CN113899349A - Sea wave parameter detection method, equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention provides a sea wave parameter detection method, equipment and a storage medium, 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 processing on a plurality of pictures to obtain a grayscale picture set; detecting and obtaining sea wave parameters of the sea 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 sea wave parameter detection storage medium provided by the embodiment of the invention have the advantages of low cost, non-contact, good reliability, high performance, higher robustness and higher calculation efficiency.
Description
Technical Field
The invention relates to the field of parameter detection, in particular to a method, equipment and a storage medium for detecting sea wave parameters.
Background
The ocean wave is a common physical phenomenon in the ocean, and has important scientific research significance and practical value for the ocean wave to carry out monitoring research. In the process of spreading sea waves to the near shore, sea waves are obviously affected by submarine topography, shore boundary and environmental flows (near shore flows and tidal currents), have more complex evolution rules and faster space-time transformation than deep sea and open land frame sea areas, and are immature in research and knowledge at present.
The research on the sea waves mainly comprises the research on sea wave elements and the relation among the elements, wherein the sea wave elements comprise effective wave period, average wave height, effective wave height, wave direction, wave steepness and the like, at present, the detection on the sea wave elements near the shore mainly takes buoy observation as main part and manual observation as auxiliary part, and radar observation is actively promoted in recent years. The buoy observes points, needs high-density deployment for accurate ocean wave measurement of complex terrains of estuary and has high operation and maintenance cost; the manual observation is to estimate the sea wave information in a visual inspection mode of an experienced forecaster, the requirement on the personnel is high, and the prediction frequency and the prediction precision are difficult to guarantee. The ground wave radar can realize large-area and long-time automatic measurement, but radar measuring equipment is expensive, microwave pulse and echo propagation are influenced by an ionosphere, a stratosphere and a sea-air interface, data quality is obviously interfered by space variation at any time, and measurement accuracy depends on inversion of signals. The shore-based monitoring video is used for sea wave element measurement, has the advantages of non-contact, low cost and space-time continuity, and can effectively make up the defects of the buoy and radar measurement technology in direction spectrum observation.
At present, some researches on detection of sea wave elements by using visual data at home and abroad are available, and the methods mainly include 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 ocean wave element analysis based on the stereoscopic vision is mostly based on video images, ocean wave element detection is carried out through a stereoscopic vision system, the wave height analysis precision is high, the model is complex, model parameters need to be reset for element detection of different environment sea areas, the robustness is poor, the calculation efficiency is low, and the practical application cannot be well met. The video-based sea wave element detection mainly detects the motion direction and the wave height level of sea waves, cannot acquire the wave height value with high precision, and has higher complexity and lower calculation efficiency of a model algorithm for wave direction detection; the wave height level detection is unstable due to incompleteness of manual design features of a wave level threshold model based on image features, 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, an apparatus, and a storage medium for detecting wave parameters, which overcome the above problems or at least partially solve the above problems.
According to a first aspect of the embodiments of the present invention, there is provided a method for detecting sea wave parameters, the method 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 processing on a plurality of pictures to obtain a grayscale picture set;
detecting and obtaining sea wave parameters of the sea 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 wave parameter is wave height; correspondingly, based on the grayscale picture set, sea wave parameters of the sea waves are detected and obtained, and the method comprises the following steps:
calculating the roughness of the gray level 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 wave height of the buoy.
Preferably, calculating the roughness of the grayscale picture set includes:
counting the gray distribution of the pictures in the gray picture set;
and after removing the abnormal value, obtaining a fractal dimension through a gray level statistical method, and taking the fractal dimension as the roughness.
Preferably, the fractal dimension is obtained by a gray scale statistical method, including:
given a scale r, the mean of the increments in the x and y directions are calculated, respectively:
where I (x, y) represents the gray scale value of the pixel at (x, y) and N is the size of the selected region;
calculating the mean value of the total increase E (r) ═ Ex(r)+Ey(r)；
According to the formula E (r) ═ CrHLogarithms are taken on two sides of the fractal dimension D, wherein log E (r) ═ H × log (r) + log (C) is provided, and the estimated value of the H parameter is obtained by least square fitting in the range of r, and then the fractal dimension D is obtained.
