CN116930976B - Submarine line detection method of side-scan sonar image based on wavelet mode maximum value - Google Patents
Submarine line detection method of side-scan sonar image based on wavelet mode maximum value Download PDFInfo
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Abstract
The invention relates to the technical field of ocean mapping, in particular to a submarine line detection method of a side-scan sonar image based on wavelet mode maxima. The method comprises the following steps: s1, constructing a side-scan sonar imaging system: the sonar transducer array continuously transmits and receives sound pulses to the sea bottom along with the movement of the towed fish, and the processor unit can display image information in an arrangement mode according to a time sequence, so that a two-dimensional side-scan sonar waterfall table is formed; s2, detecting a wavelet mode maximum value seabed line: detecting a seabed line of the side-scan sonar image by using an edge detection method of the image; s3, obtaining experimental results and comparative analysis: in order to verify the applicability of the wavelet mode maximum value-based image edge detection method to the side scan sonar image and the detection effect of the seabed line, the seabed line detection of the original image and the seabed line detection of the noise added image are respectively carried out. The invention has more efficient noise suppression in the image and more accurate detection result.
Description
Technical Field
The invention relates to the technical field of ocean mapping, in particular to a submarine line detection method of a side-scan sonar image based on wavelet mode maxima.
Background
The side-scan sonar is used as ocean detection equipment capable of acquiring high-resolution submarine topography and topography images, is widely applied to the ocean engineering fields such as topography and topography detection, underwater object and obstacle detection, substrate type detection and the like, and also has the important role of applying the side-scan sonar images to the fields such as submarine substrate classification, topography inversion, image interpretation and the like by students, and the high-resolution sonar images play an important role in ocean detection and resource development. In order to meet the deep application requirements of accurate detection and identification of targets, the processing, analysis and interpretation of the side-scan sonar image are needed, wherein the seabed line of the side-scan sonar image is used as a reference line for geometric correction and radiation correction in image preprocessing, and is a reference line for accurately positioning all targets in the image. Therefore, it is important to accurately locate the seabed line on the sonar image, which affects the quality of the final interpretation of the side scan sonar image. Currently, commercial software (such as SonarWeb, trition and the like) for side-scan sonar image processing generally adopts a threshold control method when in sea bottom line detection, and parameters such as amplitude threshold parameters, continuity parameters, height threshold values, ping mean values and the like are manually adjusted according to real-time submarine environment and sonar image quality, so that the method is relatively complex and can be influenced by human factors. Zhang Jibo and the like propose a seabed line detection method based on a Log operator by utilizing the application of the Log operator in image edge detection according to the characteristics of a sonar waterfall diagram, but the quality requirement on an original sonar image is higher, and the detection effect is poor under the condition of noise. Zhao Jianhu combines Kalman filtering and seabed line final peak method, provides a seabed line self-adaptive comprehensive detection method, and carries out seabed line tracking detection on a sonar image containing noise, but the core principle is based on the conclusion that the first seabed echo returns later than other interference echoes, and certain generality is lacking when the intensity of the interference echo is higher than that of the seabed echo. Wang Aixue and the like propose a seabed line detection method combining point density clustering and chain searching based on seabed line space distribution characteristics, which can track seabed lines in complex water environment, but the points which are possibly seabed lines need to be selected, and the algorithm implementation is complex. The edge detection method based on wavelet transformation is mature in application to images, but has relatively few application in sonar image processing, and particularly in submarine line detection applied to side scan sonar images, and lacks relevant experimental data.
Disclosure of Invention
The invention aims to solve the technical problems that: the method for detecting the seabed line of the side-scan sonar image based on the wavelet mode maximum value overcomes the defects of the prior art, is more efficient in noise suppression in the image, and is more accurate in detection result.
The technical scheme of the invention is as follows:
a submarine line detection method of a side-scan sonar image based on wavelet mode maxima comprises the following steps:
s1, constructing a side-scan sonar imaging system: the side-scan sonar system comprises underwater towed fish, a connecting line and a deck unit, wherein in the course of navigation detection, transducer matrixes at two sides of the towed fish emit short acoustic pulses to the sea bottom at a certain inclination angle, sound waves are outwards diffused in a spherical wave mode, when the acoustic pulses meet the sea bottom or an object in water, the acoustic pulses are scattered, back-scattered echoes are returned to be received by the transducer according to an original propagation path and are transmitted to a sonar data processing unit upwards through a cable, the processing unit can display the gray level intensity of an image according to the intensity of the scattered echoes, the sonar transducer matrixes emit and receive the acoustic pulses to the sea bottom along with the movement of the towed fish, and the processor unit can display image information in a sequence, so that a two-dimensional side-scan sonar waterfall map is formed;
s2, detecting a wavelet mode maximum value seabed line: the two-dimensional side scan sonar waterfall diagram analysis can obtain that an image-free area is formed in the central area of the waterfall diagram due to the occurrence of a 'water column area', the area is represented as a pixel with a gray value of 0 on the basis of echo intensity quantification because no echo signal arrives, the boundary of the water column area and an image area formed by scanning lines is represented as gray change from weak to strong on the image pixel, and the extreme point of the change at the boundary is the position of a sea bottom line of the side scan sonar image; the edge of the image is defined as the boundary of two image areas with different gray scales, and the gray scales of the edge areas change drastically, so that the sea bottom line of the side scan sonar image is detected by using an edge detection method of the image;
s3, obtaining experimental results and comparative analysis: in order to verify the applicability of the wavelet mode maximum value-based image edge detection method to the side scan sonar image and the detection effect of the seabed line, the seabed line detection of the original image and the seabed line detection of the noise added image are respectively carried out; the experimental result can accurately locate the position of the seabed line, and the detection result of the comparison analysis comprises the maximum error and the root mean square error of comparison with the multi-beam actually measured water depth, the noise suppression capability in the image, the stability of the detection result and the influence of noise suppression.
