CN113899349B - Sea wave parameter detection method, equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention provides a method, equipment and a storage medium for detecting sea wave parameters, wherein the method comprises the following steps: acquiring a video stream for shooting sea waves, intercepting a picture from the video stream by each preset frame number, and accumulating to obtain a plurality of pictures; carrying out graying treatment on a plurality of pictures to obtain a gray picture set; detecting and obtaining wave parameters of the waves based on the gray level picture set; the wave parameters include at least one of wave height, wave period, and wave direction. The sea wave parameter detection method, the sea wave parameter detection equipment and the storage medium provided by the embodiment of the invention can acquire the sea wave parameter with low cost, non-contact, good reliability and high performance, and have the advantages of higher robustness and higher calculation efficiency.
Description
Technical Field
The present invention relates to the field of parameter detection, and more particularly, to a method, apparatus, and storage medium for detecting sea wave parameters.
Background
Sea waves are common physical phenomena in the ocean, and have important scientific research significance and practical value for developing monitoring research on the sea waves. In the process of spreading sea waves to the coast, the sea waves are obviously affected by the submarine topography, the coastal boundary and the environmental flow (the coastal flow and the tide), have more complex evolution rules and faster space-time transformation than deep sea and open land frame sea areas, and are still immature in research and knowledge at present.
The research on sea waves mainly comprises the research on sea wave elements and relations among the elements, wherein the sea wave elements comprise effective wave periods, average wave heights, effective wave heights, wave directions, wave steepness and the like, the detection on offshore sea wave elements is mainly carried out mainly by buoy observation and manual observation is carried out as assistance, and radar observation is actively promoted in recent years. The buoy is used for observing 'points', high-density deployment is required for accurate sea wave measurement of complex terrains of an estuary, and the operation and maintenance costs are high; the manual observation is to estimate the sea wave information by means of visual observation of experienced predictors, so that the requirement on personnel is high, and the prediction frequency and precision are difficult to guarantee. The ground wave radar can realize large-area and long-time automatic measurement, but radar measurement equipment is expensive, microwave pulse and echo propagation are influenced by ionosphere, stratosphere and sea-air interfaces, the interference of the data quality on air change is obvious at any time, and the measurement accuracy depends on inversion of signals. The shore-based monitoring video used for measuring the sea wave elements has the advantages of non-contact, low cost and space-time continuity, and can effectively make up the defect of the buoy and radar measurement technology on direction spectrum observation.
At present, there are some researches on detecting sea wave elements by using visual data at home and abroad, and the methods are mainly divided into two types. One is a photogrammetry-based method, and the other is an image/video feature-based detection method, including statistical features, transform domain features, texture features, and the like. The wave element analysis based on stereoscopic vision is mainly based on video images, wave element detection is carried out through a stereoscopic vision system, wave high resolution precision is high, a model is complex, model parameters are required to be reset for element detection in different environment sea areas, robustness is poor, calculation efficiency is low, and practical application cannot be well met. The wave factor detection based on the video mainly detects the motion direction and wave height level of the wave, cannot obtain the wave height value with high precision, has higher complexity of a model algorithm for wave direction detection and lower calculation efficiency; based on the wave level threshold model of the image features, the imperfection of the artificial design features leads to instability of wave height level detection, and fine detection of wave height values cannot be achieved.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a method, apparatus and storage medium for detecting sea wave parameters, which overcome or at least partially solve the above problems.
According to a first aspect of an embodiment of the present invention, there is provided a method for detecting sea wave parameters, the method comprising: acquiring a video stream for shooting sea waves, intercepting a picture from the video stream by each preset frame number, and accumulating to obtain a plurality of pictures;
carrying out graying treatment on a plurality of pictures to obtain a gray picture set;
detecting and obtaining wave parameters of the waves based on the gray level picture set; the wave parameters include at least one of wave height, wave period, and wave direction.
Preferably, the sea wave parameter is wave height; correspondingly, detecting the sea wave parameters of the sea wave based on the gray level picture set comprises the following steps:
calculating the roughness of the gray picture set;
and obtaining the wave height of the ocean based on the roughness of the gray level picture set and a fitting function between the video image roughness and the buoy wave height.
Preferably, calculating the roughness of the gray picture set includes:
counting the gray distribution of the pictures in the gray picture set;
after the abnormal value is removed, a fractal dimension is obtained through a gray level statistical method, and the fractal dimension is used as the roughness.
