining an overall picture containing pixel points. The image contains all pixel points of the original image, and is processed by QUOTE
Figure DEST_PATH_IMAGE012A
Figure DEST_PATH_IMAGE012AA
And (4) showing.
S2, performing K-means clustering on the original images: and selecting a K value of 2, wholly dividing the pixel points of the original image into a water column region 1 and a substrate region 2, and classifying to obtain a clustering image. The K value of 2 is set to divide the original image into two parts, the divided water column region 1 contains the target 3, and the junction between the water column region 1 and the substrate region 2 contains the sea bottom line.
S3, obtaining boundary line: and carrying out binarization processing on the clustered image to obtain a binarized image comprising a plurality of boundary lines, wherein the boundary lines of the binarized image are prominent.
S4, obtaining an average gray scale statistical curve: and carrying out gray level analysis (counting according to the direction vertical to the flight path) on the original image, and calculating the average gray level of each column of the original image to obtain an average gray level statistical curve.
S5, obtaining the average pixel position 4 of the sea bottom line: carrying out maximum amplitude detection on the average gray scale statistical curve obtained in S4 to obtain the average pixel position 4 of the sea bottom linepnTo indicate. The maximum amplitude detection is to find the maximum amplitude point in the average gray scale statistical curve and to set the pixel position corresponding to the maximum amplitude point as the average pixel position 4.
S6, extracting the sea bottom line: the method for extracting the sea bottom line from the binarized image obtained in S3 comprises the following steps:
s6.1, from the first line of the binary imagepnAs the center, select d as the search distance in [ QUOTE
Figure DEST_PATH_IMAGE002AAA
Figure DEST_PATH_IMAGE002AAAA
]In the search interval of (2), the image point in the first line is extracted and QUOTE is used
Figure DEST_PATH_IMAGE004_5A
Figure DEST_PATH_IMAGE004_6A
This image point is a point of a white portion in the binarized image;
s6.2, QUOTE from the second line of the binarized image
Figure DEST_PATH_IMAGE004_7A
Figure DEST_PATH_IMAGE004_8A
As the center, select d as the search distance in [ QUOTE
Figure DEST_PATH_IMAGE006AAA
Figure DEST_PATH_IMAGE006AAAA
]In the search interval of (2), image points in the second line are extracted, using QUOTE
Figure DEST_PATH_IMAGE008AAA
Figure DEST_PATH_IMAGE008AAAA
This image point is also a point of a white portion in the binarized image;
and S6.3, repeating the steps until the extraction of the image points of all the rows of the binary image is completed, so that a section of white contour line can be obtained, and the section of contour line finally extracted through S6 is the scanning seabed line 5.
It should be noted that the above-mentioned search distance d has a value in the range of QUOTE
Figure DEST_PATH_IMAGE010AAA
Figure DEST_PATH_IMAGE010AAAA
If the value is too small, the searching range is too small, and the image point can not be easily searched; the value is too large, the search range is too large, and the pixel points of the undersea line are not unique.
It should be noted that, when the search region exceeds the image frame, the portion of the search region exceeding the image frame needs to be removed, so as to ensure that the search range does not exceed the binary image.
S7, drawing a sea bottom line: the scanned seabed line 5 obtained in S6 is drawn onto the original image to obtain the final seabed line 6.
Example one
Figure 2 is an original image of a first side scan sonar including a water column region 1 and a substrate region 2.
FIG. 3 is the result of the line tracing directly by the maximum amplitude method, and it can be seen that there is no precise delineation of the line. The detection of the sea bottom line is performed by the method of the present invention.
First, the image frame of the first original image is read, and the image frame size is obtained as 265 rows × 275 columns (i.e., m =265, n = 275).
Referring to fig. 4, after that, clustering is performed by K-means, and K =2 is taken, a cluster image is obtained, and it can be seen that the cluster image entirely includes two parts.
Referring to fig. 5, the cluster image is binarized to obtain a binarized image, and a plurality of boundary lines are visible, but these boundary lines include the sea bottom line and the boundary of the target 3, and therefore, it is necessary to perform search and screening.
Referring to fig. 6, gray statistics are performed on the first original image, and an average gray value of each column of the original image is calculated, so that an average gray statistics curve of the first original image can be obtained. The maximum amplitude detection of the average gray scale statistic curve can result in 193 as the average pixel position 4 where the sea bottom line is located, that ispn=193。
Referring to fig. 7, a first line of an image is binarizedpn(i.e., 193) as the center, and a distance of 0.05n (i.e., 14), i.e., at [179,207 ]]Is searched for in the interval of (2) to obtain the image point QUOTE of the first line
Figure DEST_PATH_IMAGE014A
Figure DEST_PATH_IMAGE014AA
. Order Quote
Figure DEST_PATH_IMAGE016A
Figure DEST_PATH_IMAGE016AA
In the second row [183,214]Is searched for in the interval of (2) to obtain the image point QUOTE of the second line
Figure DEST_PATH_IMAGE008_5A
Figure DEST_PATH_IMAGE008_6A
And repeating the steps until all the rows are searched, and extracting the scanning seabed line 5.
Referring to fig. 8, the scanned sub-sea line 5 is rendered onto the original image to obtain the final sub-sea line 6.
Example two
Figure 9 is a second side scan sonar raw image, including water column region 1 and substrate region 2.