Preferably, in step S1, the two-dimensional side scan sonar waterfall graph includes a path line, a seabed line, and a scan line, wherein:
the navigation line is characterized by a motion track of the towed fish and is also a datum line for measuring the distance from the towed fish to a seabed target;
the sea bottom line is characterized by the height from the towed fish to the water bottom, reflects the fluctuation of the submarine topography right below the towed fish, and is a datum line for slope correction, radiation correction and target measurement; the portion of the image between the course line and the seabed line is called the "water column region", which is the first echo location where the sonar array reaches the seabed, and is displayed as a full black pixel on the sonar image because no echo is received before that location;
the scanning lines are components of the side scan sonar image, each Ping scanning line is orderly arranged to form the submarine sonar image, and the pixel gray level change of the part of the image is processed and displayed by the sonar processing unit according to the received signal intensity, so that the target characteristics and the topographical features are reflected.
Preferably, in the step S1, the back scattering intensity of the submarine target facing the sound wave irradiation surface is strong, and the strong gray pixel is displayed on the sonar image, while the reflection intensity facing away from the sound wave irradiation surface is weak or no reflection, and the weak gray black pixel is displayed.
Preferably, in the step S2, the detection based on the wavelet mode maximum undersea line includes the following steps:
s211, inputting a side scan sonar image analyzed from an XTF file, and intercepting a proper size;
s212, setting a scale coefficient S of a wavelet, constructing a smoothing function, selecting a Gaussian function as the smoothing function in an experiment, defining the length and the amplitude value of a filter, calculating derivatives in the x and y directions of pixels in an image by using the smoothing function, performing energy normalization processing, and performing row and column convolution processing on the smoothed image to calculate the wavelet coefficient;
s213, traversing the smoothed original image, and obtaining the gradient direction and the amplitude angle of each pixel point;
s214, dividing an amplitude angle into 4 directions aiming at 8 field points of the pixel point, namely four directions of the horizontal, vertical, 45 degrees or 225 degrees, 135 degrees or 315 degrees of the pixel point, respectively solving local mode maximum values of the image along respective phase angle directions, recording gradient values if the local mode maximum values are the maximum values, and assigning the gradient to zero if the local mode maximum values are not the maximum values;
s215, solving the maximum amplitude, finding the gradient maximum value of all the mode maximum value points, and taking the maximum gradient value as a normalized reference value;
s216, setting a proper threshold, removing false edges caused by noise, and deleting edges which are larger than the retention of the threshold, otherwise, deleting edges which are smaller than the retention of the threshold, linking edge points in the image, obtaining edge information of the image, and positioning the position of a sea bottom line of the side-scan sonar image.
Preferably, in the step S2, the edge detection method based on the wavelet mode maximum value includes the following specific steps:
setting a two-dimensional image functionSelecting a suitable two-dimensional smoothing function>The function has good localization properties, i.e. the following conditions need to be met:
introduction of scale parametersSmoothing the original image under the scale parameter:
and recordThe horizontal and vertical wavelet functions at scale S are:
thus an imageThe two-dimensional wavelet transform at scale S can be expressed as:
i.e.Gradient vector of>The relation between the modulus value of the wavelet transformation and the modulus value number of the wavelet transformation is as follows:
the included angle between the gradient direction and the horizontal direction is the phase angle:
according to the algorithm principle of wavelet mode maximum, the mode maximum of calculating smooth function along gradient direction is obtained and is equivalent to the mode maximum of calculating wavelet transformation, and is recorded asUnit vector +.>And gradient vector->Are parallel and therefore are in the dimension +.>Lower, if dieAlong with->The local maximum value is obtained in the vertical direction, and the point +.>An edge point of the smooth image function;
and determining edge points of the image by detecting the mode maximum value points of the two-dimensional wavelet transformation, and detecting boundary lines between a water column region and a scanning region in the side scan sonar image to realize detection of sea bottom lines.
Preferably, in the step S3, the experimental process is compared with a threshold method and a log operator-based seabed line detection method, and two parameters of a maximum error and a root mean square error are set as quantitative analysis basis, wherein:
the maximum error represents the maximum difference between the detection result and the actual value, and the smaller the maximum value is, the better the detection result is;
the root mean square error represents the deviation degree of the detection result and the actual value, and the smaller the root mean square error is, the closer the whole detection result is to the actual value;
in addition, the side-scan sonar system is affected by various seabed noises in the actual working process, so that in the experimental process, the influence degree of each noise on a sonar image is analyzed, speckle noise with the largest influence effect is added on the basis of an original side-scan sonar image so as to simulate the influence of the side-scan sonar image when the side-scan sonar image is affected by the noise, the seabed line detection results of each method on the noise-added side-scan sonar image are compared again, and the detection effect and the noise suppression capability of each method are analyzed.
Preferably, in the step S3, the detecting of the subsea line of the original image includes the following steps:
taking sounding data of a 3DSS-DX sonar swept experimental area as an actual measurement water depth value, and using the sounding data as an error analysis reference of detection results of all methods:
the threshold method is used for setting the threshold K to 0.5 in experiments on the sonar image detection result, and the seabed line detection result is optimal; in the detection process, the noise point is taken as a seabed line, and the generated seabed line is subjected to filtering smoothing operation, so that the influence of interference factors is eliminated;
the log operator method has different positioning of the seabed line position and obvious difference in the places with larger seabed height fall.