Preferably, the fractal dimension is obtained by gray statistics, comprising:
given the scale r, the mean of the increments in the x and y directions is calculated:
wherein I (x, y) represents the gray value of the pixel at (x, y), N is the size of the selected region;
calculate the mean value of the total increment E (r) =e x (r)+E y (r)；
According to E (r) =Cr H Log E (r) =h+log (r) + log (C) is obtained from the two sides of the square, wherein (r is greater than or equal to 1), and the square is fitted in the range of r by a least square method to obtain an estimated value of the H parameter, and then the fractal dimension D is obtained.
Preferably, the fractal dimension is obtained by gray statistics, comprising:
the basic properties of fractional brownian motion:
Var(|B(t 2 )-B(t 1 )|)～|t2-t1| 2H (3)
converting the formula (3) into the following formula:
E(|dI Δn |)|Δn| -H ＝E(dI Δn＝1 ) (4)
wherein E (|dI) Δn I) is the expected value of the gray level variation within the area range Δn;
taking the logarithm of the two sides of the formula (4) to obtain the following formula:
for a certain image block f (i, j), four new image blocks gm (i, j) are defined, m=1, 2,3,5;
g m (i,j)＝∑ (k,I)eU |f(i,j)-f(k,l)| (6)
wherein ,
for each m, calculating the histogram of the image block gm (i, j), wherein the specific algorithm is that the number of straight square blocks with the height larger than zero in the histogram is recorded as n; summing the heights of the squares; dividing the sum of the heights of the square blocks by the number n to obtain a histogram mean E (|dI) Δn I), then substituting the same into the formula (1) to have corresponding Hm; d= (h1+h2+h3)/3, where D is the fractal dimension.
Preferably, the wave parameter is a wave period; correspondingly, detecting the sea wave parameters of the sea wave based on the gray level picture set comprises the following steps:
calculating a square sum error R of the difference between each frame in the gray image set and the first frame to obtain a square sum error set Rs;
acquiring a spacing frame number dNum between pictures corresponding to a smaller value in the square sum error set Rs, and acquiring the total frame number of one wave period through the spacing frame number dNum;
dividing the total frame number by the frame number per second of a camera for shooting the video stream to obtain a wave period.
Preferably, the sea wave parameter is wave direction; correspondingly, detecting the sea wave parameters of the sea wave based on the gray level picture set comprises the following steps:
dividing the gray level pictures in the gray level picture set to obtain a plurality of rectangular blocks; a binarization threshold value is obtained for each rectangular block, binarization processing is carried out, and a binarization result is integrated into a binarization picture;
performing corrosion, denoising and expansion treatment on the binarized picture, and dividing the binarized image to obtain texture blocks so as to obtain a texture block set;
calculating to obtain an angle set angles and a size set sizes for each texture block in the texture block set;
the wave direction is obtained by weighting the sizes/sum (sizes) as the weight of each element in the angles set.
Preferably, after detecting the wave parameters of the wave, the method further comprises:
and estimating errors through a time series neural network according to the time series data of the wave height, the wave direction and/or the wave period, and realizing error correction of the wave height, the wave direction and/or the wave period.
According to a second aspect of the embodiments of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of detecting sea wave parameters as provided by any one of the various possible implementations of the first aspect when the program is executed.
According to a third aspect of embodiments of the present invention, there is provided a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of sea wave parameter detection as provided by any of the various possible implementations of the first aspect.
According to the ocean wave parameter detection method, the ocean wave parameter detection equipment and the storage medium, through obtaining the video stream for shooting ocean waves, each preset frame number is used for capturing one picture from the video stream, and a plurality of pictures are obtained in an accumulated mode; carrying out graying treatment on a plurality of pictures to obtain a gray picture set; based on the gray level picture set, the sea wave parameters of the sea waves are obtained through detection, the sea wave parameters are obtained with low cost, non-contact, good reliability and high performance, the robustness is high, and the calculation efficiency is high.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is apparent that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 is a schematic flow chart of a method for detecting sea wave parameters according to an embodiment of the present invention;
FIG. 2 is a schematic view of sea waves according to an embodiment of the present invention;
FIG. 3 is a diagram of a binary wave direction result of an ocean wave image and an angle statistics diagram of each segment according to an embodiment of the present invention;
fig. 4 is a sequence chart of quantized values of sea wave image differences according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In view of the foregoing problems in the prior art, an embodiment of the present invention provides a method for detecting sea wave parameters, referring to fig. 1, the method includes, but is not limited to, the following steps:
Specifically, the video stream of the near-case sea wave can be obtained through the shooting of the network camera. And intercepting one picture in the video stream every m frames, and accumulating to obtain n frames of pictures.
And 102, carrying out graying treatment on a plurality of pictures to obtain a gray picture set.
Wherein, referring to fig. 2, the original picture set is grayed.