FIG. 10 is the result of the seafloor line tracking directly by the maximum amplitude method, and it can be seen that the seafloor line profile is not accurately delineated. The detection of the sea bottom line is performed by the method of the present invention.
First, the image frame of the second original image is read, and the image frame size is 623 rows × 675 columns (i.e., m =623, n = 675).
Referring to fig. 11, after that, clustering is performed by K-means, and K =2 is taken to obtain a clustered image, and it can be seen that the clustered image includes two parts as a whole.
Referring to fig. 12, the cluster image is binarized to obtain a binarized image, and a plurality of boundary lines are visible, but these boundary lines include the sea bottom line and the boundary of the target 3, and therefore, it is necessary to perform search and screening.
Referring to fig. 13, the average gray scale statistical curve of the first original image may be obtained by performing gray scale statistics on the second original image and calculating the average gray scale value of each column of the original image. Maximum vibration is carried out on the average gray level statistical curveThe average pixel position 4 (of the sea bottom line) can be obtained by amplitude detectionpn=670）。
Referring to fig. 14, a first line of an image is binarized topn(i.e., 670) as the center, and a distance of 0.08n (i.e., 54), i.e., at [616,724 ]]The search is carried out in the interval of (2), but because the maximum image frame of the original image only reaches 675, the exceeding part is removed, and the search interval of the first line is finally determined as [616,675 ]]Obtaining the image point QUOTE of the first line
Figure DEST_PATH_IMAGE018A
Figure DEST_PATH_IMAGE018AA
. Order Quote
Figure DEST_PATH_IMAGE020A
Figure DEST_PATH_IMAGE020AA
In the second row [582,690]But because the maximum frame of the original image only reaches 675, the excess part is removed, and the search interval of the second row is finally determined as [585,675 ]]Obtaining the image point QUOTE of the second line
Figure DEST_PATH_IMAGE008_7A
Figure DEST_PATH_IMAGE008_8A
And repeating the steps until all the rows are searched, and extracting the scanning seabed line 5.
Referring to fig. 15, the scanned sub-sea line 5 is rendered onto the original image to obtain the final sub-sea line 6.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims (3)

1. A submarine line detection method of side scan sonar images is characterized in that: the analysis was performed using a combination of K-Means and mean peak detection, which included the following steps:
s1, obtaining the image amplitude of the side scan sonar original image: reading an original image of a side scan sonar containing a target object (3) to obtain an overall picture containing pixel points, and expressing the overall picture by m rows and n columns;
s2, performing K-means clustering on the original images: selecting a K value of 2, dividing pixel points of an original image into a water column region (1) and a substrate region (2), and classifying to obtain a clustering image;
s3, obtaining boundary line: carrying out binarization processing on the clustered image to obtain a binarized image comprising a plurality of boundary lines;
s4, obtaining an average gray scale statistical curve: carrying out gray level analysis on the original image, calculating the average gray level of each column of the original image, and obtaining an average gray level statistical curve;
s5, obtaining the average pixel position of the sea bottom line (4): maximum amplitude detection is carried out on the average gray scale statistical curve obtained in S4 to obtain the average pixel position (4) of the sea bottom lineWith pnRepresents;
s6, extracting the sea bottom line: the method for extracting the sea bottom line from the binarized image obtained in S3 comprises the following steps:
s6.1, from the first line of the binary imagepnAs a center, selectingdFor searching for distances, in [ QUOTE
Figure DEST_PATH_IMAGE002A
Figure DEST_PATH_IMAGE002AA
]In the search interval of (2), the image point in the first line is extracted and QUOTE is used
Figure DEST_PATH_IMAGE004A
Figure DEST_PATH_IMAGE004AA
Represents;
s6.2, QUOTE from the second line of the binarized image
Figure DEST_PATH_IMAGE004AAA
Figure DEST_PATH_IMAGE004AAAA
As a center, selectingdFor searching for distances, in [ QUOTE
Figure DEST_PATH_IMAGE006A
Figure DEST_PATH_IMAGE006AA
]In the search interval of (2), image points in the second line are extracted, using QUOTE
Figure DEST_PATH_IMAGE008A
Figure DEST_PATH_IMAGE008AA
Represents;
s6.3, repeating the steps until the extraction of the image points of all the rows of the binary image is finished;
all image points finally extracted by S6 constitute the scanning seafloor line (5);
s7, drawing a seabed line: the scanned sea bottom line (5) obtained in S6 is rendered onto the original image to obtain the final sea bottom line (6).
2. The sea line detection method of the side-scan sonar image according to claim 1, comprising: in the step S6, in the step S,dhas a value range of QUOTE
Figure DEST_PATH_IMAGE010A
Figure DEST_PATH_IMAGE010AA
。
3. The sea line detection method of the side-scan sonar image according to claim 2, comprising: in S6, if the search interval exceeds the map width, the part of the search interval exceeding the map width is removed.
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CN116930976A (en) * 2023-06-19 2023-10-24 自然资源部第一海洋研究所 Submarine line detection method of side-scan sonar image based on wavelet mode maximum value
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CN117554966A (en) * 2024-01-12 2024-02-13 哈船通航(三亚)技术服务合伙企业(有限合伙) Sonar data intelligent processing method for accurate positioning of whole water area
CN117554966B (en) * 2024-01-12 2024-03-19 哈船通航(三亚)技术服务合伙企业(有限合伙) Sonar data intelligent processing method for accurate positioning of whole water area

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