Preferably, in the step S3, a bottom line of the noisy image is detected: the method comprises the following steps:
in the experimental process, gaussian white noise with the mean value of 0 and the noise standard deviation of 0.06 is added into an original side-scan sonar image, and the influence of reverberation noise on the sonar image is simulated to compare the inhibition capability of each seabed line detection method on noise factors; for the original side-scan sonar image and the sonar image added with noise, comparison between the two images obviously shows that after noise is added, the resolution of the side-scan sonar image is reduced, the boundary becomes fuzzy, the definition of the whole sonar image is lowered, and the readability is poor.
Compared with the prior art, the invention has the following beneficial effects:
the method is applied to the submarine line detection of the side scan sonar image, the detection effects of a threshold method and a differential Log operator method are compared and analyzed, and in order to further verify the applicability of the method in a more complex actual marine working environment, noise is added in the sonar image to simulate the submarine line detection in the actual submarine environment. Experimental results show that the method can accurately detect the seabed line from the side scan sonar image, can effectively inhibit the influence of image noise on the detection result, has the detection positioning accuracy reaching about 0.2m root mean square error, has higher accuracy and more stable detection result compared with other seabed line detection methods, and can provide guarantee for obtaining the high-resolution side scan sonar image.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic flow diagram of the present invention.
Fig. 2 is a schematic diagram of the principle of operation of the side-scan sonar of the present invention.
FIG. 3 is a schematic illustration of the composition of a side-scan sonar image of the present invention.
FIG. 4 is a flow chart of the subsea line detection according to the invention.
Fig. 5 is a pixel domain dot diagram of the present invention.
FIG. 6 is a schematic view of the amplitude of the present invention.
FIG. 7 is a graph of experimental area versus local sonar of the present invention.
Fig. 8 (a) is a thresholding result diagram before filtering smoothing.
Fig. 8 (b) is a diagram showing the result of the thresholding method with the lower left corner enlarged.
Fig. 8 (c) is a thresholding result diagram after filtering smoothing.
Fig. 9 (a) is a chart of the result of the detection of the sea bottom line based on the log operator method.
Fig. 9 (b) is a chart showing the result of the subsea line detection according to the present invention.
FIG. 10 is a comparison of water depths according to the present invention.
FIG. 11 (a) is a sonar image before noise is added.
FIG. 11 (b) is a sonar image after noise is added.
Fig. 12 (a) is a chart of the bottom line detection result of the noisy image with the threshold method k=0.5.
Fig. 12 (b) is a chart of the bottom line detection result of the noisy image with the threshold method k=0.76.
Fig. 12 (c) is a chart of the result of detecting the sea bottom line of the noisy image based on the Log operator.
Fig. 12 (d) is a chart showing the result of detecting the sea bottom line of the noisy image of the present invention.
FIG. 13 is a comparison of detected water depths for noisy images of the present invention.
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
As shown in fig. 1, this embodiment provides a method for detecting a seabed line of a side scan sonar image based on a wavelet mode maximum value, and since the high-resolution side scan sonar image can clearly reflect the topographical features of the seabed, detecting the seabed line as the first step of the side scan sonar image processing can accurately detect whether the position of the seabed line affects the quality of the sonar image. However, the current sea bottom line detection methods of all commonly used side scan sonar images have certain limitations, and reverberation noise in the ocean can influence the precision of sea bottom line detection results. Therefore, based on the composition characteristics of the side scan sonar submarine image, the invention combines the multi-scale analysis of wavelet transformation in the image edge detection, applies the edge detection method of wavelet mode maximum value to the submarine line detection of the side scan sonar image, and performs comparison analysis with the existing submarine line detection methods such as a threshold method, a differential operator method and the like, and the experimental result shows that the invention can more accurately position the submarine line. In addition, noise information is added into the side scan sonar image to simulate a real submarine environment, and compared with other submarine line detection methods, the method has the advantages that noise suppression in the image is more efficient, and the detection result is more accurate.
The invention comprises the following steps:
s1, constructing a side-scan sonar imaging system:
the side-scan sonar system mainly comprises three parts, namely underwater towed fish, a connecting cable and a deck unit, in the course of navigation detection, transducer arrays on two sides of the towed fish emit short acoustic pulses to the seabed at a certain inclination angle, sound waves can be outwards diffused in a spherical wave mode, when the acoustic pulses meet the seabed or an object in water, scattering can occur, back scattering echoes can be returned to be received by the transducers according to an original propagation path, the back scattering echoes are upwards transmitted to a sonar data processing unit through the cable, and the processing unit can display the gray level intensity of images according to the intensity of the scattered echoes. The sonar transducer array continuously transmits and receives sound pulses to the sea bottom along with the movement of the towed fish, and the processor unit can display image information in a sequence, so that a two-dimensional side-scan sonar waterfall diagram is formed, and the specific imaging principle is shown in the following figure 2.