In particular, different detection methods may be employed for different detected sea wave parameters.
Firstly, the sea wave parameter is wave height; correspondingly, detecting the sea wave parameters of the sea wave based on the gray level picture set, including:
calculating the roughness of the gray picture set;
and obtaining the wave height of the ocean based on the roughness of the gray level picture set and a fitting function between the video image roughness and the buoy wave height.
The roughness is calculated specifically by the following way:
counting the gray distribution of the pictures in the gray picture set;
after the abnormal value is removed, a fractal dimension is obtained through a gray level statistical method, and the fractal dimension is used as the roughness.
Specifically, according to the gray distribution of the sea wave picture statistics picture, after abnormal values are removed, the fractal dimension D is obtained through a gray statistics method, and the fractal dimension is equal to the roughness of the gray picture.
And further, the roughness can be calculated in two ways:
mode one, calculated by calculating the increment of gray scale in the horizontal and vertical directions, the specific algorithm is as follows:
(1) Given the scale r, the mean of the increments in the x and y directions is calculated:
wherein I (x, y) represents the gray value of the pixel at (x, y), N is the size of the selected region;
(2) Calculate the mean value of the total increment E (r) =e x (r)+E y (r)；
(3) According to E (r) =Cr H Log E (r) =h+log (r) + log (C) is obtained from the two sides of the square, wherein (r is greater than or equal to 1), and the square is fitted in the range of r by a least square method to obtain an estimated value of the H parameter, and then the fractal dimension D is obtained.
The method mainly only needs to obtain the increment of the image gray level in the x direction and the y direction to finish the calculation of the score dimension, and is simpler and less in calculation amount. In one embodiment, r should not be too large because the resolution of the processed image is 140×140. In this embodiment, the incremental mean values of the images when r=1, 2,3, and 5 are calculated respectively, and then substituted into step (3) to obtain a double-log linear relationship between the mean value and the scale r, r=1 and r=2, r=2 and r=3, and r=3 and r=5 are taken to obtain three groups of H values, which are 0.63,0.50 and 0.34 respectively, and the mean values thereof are calculated, and then substituted into d=3-H to obtain the fractal dimension d=2.51 of the image.
The definition method of the mode II and gray statistics comprises the following specific algorithm:
Var(|B(t 2 )-B(t 1 )|)～|t2-t1| 2H (3)
the above formula may also be written as follows:
E(|dI Δn |)|Δn| -H ＝E(dI Δn＝1 ) (4)
E(|dI Δn i) is the expected value of the gray level variation within the area range deltan.
The same logarithm is taken for the two sides of the upper part:
for a certain image block f (i, j), we define four new image blocks gm (i, j) again, m=1, 2,3,5.
g m (i,j)＝∑ (k,I)eU |f(i,j)-f(k,l)| (6)
wherein 
For each m, calculating the histogram of the image block gm (i, j), wherein the specific algorithm is that the number of the straight squares with the height larger than zero in the histogram is recorded as n, then the sum of the heights of the straight squares is calculated, and finally the sum of the heights of the straight squares is divided by the number n to obtain the average value E (|dI) of the histogram Δn I) and then substituted into equation (1) with the corresponding Hm. Finally d= (h1+h2+h3)/3.
Then, after the roughness is obtained through calculation, performing function fitting according to the roughness extracted by the picture and the actual wave height to obtain a function relation wave h=a×exp (b×d) +c of the picture roughness and the wave height, and obtaining the wave height wave h according to the function relation of the roughness and the wave height through subsequent real-time wave video calculation.
Secondly, if the wave parameters are wave periods; correspondingly, detecting the sea wave parameters for obtaining the sea waves based on the gray level picture set comprises the following steps:
calculating a square sum error R of the difference between each frame in the gray image set and the first frame to obtain a square sum error set Rs;
acquiring a spacing frame number dNum between pictures corresponding to a smaller value in the square sum error set Rs, and acquiring the total frame number of one wave period through the spacing frame number dNum;
dividing the total frame number by the frame number per second of a camera for shooting the video stream to obtain a wave period.
Specifically, the wave period can be calculated as follows: and quantifying the difference between gray scale images. The sum of squares error R of the difference between each frame and the first frame is calculated by the gray image set, the difference between each frame and the first frame in the image set is quantized, as shown in fig. 4, the interval frame number dNum between smaller values in the set is obtained by the sum of squares error set Rs, the total frame number of one wave period is obtained by m×dnum, and the wave period is obtained by dividing the total frame number by the frame number of each second of the camera. In one particular embodiment, wavet= 4.166s.