Preferably, the fractal dimension is obtained by a gray scale statistical method, including:
from the fundamental nature of fractional brownian motion:
Var(|B(t2)-B(t1)|)～|t2-t1|2H (3)
converting equation (3) to the following equation:
E(|dIΔn|)|Δn|-H＝E(dIΔn＝1) (4)
wherein, E (| dI)Δn|) is the expected value of the gray scale change within the region range Δ n;
logarithms are taken on both sides of the above formula (4) to obtain the following formula:
for a certain image block f (i, j), four new image blocks gm (i, j) are defined, where m is 1,2,3, 5;
gm(i,j)＝∑(k,I)eU|f(i,j)-f(k,l)| (6)
wherein ,
for each m, a histogram of the image block gm (i, j) is calculated, specificallyRecording the number of the histogram blocks with the height larger than zero in the histogram as n; summing the heights of the squares, sum; dividing the sum of the heights of the histogram blocks by the number n of the histogram blocks to obtain a histogram mean value E (| dI)Δn|), then substituting the compound with the formula (1) to obtain the corresponding Hm; d ═ H1+ H2+ H3)/3, where D is the fractal dimension.
Preferably, the wave parameter is a wave period; correspondingly, based on the grayscale picture set, sea wave parameters of the sea waves are detected and obtained, and the method comprises the following steps:
calculating the square sum error R of the difference between each frame in the gray level image set and the first frame to obtain a square sum error set Rs;
acquiring interval frame numbers dNum between pictures corresponding to a smaller value in the sum of squares error set Rs, and acquiring the total frame number of a wave period through the interval frame numbers dNum;
and dividing the total frame number by the frame number per second of a camera used for shooting the video stream to obtain the wave period.
Preferably, the wave parameter is wave direction; correspondingly, based on the grayscale picture set, sea wave parameters of the sea waves are detected and obtained, and the method comprises the following steps:
carrying out picture segmentation on the gray level pictures in the gray level picture set to obtain a plurality of rectangular blocks; obtaining a binarization threshold value for each rectangular block, carrying out binarization processing, and integrating a binarization result into a binarization picture;
carrying out corrosion, denoising and expansion processing on the binarization image, and segmenting the binarization image to obtain each texture block so as to obtain a texture block set;
calculating and obtaining an angle set and a size set sizes for each texture block in the texture block set;
weighting the sizes/sum (sizes) as the weight of each element in the angles set to obtain the wave direction.
Preferably, after the wave parameters of the sea waves are detected, the method further comprises the following steps:
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 the 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, including a memory, a processor, and a computer program stored in the memory and being executable on the processor, where the processor executes the computer program to implement the wave parameter detection method according to any one of the possible implementations of the first aspect.
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 wave parameter detection method as provided in any one of the various possible implementations of the first aspect.
According to the sea wave parameter detection method, the equipment and the storage medium provided by the embodiment of the invention, one picture is intercepted from a video stream by acquiring the video stream for shooting sea waves, and a plurality of pictures are obtained through accumulation; carrying out graying processing on a plurality of pictures to obtain a grayscale picture set; based on the gray level picture set, sea wave parameters of the sea waves are obtained through detection, sea surface wave parameters are obtained with low cost, non-contact, good reliability and high performance, robustness is high, and 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 obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from these without inventive effort.
Fig. 1 is a schematic flow chart of a method for detecting wave parameters according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of sea surface waves provided by an embodiment of the present invention;
fig. 3 is a wave direction result graph of sea wave image binarization and an angle statistical graph of each segmentation block provided by the embodiment of the invention;
fig. 4 is a sequence diagram of a difference quantization value of a sea wave image according to an embodiment of the present invention;
fig. 5 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In view of the above problems in the prior art, an embodiment of the present invention provides a method for detecting wave parameters, referring to fig. 1, the method includes, but is not limited to, the following steps:
Specifically, a video stream of the near-case sea waves can be obtained through shooting by a 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 processing on the plurality of pictures to obtain a grayscale picture set.
Wherein the original picture set is grayed out, see fig. 2.
103, detecting and obtaining sea wave parameters of the sea waves based on the gray level picture set; the wave parameters include at least one of wave height, wave period, and wave direction.
In particular, different detection methods may be employed for different detected wave parameters.