The side scan sonar waterfall map mainly comprises three parts, namely a path line, a seabed line and a scanning line, as shown in fig. 2. The course is characterized as the motion trail of the towed fish and is also a datum line for measuring the distance from the towed fish to the target on the sea floor. The sea floor line is a reference line representing the height from the towed fish to the water bottom, reflecting the fluctuation of the topography of the sea floor directly below the towed fish, and is used for slope correction, radiation correction and target measurement. The portion of the image between the course line and the seabed line is called the "water column region", which is the first echo location where the sonar array reaches the seabed, and is displayed as a full black pixel on the sonar image because no echo was received before that location. The scanning lines are main components of the side scan sonar image, each Ping scanning line is orderly arranged to form the submarine sonar image, and the pixel gray level change of the part of the image is processed and displayed by the sonar processing unit according to the received signal intensity, so that the target characteristics and the topographical features can be reflected. The submarine target of the sound wave irradiation surface has stronger back scattering intensity, and the submarine target presents stronger gray pixels on a sonar image, and the reflection intensity of the back sound wave irradiation surface is weaker or no reflection, and presents weak gray black pixels.
S2, detecting a wavelet mode maximum value seabed line:
as can be obtained from the analysis of the schematic composition diagram of the side scan sonar image in fig. 3, an image-free area is formed in the central area of the waterfall diagram due to the presence of a 'water column area', and the area is represented as a pixel with a gray value of 0 on the quantification of echo intensity due to the fact that no echo signal arrives, the boundary between the water column area and the image area formed by the scanning lines is represented as gray change from weak to strong on the image pixel, and the extreme point of the change at the boundary is the position of the sea bottom line of the side scan sonar image. The edge of the image is defined as the boundary of two image areas with different gray scales, and the gray scales of the edge areas change drastically, so that the sea bottom line of the side scan sonar image can be detected by using the edge detection method of the image. At present, a plurality of methods for detecting the edges of images are provided, and a plurality of edge detection means such as a differential operator method, a morphological method, a curved surface fitting method, a wavelet transformation method, a neural network analysis method, a genetic algorithm and the like are formed. In the image edge detection, the contradiction exists between noise elimination and edge positioning, the noise suppression needs to carry out smoothing operation on the image, but the smooth image can cause information loss in the image, structure shift and edge positioning precision deterioration, and noise information in the image can also cause the edge detection precision deterioration, so that the compromise processing needs to be carried out in the noise suppression and edge positioning. The edge detection method based on the wavelet mode maximum value benefits from good time-frequency characteristics of wavelet analysis, can carry out multi-scale analysis on edge information in a sonar image, and can more completely retain details in the image while suppressing noise influence. By combining the characteristics of the side-scan sonar image and the applicability of edge detection, the invention selects the edge detection algorithm based on the wavelet mode maximum as a seabed line detection method of the side-scan sonar image.
The principle of wavelet mode maximum edge detection is explained below:
the edge detection method based on the wavelet mode maximum value is proposed by Mallat at the earliest time, the method utilizes the wavelet mode maximum value to detect singular points in signals, the singular points in the signals generally contain important physical significance, and the singular points of gray level change in images reflect the edge information of the images and are opposite to the local mode maximum value of wavelet transformation. Wherein the modulus of wavelet transformation is proportional to the modulus of the gradient vector of the image, and the amplitude angle of wavelet transformation is the included angle between the gradient vector of the image and the horizontal direction, which is the direction of the edge point of the image. Therefore, the edge detection only needs to find the maximum point of the mode along the gradient direction, namely the maximum point of the mode of wavelet transformation along the gradient direction is the edge point in the image. The basic principle of the wavelet mode maximum edge detection algorithm is as follows:
setting a two-dimensional image functionSelecting a suitable two-dimensional smoothing function>The function has good localization properties, i.e. the following conditions need to be met:
introduction of scale parametersSmoothing the original image under the scale parameter:
and recordThe horizontal and vertical wavelet functions at scale S are:
thus an imageThe two-dimensional wavelet transform at scale S can be expressed as:
i.e.Gradient vector of>The relation between the modulus value of the wavelet transformation and the modulus value number of the wavelet transformation is as follows:
the included angle between the gradient direction and the horizontal direction is the phase angle:
according to the algorithm principle of wavelet mode maximum, the mode maximum of calculating smooth function along gradient direction is obtained and is equivalent to the mode maximum of calculating wavelet transformation, and is recorded asUnit vector +.>And gradient vector->Are parallel and therefore are in the dimension +.>Lower, if dieAlong with->The local maximum value is obtained in the vertical direction, and the point +.>An edge point of the smooth image function;
and determining edge points of the image by detecting the mode maximum value points of the two-dimensional wavelet transformation, and detecting boundary lines between a water column region and a scanning region in the side scan sonar image to realize detection of sea bottom lines.
S21, detecting seabed lines of the side scan sonar images:
based on the principle of edge detection of wavelet mode maxima, a flow chart of the detection of the sea bottom line of the side scan sonar image adopted by the invention is shown in fig. 4.
The algorithm comprises the following steps:
s211, inputting a side scan sonar image analyzed from an XTF file, and intercepting a proper size;
s212, setting a scale coefficient S of a wavelet, constructing a smoothing function, selecting a Gaussian function as the smoothing function in an experiment, defining the length and the amplitude value of a filter, calculating derivatives in the x and y directions of pixels in an image by using the smoothing function, performing energy normalization processing, and performing row and column convolution processing on the smoothed image to calculate the wavelet coefficient;
s213, traversing the smoothed original image, and obtaining the gradient direction and the amplitude angle of each pixel point;
s214, dividing the amplitude angle into 4 directions shown in FIG. 6, namely four directions of the pixel point, namely the horizontal direction, the vertical direction, 45 degrees or 225 degrees, 135 degrees or 315 degrees, aiming at 8 field points of the pixel point, respectively solving the local mode maximum value of the image along the respective phase angle direction, recording the gradient value if the local mode maximum value is the maximum value, and assigning the gradient to zero if the local mode maximum value is not the maximum value;
s215, solving the maximum amplitude, finding the gradient maximum value of all the mode maximum value points, and taking the maximum gradient value as a normalized reference value;
s216, setting a proper threshold, removing false edges caused by noise, and deleting edges which are larger than the retention of the threshold, otherwise, deleting edges which are smaller than the retention of the threshold, linking edge points in the image, obtaining edge information of the image, and positioning the position of a sea bottom line of the side-scan sonar image.