Finally, if the sea wave parameter is wave direction; correspondingly, detecting the sea wave parameters for obtaining the sea waves based on the gray level picture set comprises the following steps:
step 1, dividing the gray level pictures in the gray level picture set to obtain a plurality of rectangular blocks; a binarization threshold value is obtained for each rectangular block, binarization processing is carried out, and a binarization result is integrated into a binarization picture;
specifically, calculating an image texture angle through a binarized image to obtain a wave direction, aiming at different positions of a gray picture, aiming at different brightness, so that the texture of the binarized picture is not obvious, dividing blocks are large, dividing the gray picture into blocks and binarizing the blocks, and dividing the gray picture into a plurality of rectangular blocks, for example, 12 rectangular blocks in total, 3 x 4; and each rectangular block is respectively subjected to binarization threshold value and binarization, and the binarization results are combined to obtain an integral binarization picture.
Step 2, performing corrosion, denoising and expansion treatment on the binarized picture, and dividing the binarized picture to obtain texture blocks so as to obtain a texture block set;
specifically, for the binarized picture, multi-row wave texture connection in the binarization process is avoided through corrosion, the situation that texture blocks are extremely small after segmentation processing is removed through noise removal, the situation that the same row of waves are small to cause fracture in the binarization process is avoided through expansion, and the binarized image is segmented through an eight-way communication method to obtain each wave texture block, as shown in fig. 3.
Step 3, calculating to obtain an angle set angles and a size set sizes for each texture block in the texture block set; such as fig. 3.
And 4, taking the sizes/sum (sizes) as the weight of each element in the angles set, and weighting to obtain the wave direction.
Specifically, regarding the texture angle set angles and the texture size set sizes, weighting the sizes/sum (sizes) as the weight of each element in the angles set to obtain the wave direction. In a specific embodiment, waved= -70.47 °, waved=19.53 °. Meanwhile, the final normalized wave direction wave D=wave D-alpha can be obtained according to the included angle alpha between the camera direction and the north clockwise direction.
Based on the foregoing embodiment, as an alternative embodiment, after detecting the ocean wave parameters of the ocean wave, the method further includes: and estimating errors through a time series neural network according to the time series data of the wave height, the wave direction and/or the wave period, and realizing error correction of the wave height, the wave direction and/or the wave period.
In summary, the embodiment of the invention provides an automatic detection method for the wave parameters of the offshore wave video, which is used for quantitatively calculating and obtaining wave elements by capturing video images from the offshore monitoring video according to frames, and mainly comprises an effective wave period, an effective wave height, a wave direction and the like. The effective wave period can be seen to have certain periodicity through the video, waves have certain repeatability in a short time, when the waves in the next row are propagated to the waves in the previous row, two frames of images are subjected to difference quantization processing, the frame interval with the smallest difference is obtained, and the wave period can be obtained through the time length between the two frames. The wave direction is achieved by converting a frame image in a video into a gray level image, carrying out binarization on the frame image in a block local mode, extracting image shadows, improving shadow features through a series of image processing algorithms (open operation, closed operation, expansion corrosion and the like), facilitating extraction of wave shadow surfaces, and finally obtaining wave direction results through statistics of shadow angles. The wave height is obtained by constructing a differential image dataset containing static information and dynamic information of the sea wave static image dataset, calculating the roughness of a gray level picture set, and obtaining the wave height through a fitting function relation between a video image roughness concept and buoy wave height established by a large amount of data in the earlier stage and a wave height sequence based on a time sequence. And finally, estimating errors through a time sequence neural network according to the time sequence data of wave height, wave direction and wave period, and realizing the correction of the wave height, wave direction and wave period errors. Thereby automatically detecting wave height, wave direction and wave period.
An embodiment of the present invention provides an electronic device, as shown in fig. 5, including: a processor (processor) 501, a communication interface (Communications Interface) 502, a memory (memory) 503 and a communication bus 504, wherein the processor 501, the communication interface 502, and the memory 503 communicate with each other via the communication bus 504. The processor 501 may call a computer program on the memory 503 and executable on the processor 501 to perform the method for detecting sea wave parameters provided in the above embodiments, for example, including: acquiring a video stream for shooting sea waves, intercepting a picture from the video stream by each preset frame number, and accumulating to obtain a plurality of pictures; carrying out graying treatment on a plurality of pictures to obtain a gray picture set; detecting and obtaining wave parameters of the waves based on the gray level picture set; the wave parameters include at least one of wave height, wave period, and wave direction.