Firstly, the wave parameter is wave height; correspondingly, based on the grayscale picture set, detecting and obtaining the sea wave parameters of the sea waves, including:
calculating the roughness of the gray level 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 wave height of the buoy.
Specifically, the roughness may be calculated as follows:
counting the gray distribution of the pictures in the gray picture set;
and after removing the abnormal value, obtaining a fractal dimension through a gray level statistical method, and taking the fractal dimension as the roughness.
Specifically, the gray distribution of the picture is counted according to the sea wave picture, after an abnormal value is removed, a fractal dimension D is obtained through a gray counting method, and the fractal dimension is equal to the roughness of the gray picture.
And further, roughness can be calculated in two ways:
the method I is calculated by calculating the gray scale increment in the horizontal direction and the vertical direction, and the specific algorithm is as follows:
(1) given a scale r, the mean of the increments in the x and y directions are calculated, respectively:
where I (x, y) represents the gray scale value of the pixel at (x, y) and N is the size of the selected region;
(2) calculating the mean value of the total increase E (r) ═ Ex(r)+Ey(r)；
(3) According to the formula E (r) ═ CrHLogarithms are taken on two sides of the fractal dimension D, wherein log E (r) ═ H × log (r) + log (C) is provided, and the estimated value of the H parameter is obtained by least square fitting in the range of r, and then the fractal dimension D is obtained.
The method can complete the calculation of the fractional dimension by only calculating the image gray scale increment in the x direction and the y direction, and is simple and less in calculation amount. In one embodiment, r is not too large because the resolution of the processed image is 140 × 140. This embodiment calculates the average values of image increments when r is 1,2,3, and 5, respectively, then substitutes in step (3) to obtain the log-log linear relationship between the average value and the scale r, and takes r 1 and r 2, r 2 and r 3, and r 3 and r 5 to obtain three sets of H values, 0.63,0.50, and 0.34, respectively, to obtain the average values, and substitutes D3-H to obtain the fractal dimension D of the image 2.51.
The second mode and the definition method of gray statistics have the following specific algorithm that the basic property of fractional Brownian motion is as follows:
Var(|B(t2)-B(t1)|)～|t2-t1|2H (3)
the above formula can also be written as follows:
E(|dIΔn|)|Δn|-H＝E(dIΔn＝1) (4)
E(|dIΔn|) is an expected value of the gray scale change within the area range Δ n.
The logarithm is taken at both sides of the formula:
for a certain image block f (i, j), we further define four new corresponding image blocks gm (i, j), where m is 1,2,3, 5.
gm(i,j)＝∑(k,I)eU|f(i,j)-f(k,l)| (6)
wherein 
For each m, calculating a histogram of the image block gm (i, j), wherein the specific algorithm is to record the number of the square blocks with the height greater than zero in the histogram as n, then calculate the sum of the heights of the square blocks, and finally divide the sum of the heights of the square blocks by the number n to obtain a histogram mean value E (| dI j)ΔnAnd | h) and then substituted into formula (1) with the corresponding Hm. And finally D ═ (H1+ H2+ H3)/3.
Then, after the roughness is obtained through calculation, function fitting is carried out according to the roughness extracted from the picture and the actual wave height to obtain a functional relation WaveH (a x exp (b x D) + c) of the picture roughness and the wave height, the roughness is obtained through subsequent real-time wave video calculation, and the wave height WaveH can be obtained according to the functional relation of the roughness and the wave height.
Secondly, if the wave parameter is a wave period; correspondingly, based on the gray level picture set, sea wave parameters of the sea waves are detected and obtained, and the method comprises the following steps:
calculating the square sum error R of the difference between each frame in the gray level image set and the first frame to obtain a square sum error set Rs;
acquiring interval frame numbers dNum between pictures corresponding to a smaller value in the sum of squares error set Rs, and acquiring the total frame number of a wave period through the interval frame numbers dNum;
and dividing the total frame number by the frame number per second of a camera used for shooting the video stream to obtain the wave period.
Specifically, the wave period can be calculated as follows: and quantizing the difference between the gray level images. Calculating the square sum error R of the difference between each frame and the first frame through the gray image set, quantizing the difference between each frame and the first frame in the image set, as shown in FIG. 4, acquiring the number of interval frames dNum between smaller values in the set through the square sum error set Rs, acquiring the total number of frames of a wave period through m × dNum, and dividing the total number of frames per second of the camera to obtain the wave period. In one particular embodiment, wave t 4.166 s.