S3, obtaining experimental results and comparative analysis:
in order to verify the applicability of the wavelet mode maximum value-based image edge detection method to the side scan sonar image and the detection effect of the seabed line, the invention designs and completes a related simulation experiment and quantitatively compares and analyzes the experimental result.
The experiment is designed and realized based on Matlab program, wherein the experimental data is derived from a submarine actually measured sonar image (a sonar image shown by a big box in FIG. 7) detected by 3DSS-DX sonar on the south of a small pipe island (the position shown by the small box in FIG. 7), the maximum sounding range of the sonar can reach 150m, and the sonar emission pulse length is 22-444/>The maximum vertical width of the transmitting beam can reach 125 degrees, and the submarine side scan sonar image data is obtained by scanning the marine pasture. In the experimental process, a submarine image on one of the survey lines is intercepted, the image consists of 500 Ping sonar data, and a starboard image of the side scan sonar is taken as an experimental object. In the experimental process, the method is compared with a threshold value method and a seabed line detection method based on a log operator, and two parameters of a maximum error and a root mean square error are set as quantitative analysis basis, wherein the maximum error represents the maximum difference between a detection result and an actual value, and the smaller the maximum value is, the better the detection result is; the root mean square error represents the deviation degree of the detection result and the actual value, and the smaller the root mean square error is, the closer the whole detection result is to the actual value. In addition, the influence of various seabed noises can be received in the actual working process of the side-scan sonar system, so that the influence degree of each noise on a sonar image is analyzed in the experimental process, speckle noise with the largest influence effect is added on the basis of an original side-scan sonar image so as to simulate the influence of the side-scan sonar image when the side-scan sonar image is subjected to the noise, the seabed line detection results of each method on the noisy side-scan sonar image are compared again, and the detection effect and the noise inhibition capability of each method are analyzed.
S31, detecting a submarine line of an original image:
in the experiment, various methods are applied to the seabed line detection of the experimental sonar image, and as the 3DSS-DX sonar can accurately record real-time water depth data while scanning the seabed, the sounding data of the scanned experimental area can be used as an actually measured water depth value for the error analysis reference of the detection result of each method. Fig. 8 (a) below shows the result of sonar image detection by the thresholding method, where the threshold K is set to 0.5 in the experiment, and the bottom line detection result is optimal. However, due to the influence of factors such as bubbles of the suspended matter on the sea floor, noise points exist in the water column area, as shown in fig. 8 (b), the noise points are regarded as the seabed line in the detection process, filtering and smoothing operations are required to be carried out on the generated seabed line, the influence of interference factors is eliminated, and the final detection result is shown in fig. 8 (c).
Compared with the detection results based on the log operator method and the detection results based on the log operator method, as shown in the following fig. 9 (a) to 9 (b), the detection results of the seabed line of the three are not greatly different, but in the area of certain details in the image, the positions of the seabed line are not the same, and particularly the difference is obvious in some places with larger seabed height fall. Therefore, the detection results of the three sea bottom lines are compared and analyzed with external multi-beam actual measurement contrast depth data, and as shown in fig. 10, through water depth comparison, the water depth errors detected by a threshold method are larger at Ping 250-325 and Ping 400; at Ping325, the detection error based on the Log operator method is the largest, the detection error exceeding 1m exists in both methods, the detection error of the three methods is quantitatively analyzed, the maximum error and the root mean square error of the seabed line detected by the method are the smallest (table 1), the detection position is basically accurate, the detail processing in the junction of the image area and the water column area is perfect, and the method has good applicability and can effectively and accurately detect the position of the seabed line of the side scan sonar image.
TABLE 1 subsea line detection error
S32, detecting a submarine line of the noisy image:
the working principle of the side-scan sonar system is that underwater detection is carried out by utilizing underwater acoustic echo to acquire submarine topography images, and the whole working process is carried out underwater, so that the quality of the underwater working environment can influence the final image quality. When the side-scan sonar system works, the underwater environment can change along with the change of weather, position and time, suspended particles can be inevitably present in seawater, the navigation of a workboat can excite underwater wake flow, even bubble flow, and scattered particles, bubbles and the like present in the seawater can generate reflection and scattering effects on acoustic echo and overlap target echo, so that the noise of a side-scan sonar image is serious, the texture is weaker and the edge information is poor finally due to confusion with a real submarine signal. Echo signals generated by the scatterers are overlapped at a receiving end to form a reverberation signal, which is a primary factor influencing the image quality of the side-scan sonar, and multiplicative speckle noise related to signals in the reverberation noise is a main influencing part. Therefore, gaussian white noise (speckle noise) with the mean value of 0 and the noise standard deviation of 0.06 is added into the original side-scan sonar image in the experimental process, and the influence of reverberation noise on the sonar image is simulated to compare the inhibition capability of each seabed line detection method on noise factors. Fig. 11 (a) to 11 (b) show an original side scan sonar image and a sonar image after noise is added, and comparison of the two shows that after noise is added, the side scan sonar image is reduced in resolution, the boundary becomes blurred, the definition of the whole sonar image is lowered, and the readability is deteriorated.