The embodiments of the present invention also provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the method for detecting sea wave parameters provided in the above embodiments, for example, including: acquiring a video stream for shooting sea waves, intercepting a picture from the video stream by each preset frame number, and accumulating to obtain a plurality of pictures; carrying out graying treatment on a plurality of pictures to obtain a gray picture set; detecting and obtaining wave parameters of the waves based on the gray level picture set; the wave parameters include at least one of wave height, wave period, and wave direction.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (methods), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (6)
1. The sea wave parameter detection method is characterized by comprising the following steps of:
acquiring a video stream for shooting sea waves, intercepting a picture from the video stream by each preset frame number, and accumulating to obtain a plurality of pictures;
carrying out graying treatment on a plurality of pictures to obtain a gray picture set;
detecting and obtaining wave parameters of the waves based on the gray level picture set; the wave parameters comprise at least one of wave height, wave period and wave direction;
the wave parameter is wave height; correspondingly, detecting the sea wave parameters of the sea wave based on the gray level picture set comprises the following steps:
calculating the roughness of the gray picture set;
obtaining the wave height of the sea wave based on the roughness of the gray picture set and a fitting function between the video image roughness and the buoy wave height;
calculating the thickness of the gray-scale picture set
Roughness, including:
counting the gray distribution of the pictures in the gray picture set;
after removing abnormal values, obtaining fractal dimension through a gray level statistical method, and taking the fractal dimension as the roughness;
obtaining fractal dimension by gray level statistics method, comprising:
given the scale r, the mean of the increments in the x and y directions is calculated:
wherein I (x, y) represents the gray value of the pixel at (x, y), N is the size of the selected region;
calculate the mean value of the total increment E (r) =e x (r)+E y (r)；
According to E (r) =Cr H Taking logarithms on two sides of the fractal dimension D, wherein log E (r) =H+log (r) +log (C), wherein (r is more than or equal to 1), fitting the log by a least square method in the range of r to obtain an estimated value of an H parameter, and further obtaining the fractal dimension D;
or
Obtaining fractal dimension by gray level statistics method, comprising:
the basic properties of fractional brownian motion:
Var(|B(t 2 )-B(t 1 )|)～|t2-t1| 2H (3)
converting the formula (3) into the following formula:
E(|dI Δn |)|Δn| -H ＝E(dI Δn＝1 ) (4)
wherein E (|dI) Δn I) is the expected value of the gray level variation within the area range Δn;
taking the logarithm of the two sides of the formula (4) to obtain the following formula:
for a certain image block f (i, j), four new image blocks gm (i, j) are defined, m=1, 2,3,5; g m (i ,j)＝∑ (k,I)eU |f(i ,j)-f(k ,l)| (6)
wherein ,
for each m, calculating the histogram of the image block gm (i, j), wherein the specific algorithm is that the number of straight square blocks with the height larger than zero in the histogram is recorded as n; summing the heights of the squares; dividing sum of the heights of the square blocks by sumThe number n of the obtained values is the histogram mean E (|dI) Δn I), then substituting the same into the formula (1) to have corresponding Hm; d=
(h1+h2+h3)/3, wherein D is a fractal dimension.
2. A method according to claim 1, wherein the wave parameter is a wave period; correspondingly, detecting the sea wave parameters of the sea wave based on the gray level picture set comprises the following steps:
calculating a square sum error R of each frame of the gray picture set and the first frame difference to obtain a square sum error set Rs;
acquiring a spacing frame number dNum between pictures corresponding to a smaller value in the square sum error set Rs, and acquiring the total frame number of one wave period through the spacing frame number dNum;
dividing the total frame number by the frame number per second of a camera for shooting the video stream to obtain a wave period.
3. A method according to claim 1, wherein the sea wave parameter is wave direction; correspondingly, detecting the sea wave parameters of the sea wave based on the gray level picture set comprises the following steps:
dividing the gray level pictures in the gray level picture set to obtain a plurality of rectangular blocks; a binarization threshold value is obtained for each rectangular block, binarization processing is carried out, and a binarization result is integrated into a binarization picture; performing corrosion, denoising and expansion treatment on the binarized picture, and dividing the binarized image to obtain texture blocks so as to obtain a texture block set;
calculating to obtain an angle set angles and a size set sizes for each texture block in the texture block set;
the sizes/sum (sizes) is taken as the weight of each element in the angles set
Heavy, weighted to obtain wave direction.
4. The method according to claim 1, further comprising, after detecting the wave parameters of the wave obtained:
and estimating errors through a time series neural network according to the time series data of the wave height, the wave direction and/or the wave period, and realizing error correction of the wave height, the wave direction and/or the wave period.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the wave parameter detection method according to any one of claims 1 to 4 when the program is executed.
6. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the wave parameter detection method according to any of claims 1 to 4.
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