Finally, if the wave parameter is wave direction; correspondingly, based on the gray level picture set, sea wave parameters of the sea waves are detected and obtained, and the method comprises the following steps:
step 1, carrying out picture segmentation on the gray level pictures in the gray level picture set to obtain a plurality of rectangular blocks; obtaining a binarization threshold value for each rectangular block, carrying out binarization processing, and integrating a binarization result into a binarization picture;
specifically, the wave direction is obtained by calculating the image texture angle through the binary image, aiming at different positions of the gray level image, the brightness is different, so that the texture of the binary image is not obvious, the segmentation block is large, the gray level image is segmented and binarized, and the gray level image is segmented into a plurality of rectangular blocks, for example, 12 rectangular blocks in total of 3 × 4; and (4) respectively solving a binarization threshold value for each rectangular block, carrying out binarization, and combining binarization results to obtain an overall binarization picture.
Step 2, carrying out corrosion, denoising and expansion processing on the binary image, and segmenting the binary image to obtain each texture block so as to obtain a texture block set;
specifically, for a binarized image, multi-row wave texture connection in the binarized process is avoided through corrosion, the condition that a segmented texture block is extremely small is removed through denoising, the condition that the same row of waves are small and fracture is caused in the binarized process is avoided through expansion, and the binarized image is segmented through an eight-way communication method to obtain each texture block of the waves, as shown in fig. 3.
Step 3, calculating and obtaining an angle set and a size set for each texture block in the texture block set; such as fig. 3.
And 4, weighting by using sizes/sum (sizes) as the weight of each element in the angles set to obtain the wave direction.
Specifically, for the texture angle set angles and the texture size set sizes, the sizes/sum (sizes) is used as the weight of each element in the angles set, and the wave direction can be obtained by weighting. In one specific embodiment, WaveD ═ 70.47 °, WaveD ═ 19.53 °. And meanwhile, the final normalized wave direction WaveD-alpha can be obtained according to the clockwise included angle alpha between the direction of the camera and the positive north.
Based on the content of the foregoing embodiment, as an optional embodiment, after the detecting and obtaining the wave parameter of the 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 the 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 sea wave parameters of a near-shore sea wave video, which captures video images from a near-shore monitoring video according to frames, and quantitatively calculates and obtains sea wave elements mainly including a period of an effective wave, a height of the effective wave, a direction of the wave and the like. The effective wave period can be seen through a video, the waves have certain periodicity, the waves have certain repeatability in a short time, when the waves in the next row are transmitted to the waves in the previous row, the difference between two frames of images is quantized, the frame interval with the minimum difference is obtained, and the wave period can be obtained through the time length between the two frames. The wave direction is that frame images in a video are converted into a gray-scale image, block local binaryzation is carried out, image shadows are extracted, shadow features are improved through a series of image processing algorithms (open operation, closed operation, swelling corrosion and the like), a wave shadow surface is convenient to extract, and wave direction results are finally obtained through statistics of shadow angles. The wave height is obtained by constructing a wave static image data set containing static information and a difference image data set of dynamic information, calculating the roughness of the gray level image set, and obtaining the wave height through a fitting function relation between a video image roughness concept and the buoy wave height established by a large amount of data in the early stage and a wave height sequence based on a time sequence. And finally, estimating errors through a time series neural network according to the time series data of the wave height, the wave direction and the wave period, and realizing the error correction of the wave height, the wave direction and the wave period. 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, the electronic device includes: 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 are configured to communicate with each other via the communication bus 504. The processor 501 may call a computer program running on the memory 503 and on the processor 501 to execute the wave parameter detection method provided by the foregoing embodiments, for example, the method includes: 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 processing on a plurality of pictures to obtain a grayscale picture set; detecting and obtaining sea wave parameters of the sea waves based on the gray level picture set; the wave parameters include at least one of wave height, wave period, and wave direction.
An embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the wave parameter detection method provided in the foregoing embodiments when executed by a processor, for example, the method includes: 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 processing on a plurality of pictures to obtain a grayscale picture set; detecting and obtaining sea wave parameters of the sea waves based on the gray level picture set; the wave parameters include at least one of wave height, wave period, and wave direction.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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. Therefore, it is intended that the appended claims be interpreted as including 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 changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A sea wave parameter detection method is characterized by comprising 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 processing on a plurality of pictures to obtain a grayscale picture set;
detecting and obtaining sea wave parameters of the sea waves based on the gray level picture set; the wave parameters include at least one of wave height, wave period, and wave direction.