Fig. 12 (a) to 12 (d) below are threshold method, based on differential Log operator method and the algorithm of the invention, the detection results of the seabed line of the side scan sonar image added with noise are respectively set with different thresholds in fig. 12 (a) and 12 (b), the detection results are more accurate when the threshold k=0.76 is compared with k=0.5, the detected water depth is closer to the actual water depth (see fig. 13), and the water depth detection error in table 2 is smaller. The threshold method is that the pixel points in the image are set to be 0 or 255 according to the determined threshold value, noise pixels in the water column area become very difficult to eliminate under the influence of noise, the size of the threshold value is required to be continuously adjusted according to a specific side scan sonar image so as to achieve the best effect, and the pixel points are connected on the basis, so that the position of the seabed line is determined. Fig. 12 (c) shows a result of detecting a sea bottom line by a differential Log operator, and shows that the positioning accuracy is inferior to that of a sonar image without noise by this method, and that there are many cases of erroneous detection, indicating that the entire detection result is greatly affected by noise. Since the basic idea of the algorithm is to find the pixel point with the local maximum gradient amplitude in the image and then to perform edge connection, the algorithm essentially uses a quasi-gaussian function to perform a smoothing operation and then to locate the derivative maximum with the first derivative of the direction. The method is very sensitive to noise because noise and edge points have abrupt change characteristics, and is very difficult for a first-order differential operator to extract edge information in an image and inhibit the influence of noise in the image.
In contrast to the detection result (fig. 8 (b)) in the sonar image without noise, the detection result of the two times has higher consistency, and the boundary line between the water column area and the image area can be accurately positioned. This is because wavelet analysis of the present invention has good localization characteristics and multi-scale detection characteristics, and edge information in an image can be extracted accurately while ensuring image quality. As can be seen from the root mean square error detected in the table 2, in the case that the sonar image contains noise, compared with other methods, the result of the seabed line detection is the most accurate, and the experimental result shows that the method has the capability of inhibiting the noise in the sonar image and can effectively reduce the interference of the noise on the seabed line detection result.
TABLE 2 seabed line detection error of noisy image
According to the image composition characteristics of the side-scan sonar and the excellent performance of a wavelet transformation algorithm in image edge detection and image denoising, the edge detection method of the wavelet mode maximum value is applied to the seabed line detection of the side-scan sonar image by utilizing the local positioning characteristics and the multi-scale filtering characteristics of wavelet analysis, experiments prove that the feasibility of the method can be accurately positioned, the detection result has the maximum error of 0.41m and the root mean square error of +/-0.21 m compared with the multi-beam actually measured water depth, and besides, compared with other seabed line detection methods, the method has stronger inhibition capability on noise in the image, has higher stability and inhibits the influence of noise to a certain extent.
Although the present invention has been described in detail by way of preferred embodiments with reference to the accompanying drawings, the present invention is not limited thereto. Various equivalent modifications and substitutions may be made in the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and it is intended that all such modifications and substitutions be within the scope of the present invention/be within the scope of the present invention as defined by the appended claims.
Claims (1)
1. A submarine line detection method of a side-scan sonar image based on wavelet mode maxima is characterized by comprising the following steps:
s1, constructing a side-scan sonar imaging system: the side-scan sonar system comprises underwater towed fish, a connecting line and a deck unit, wherein in the course of navigation detection, transducer matrixes at two sides of the towed fish emit short acoustic pulses to the sea bottom at a certain inclination angle, sound waves are outwards diffused in a spherical wave mode, when the acoustic pulses meet the sea bottom or an object in water, the acoustic pulses are scattered, back-scattered echoes are returned to be received by the transducer according to an original propagation path and are transmitted to a sonar data processing unit upwards through a cable, the processing unit can display the gray level intensity of an image according to the intensity of the scattered echoes, the sonar transducer matrixes emit and receive the acoustic pulses to the sea bottom along with the movement of the towed fish, and the processor unit can display image information in a sequence, so that a two-dimensional side-scan sonar waterfall map is formed;
the two-dimensional side scan sonar waterfall diagram comprises a path line, a seabed line and a scanning line, wherein:
the navigation line is characterized by a motion track of the towed fish and is also a datum line for measuring the distance from the towed fish to a seabed target;
the sea bottom line is characterized by the height from the towed fish to the water bottom, reflects the fluctuation of the submarine topography right below the towed fish, and is a datum line for slope correction, radiation correction and target measurement; the portion of the image between the course line and the seabed line is called the "water column region", which is the first echo location where the sonar array reaches the seabed, and is displayed as a full black pixel on the sonar image because no echo is received before that location;
the scanning lines are components of the side scan sonar image, each Ping scanning line is orderly arranged to form a submarine sonar image, and the pixel gray level change of the image of the part is processed and displayed by the sonar processing unit according to the received signal intensity to reflect the target characteristics and the topographical features;
the back scattering intensity of the submarine target facing the sound wave irradiation surface is strong, the strong gray pixels are displayed on the sonar image, the reflection intensity of the back sound wave irradiation surface is weak or no reflection exists, and the weak gray black pixels are displayed;
s2, detecting a wavelet mode maximum value seabed line: the two-dimensional side scan sonar waterfall diagram analysis can obtain that an image-free area is formed in the central area of the waterfall diagram due to the occurrence of a 'water column area', the area is represented as a pixel with a gray value of 0 on the basis of echo intensity quantification because no echo signal arrives, the boundary of the water column area and an image area formed by scanning lines is represented as gray change from weak to strong on the image pixel, and the extreme point of the change at the boundary is the position of a sea bottom line of the side scan sonar image; the edge of the image is defined as the boundary of two image areas with different gray scales, and the gray scales of the edge areas change drastically, so that the sea bottom line of the side scan sonar image is detected by using an edge detection method of the image;
the detection based on the wavelet mode maximum subsea line comprises the following steps:
s211, inputting a side scan sonar image analyzed from an XTF file, and intercepting a proper size;
s212, setting a scale coefficient S of a wavelet, constructing a smoothing function, selecting a Gaussian function as the smoothing function in an experiment, defining the length and the amplitude value of a filter, calculating derivatives in the x and y directions of pixels in an image by using the smoothing function, performing energy normalization processing, and performing row and column convolution processing on the smoothed image to calculate the wavelet coefficient;
s213, traversing the smoothed original image, and obtaining the gradient direction and the amplitude angle of each pixel point;
s214, dividing an amplitude angle into 4 directions aiming at 8 field points of the pixel point, namely four directions of the horizontal, vertical, 45 degrees or 225 degrees, 135 degrees or 315 degrees of the pixel point, respectively solving local mode maximum values of the image along respective phase angle directions, recording gradient values if the local mode maximum values are the maximum values, and assigning the gradient to zero if the local mode maximum values are not the maximum values;
s215, solving the maximum amplitude, finding the gradient maximum value of all the mode maximum value points, and taking the maximum gradient value as a normalized reference value;
s216, setting a proper threshold, removing false edges caused by noise, and deleting edges which are larger than the retention of the threshold, otherwise, deleting edges which are smaller than the retention of the threshold, linking edge points in the image to obtain edge information of the image, and positioning the position of a sea-bottom line of the side-scan sonar image;
the edge detection method based on the wavelet mode maximum value comprises the following specific steps:
setting a two-dimensional image functionSelecting a suitable two-dimensional smoothing function>The function has good localization properties, i.e. the following conditions need to be met:
introduction of scale parametersSmoothing the original image under the scale parameter:
and recordThe horizontal and vertical wavelet functions at scale S are:
thus an imageThe two-dimensional wavelet transform at scale S can be expressed as:
i.e.Gradient vector of>The relation between the modulus value of the wavelet transformation and the modulus value number of the wavelet transformation is as follows:
the included angle between the gradient direction and the horizontal direction is the phase angle:
according to the algorithm principle of wavelet mode maximum, the mode maximum of calculating smooth function along gradient direction is obtained and is equivalent to the mode maximum of calculating wavelet transformation, and is recorded asUnit vector +.>And gradient vector->Are parallel and therefore are in the dimension +.>Lower, if dieAlong with->The local maximum value is obtained in the vertical direction, and the point +.>An edge point of the smooth image function;
determining edge points of the image by detecting mode maximum value points of two-dimensional wavelet transformation, and detecting boundary lines between a water column region and a scanning region in the side scan sonar image to realize detection of sea bottom lines;
s3, obtaining experimental results and comparative analysis: in order to verify the applicability of the wavelet mode maximum value-based image edge detection method to the side scan sonar image and the detection effect of the seabed line, the seabed line detection of the original image and the seabed line detection of the noise added image are respectively carried out; the experimental result can accurately locate the position of the seabed line, and the detection result of the comparison analysis comprises the maximum error and the root mean square error compared with the measured water depth of the multibeam, the noise suppression capability in the image, the stability of the detection result and the influence of noise suppression;
comparing the experimental process with a threshold value method and a seabed line detection method based on a log operator, and setting two parameters of a maximum error and a root mean square error as quantitative analysis basis, wherein:
the maximum error represents the maximum difference between the detection result and the actual value, and the smaller the maximum value is, the better the detection result is;
the root mean square error represents the deviation degree of the detection result and the actual value, and the smaller the root mean square error is, the closer the whole detection result is to the actual value;