2. The method of claim 1, wherein the sea wave parameter is wave height; correspondingly, based on the grayscale picture set, sea wave parameters of the sea waves are detected and obtained, and the method comprises the following steps:
calculating the roughness of the gray level 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 wave height of the buoy.
3. The method of claim 2, wherein calculating the roughness of the grayscale picture set comprises:
counting the gray distribution of the pictures in the gray picture set;
and after removing the abnormal value, obtaining a fractal dimension through a gray level statistical method, and taking the fractal dimension as the roughness.
4. The method of claim 3, wherein obtaining the fractal dimension by a gray scale statistical method comprises:
given a scale r, the mean of the increments in the x and y directions are calculated, respectively:
where I (x, y) represents the gray scale value of the pixel at (x, y) and N is the size of the selected region;
calculating the mean value of the total increase E (r) ═ Ex(r)+Ey(r)；
According to the formula E (r) ═ CrHLogarithms are taken on two sides of the fractal dimension D, wherein log E (r) ═ H × log (r) + log (C) is provided, and the estimated value of the H parameter is obtained by least square fitting in the range of r, and then the fractal dimension D is obtained.
5. The method of claim 3, wherein obtaining the fractal dimension by a gray scale statistical method comprises:
from the fundamental nature of fractional brownian motion:
Var(|B(t2)-B(t1)|)～|t2-t1|2H (3)
converting equation (3) to the following equation:
E(|dIΔn|)|Δn|-H＝E(dIΔn＝1) (4)
wherein, E (| dI)Δn|) is the expected value of the gray scale change within the region range Δ n;
logarithms are taken on both sides of the above formula (4) to obtain the following formula:
for a certain image block f (i, j), four new image blocks gm (i, j) are defined, where m is 1,2,3, 5;
gm(i,j)＝∑(k,I)eU|f(i,j)-f(k,l)| (6)
wherein ,
for each m, calculating a histogram of the image block gm (i, j), wherein the specific algorithm is that the number of the histogram blocks with the height greater than zero in the histogram is recorded as n; summing the heights of the squares, sum; dividing the sum of the heights of the histogram blocks by the number n of the histogram blocks to obtain a histogram mean value E (| dI)Δn|), then substituting the compound with the formula (1) to obtain the corresponding Hm; d ═ H1+ H2+ H3)/3, where D is the fractal dimension.
6. The method of claim 1, wherein the wave parameter is a wave period; correspondingly, based on the grayscale picture set, sea wave parameters of the sea waves are detected and obtained, and the method comprises the following steps:
calculating the square sum error R of the difference between each frame in the gray level image set and the first frame to obtain a square sum error set Rs;
acquiring interval frame numbers dNum between pictures corresponding to a smaller value in the sum of squares error set Rs, and acquiring the total frame number of a wave period through the interval frame numbers dNum;
and dividing the total frame number by the frame number per second of a camera used for shooting the video stream to obtain the wave period.
7. The method of claim 1, wherein the wave parameter is wave direction; correspondingly, based on the grayscale picture set, sea wave parameters of the sea waves are detected and obtained, and the method comprises the following steps:
carrying out picture segmentation on the gray level pictures in the gray level picture set to obtain a plurality of rectangular blocks; obtaining a binarization threshold value for each rectangular block, carrying out binarization processing, and integrating a binarization result into a binarization picture;
carrying out corrosion, denoising and expansion processing on the binarization image, and segmenting the binarization image to obtain each texture block so as to obtain a texture block set;
calculating and obtaining an angle set and a size set sizes for each texture block in the texture block set;
weighting the sizes/sum (sizes) as the weight of each element in the angles set to obtain the wave direction.
8. The method of claim 1, wherein after detecting the wave parameters of the ocean wave, further comprising:
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 the error correction of the wave height, the wave direction and/or the wave period.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the wave parameter detection method according to any one of claims 1 to 8.
10. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the wave parameter detection method according to any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114519806A (en) * | 2022-01-29 | 2022-05-20 | 国家海洋环境预报中心 | Ocean wave level observation model training method and system |
| CN115824170A (en) * | 2023-02-17 | 2023-03-21 | 山东科技大学 | Method for measuring ocean waves by combining photogrammetry and laser radar |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101813476A (en) * | 2010-03-19 | 2010-08-25 | 天津大学 | Three-dimensional real-time monitoring system for offshore wave parameters |
| US20160203696A1 (en) * | 2013-09-26 | 2016-07-14 | Nec Corporation | Anomalous tide level fluctuation sensing device, anomalous tide level fluctuation sensing method, and anomalous tide level fluctuation sensing program |
| JP2017058322A (en) * | 2015-09-18 | 2017-03-23 | 株式会社Ihi | Wave measurement device, automatic maneuvering device and wave measurement method |
| CN107678025A (en) * | 2017-08-01 | 2018-02-09 | 北京海兰信数据科技股份有限公司 | Sea wave height computational methods and device, storage medium and processor |
| CN109886217A (en) * | 2019-02-26 | 2019-06-14 | 上海海洋大学 | A method of it is high that wave being detected from Nearshore Wave video based on convolutional neural networks |
| CN110473260A (en) * | 2019-06-28 | 2019-11-19 | 国家海洋技术中心 | A kind of wave video measuring device and method |
| CN110543698A (en) * | 2019-08-15 | 2019-12-06 | 合肥工业大学 | method for measuring surface roughness based on wavelet Brownian motion texture fusion model |
| WO2021157765A1 (en) * | 2020-02-04 | 2021-08-12 | (주)씨텍 | Waves observation method and system using image capturing camera for ocean observation buoy |
| CN113514833A (en) * | 2021-04-25 | 2021-10-19 | 南京信息工程大学 | Sea surface arbitrary point wave direction inversion method based on sea wave image |
| US20220155069A1 (en) * | 2019-03-01 | 2022-05-19 | Re Vision Consulting, Llc | System and method for wave prediction |
-
2021
- 2021-10-26 CN CN202111246378.6A patent/CN113899349B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101813476A (en) * | 2010-03-19 | 2010-08-25 | 天津大学 | Three-dimensional real-time monitoring system for offshore wave parameters |
| US20160203696A1 (en) * | 2013-09-26 | 2016-07-14 | Nec Corporation | Anomalous tide level fluctuation sensing device, anomalous tide level fluctuation sensing method, and anomalous tide level fluctuation sensing program |
| JP2017058322A (en) * | 2015-09-18 | 2017-03-23 | 株式会社Ihi | Wave measurement device, automatic maneuvering device and wave measurement method |
| CN107678025A (en) * | 2017-08-01 | 2018-02-09 | 北京海兰信数据科技股份有限公司 | Sea wave height computational methods and device, storage medium and processor |
| CN109886217A (en) * | 2019-02-26 | 2019-06-14 | 上海海洋大学 | A method of it is high that wave being detected from Nearshore Wave video based on convolutional neural networks |
| US20220155069A1 (en) * | 2019-03-01 | 2022-05-19 | Re Vision Consulting, Llc | System and method for wave prediction |
| CN110473260A (en) * | 2019-06-28 | 2019-11-19 | 国家海洋技术中心 | A kind of wave video measuring device and method |
| CN110543698A (en) * | 2019-08-15 | 2019-12-06 | 合肥工业大学 | method for measuring surface roughness based on wavelet Brownian motion texture fusion model |
| WO2021157765A1 (en) * | 2020-02-04 | 2021-08-12 | (주)씨텍 | Waves observation method and system using image capturing camera for ocean observation buoy |
| CN113514833A (en) * | 2021-04-25 | 2021-10-19 | 南京信息工程大学 | Sea surface arbitrary point wave direction inversion method based on sea wave image |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114519806A (en) * | 2022-01-29 | 2022-05-20 | 国家海洋环境预报中心 | Ocean wave level observation model training method and system |
| CN114519806B (en) * | 2022-01-29 | 2022-10-11 | 国家海洋环境预报中心 | Ocean wave level observation model training method and system |
| CN115824170A (en) * | 2023-02-17 | 2023-03-21 | 山东科技大学 | Method for measuring ocean waves by combining photogrammetry and laser radar |
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