in addition, aiming at the influence of various seabed noises in the actual working process of a side-scan sonar system, in the experimental process, analyzing the influence degree of each noise on a sonar image, adding speckle noise with the largest influence effect on the basis of an original side-scan sonar image so as to simulate the influence of the side-scan sonar image when the side-scan sonar image is influenced by the noise, comparing the seabed line detection results of each method on the noise-added side-scan sonar image again, and analyzing the detection effect of each method and the noise suppression capability;
the submarine line detection of the original image comprises the following steps:
taking sounding data of a 3DSS-DX sonar swept experimental area as an actual measurement water depth value, and using the sounding data as an error analysis reference of detection results of all methods:
the threshold method is used for setting the threshold K to 0.5 in experiments on the sonar image detection result, and the seabed line detection result is optimal; in the detection process, the noise point is taken as a seabed line, and the generated seabed line is subjected to filtering smoothing operation, so that the influence of interference factors is eliminated;
the log operator method has obvious phase difference in places with larger sea bottom height drop for different positioning of sea bottom line positions;
and (3) detecting a submarine line of the noisy image: the method comprises the following steps:
in the experimental process, gaussian white noise with the mean value of 0 and the noise standard deviation of 0.06 is added into an original side-scan sonar image, and the influence of reverberation noise on the sonar image is simulated to compare the inhibition capability of each seabed line detection method on noise factors; for the original side-scan sonar image and the sonar image added with noise, comparison between the two images obviously shows that after noise is added, the resolution of the side-scan sonar image is reduced, the boundary becomes fuzzy, the definition of the whole sonar image is lowered, and the readability is poor.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010256162A (en) * | 2009-04-24 | 2010-11-11 | Nec Corp | Analysis system for image sonar, analysis method for image sonar, and analysis program for image sonar |
| KR20130096854A (en) * | 2012-02-23 | 2013-09-02 | (주)지오투정보기술 | A system for generating seafloor 3-dimensional geospatial information using intensive filtering an edge of pipe line object and digital elevation value |
| CN108872997A (en) * | 2018-05-10 | 2018-11-23 | 国家海洋局第二海洋研究所 | A kind of seabed line detecting method based on side scan sonar data fusion and accurate processing |
| CN111443344A (en) * | 2020-04-07 | 2020-07-24 | 中国人民解放军军事科学院国防科技创新研究院 | Automatic extraction method and device for side-scan sonar sea bottom line |
| CN111476809A (en) * | 2020-04-08 | 2020-07-31 | 北京石油化工学院 | Side-scan sonar image target identification method |
| WO2021191846A1 (en) * | 2020-03-26 | 2021-09-30 | Fugro N.V. | Position correction using towed sensor |
| CN114627367A (en) * | 2022-05-17 | 2022-06-14 | 国家海洋局北海海洋技术保障中心 | Sea bottom line detection method for side-scan sonar image |
| WO2022166097A1 (en) * | 2021-02-08 | 2022-08-11 | 中国科学院声学研究所 | Side-scan sonar-based multi-mode imaging method for underwater target |
| CN114926377A (en) * | 2022-07-18 | 2022-08-19 | 自然资源部第一海洋研究所 | Modeling method of side-scan sonar gain compensation judgment model |
| CN115187666A (en) * | 2022-07-14 | 2022-10-14 | 武汉大学 | Deep learning and image processing combined side-scan sonar seabed elevation detection method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1110709C (en) * | 2001-09-13 | 2003-06-04 | 中国科学院声学研究所 | High resolution submarine microgeomorphy-measuring sounding side scan sonar system and measuring method |
| GB201016352D0 (en) * | 2010-09-29 | 2010-11-10 | Secr Defence | Integrated audio visual acoustic detection |
-
2023
- 2023-06-19 CN CN202310732063.5A patent/CN116930976B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010256162A (en) * | 2009-04-24 | 2010-11-11 | Nec Corp | Analysis system for image sonar, analysis method for image sonar, and analysis program for image sonar |
| KR20130096854A (en) * | 2012-02-23 | 2013-09-02 | (주)지오투정보기술 | A system for generating seafloor 3-dimensional geospatial information using intensive filtering an edge of pipe line object and digital elevation value |
| CN108872997A (en) * | 2018-05-10 | 2018-11-23 | 国家海洋局第二海洋研究所 | A kind of seabed line detecting method based on side scan sonar data fusion and accurate processing |
| WO2021191846A1 (en) * | 2020-03-26 | 2021-09-30 | Fugro N.V. | Position correction using towed sensor |
| CN111443344A (en) * | 2020-04-07 | 2020-07-24 | 中国人民解放军军事科学院国防科技创新研究院 | Automatic extraction method and device for side-scan sonar sea bottom line |
| CN111476809A (en) * | 2020-04-08 | 2020-07-31 | 北京石油化工学院 | Side-scan sonar image target identification method |
| WO2022166097A1 (en) * | 2021-02-08 | 2022-08-11 | 中国科学院声学研究所 | Side-scan sonar-based multi-mode imaging method for underwater target |
| CN114627367A (en) * | 2022-05-17 | 2022-06-14 | 国家海洋局北海海洋技术保障中心 | Sea bottom line detection method for side-scan sonar image |
| CN115187666A (en) * | 2022-07-14 | 2022-10-14 | 武汉大学 | Deep learning and image processing combined side-scan sonar seabed elevation detection method |
| CN114926377A (en) * | 2022-07-18 | 2022-08-19 | 自然资源部第一海洋研究所 | Modeling method of side-scan sonar gain compensation judgment model |
Non-Patent Citations (11)
| Title |
|---|
| Side-scan Sonar Image De-noising Based on Bidimensional Empirical Mode Decomposition and Non-local Means;Kun Zhao et al.;E3S Web of Conferences;20201231;全文 * |
| The principle of side scan sonar and its application in the detection of suspended submarine pipeline treatment;Li Jing;IOP Conference Series: Materials Science and Engineering;20181231;全文 * |
| 一种侧扫声呐检测力的评价方法;库安邦等;海洋技术学报;20170430;第36卷(第2期);全文 * |
| 侧扫声呐原始数据解析与可视化显示;赵昆;王方旗;丁继胜;隋海琛;许方正;;海岸工程;20200615(第02期);全文 * |
| 侧扫声呐图像海底线自动提取方法研究;赵建虎;王晓;张红梅;;武汉大学学报(信息科学版)(第12期);全文 * |
| 侧扫声呐条带数据处理及其无缝成图;王爱学;张红梅;王晓;尚晓东;;测绘地理信息(第01期);全文 * |
| 基于LOG算子的侧扫声呐海底线检测;张济博;潘国富;丁维凤;;海洋通报(第03期);全文 * |
| 基于SFS方法的侧扫声呐图像三维重构研究;覃珊珊;中国优秀硕士学位论文全文数据库 基础科学辑(第01期);第9-12, 19-20页 * |
| 基于侧扫声呐的海底管道可视化系统实现;库安邦等;水道港口;20191031;第40卷(第5期);全文 * |
| 基于小波分析的声呐图像边缘检测算法;赵晓燕;中国优秀硕士学位论文全文数据库 信息科技辑(第09期);第43-45页 * |
| 声呐探测技术在人工鱼礁建设中的应用;江丰标等;海岸工程;20230331;第42卷(第1期);全文 * |
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