CN113705501A - Offshore target detection method and system based on image recognition technology - Google Patents
Offshore target detection method and system based on image recognition technology Download PDFInfo
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Abstract
The invention provides a method and a system for detecting a marine target based on an image recognition technology, on one hand, the method comprises the steps of S1, obtaining an original image to be subjected to marine target detection; s2, judging whether the original image is a backlight image; s3, if the original image is a backlight image, preprocessing the original image by adopting a preset backlight image preprocessing algorithm to obtain a preprocessed image; if the original image is a non-backlight image, preprocessing the original image by adopting a preset frontlighting image preprocessing algorithm to obtain a preprocessed image; and S4, carrying out marine target detection based on the preprocessed image to obtain a detection result. In another aspect, the invention also provides a system for implementing the above method. The invention effectively enhances the pertinence of the preprocessing of the backlight image, thereby improving the accuracy of the preprocessed image obtained by the invention and further being beneficial to improving the accuracy of the detection of the offshore target.
Description
Technical Field
The invention relates to the field of detection, in particular to a method and a system for detecting a marine target based on an image recognition technology.
Background
In the prior art, when an image recognition technology is used for detecting a marine target, generally, the image is directly preprocessed, feature information is extracted, and then target recognition is performed based on the feature information. However, in the prior art, a scene of backlight is not considered in a preprocessing process, and under a backlight condition, a pixel value of a marine target left on an image may be smaller than a reflected sea wave, and the marine target is easily affected by the sea wave, so that a final target detection result is not accurate enough.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a method and a system for detecting a marine target based on an image recognition technology.
In one aspect, the invention provides a method for detecting a marine target based on an image recognition technology, which comprises the following steps:
s1, acquiring an original image to be subjected to marine target detection;
s2, judging whether the original image is a backlight image;
s3, if the original image is a backlight image, preprocessing the original image by adopting a preset backlight image preprocessing algorithm to obtain a preprocessed image;
if the original image is a non-backlight image, preprocessing the original image by adopting a preset frontlighting image preprocessing algorithm to obtain a preprocessed image;
and S4, carrying out marine target detection based on the preprocessed image to obtain a detection result.
Preferably, the determining whether the original image is a backlight image includes:
converting the original image into a gray image;
acquiring a gray level histogram of a gray level image;
normalizing the gray level histogram to obtain a normalized histogram S, S ═ S1,s2,…,s256)；
Calculating the standard deviation of the normalized histogram S:
wherein, devst(S) denotes the standard deviation of the normalized histogram S, StExpressing the total number of pixel points contained in the t-th gray level;
sea-sky-line detection is carried out on the gray level image, and the gray level image is divided into a sky area image and a sea surface area image based on the sea-sky-line detection;
converting the sky area image into a binary image;
performing connected domain detection on the binary image to obtain the total number num of pixel points contained in the connected domain with the maximum average pixel value in the binary imagemax；
Calculating the proportion of pixel points between the connected domain with the maximum average pixel value and the sky image:
wherein prop represents a proportion of pixel points between the maximum connected domain and the sky image, numskyRepresenting the total number of pixel points contained in the sky image;
calculating the backlight index of the original image:
wherein bklidx represents a backlight index, stdev, of an original imagestStandard value, prop, representing the standard deviation of a preset normalized histogramstRepresenting a preset proportional standard value, C representing a preset constant coefficient, α and β representing preset weight parameters, α + β being 1;
and judging whether bklidx is larger than a preset backlight index judgment threshold, if so, indicating that the original image is a backlight image, and if not, indicating that the original image is not the backlight image.
Preferably, the preprocessing the original image by using a preset backlight image preprocessing algorithm to obtain a preprocessed image includes:
converting the original image into a gray image;
carrying out gray reversal processing on the gray image by adopting a conversion filter to obtain a reversed image;
and carrying out segmentation processing on the reversed image by adopting an image segmentation algorithm to obtain a preprocessed image.
Preferably, the preprocessing the original image by using a preset front-lighting image preprocessing algorithm to obtain a preprocessed image includes:
converting the original image into a gray image;
acquiring an area extreme point image in the gray level image;
adjusting the area extreme point to obtain an adjusted area extreme point image;
and enhancing the gray level image based on the adjusted regional extreme point image to obtain a preprocessed image.
Preferably, the marine target detection based on the preprocessed image to obtain a detection result includes:
and inputting the preprocessed image into a pre-trained neural network model for recognition to obtain a detection result.
On the other hand, the invention also provides an offshore target detection system based on the image recognition technology, which comprises an acquisition module, a judgment module, a preprocessing module and a detection module;
the acquisition module is used for acquiring an original image to be subjected to marine target detection;
the judging module is used for judging whether the original image is a backlight image;
the acquisition module is used for preprocessing the original image by adopting a preset backlight image preprocessing algorithm when the original image is a backlight image to obtain a preprocessed image;
the method comprises the steps of obtaining an original image, and preprocessing the original image by adopting a preset front-light image preprocessing algorithm when the original image is a non-backlight image to obtain a preprocessed image;
the detection module is used for carrying out marine target detection based on the preprocessed image to obtain a detection result.
According to the invention, by carrying out backlight judgment on the original image and then respectively carrying out image preprocessing on the backlight image and the non-backlight image by adopting different preprocessing modes, the pertinence of preprocessing on the backlight image is effectively enhanced, so that the accuracy of the preprocessed image obtained by the method is improved, and the accuracy of detection on the offshore target is further improved.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a diagram of an exemplary embodiment of a method for detecting a marine target based on an image recognition technology according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
The invention provides a method and a system for detecting a marine target based on an image recognition technology.
In one aspect, the present invention provides a method for detecting a marine target based on an image recognition technique, which includes:
s1, acquiring an original image to be subjected to marine target detection;
s2, judging whether the original image is a backlight image;
s3, if the original image is a backlight image, preprocessing the original image by adopting a preset backlight image preprocessing algorithm to obtain a preprocessed image;
if the original image is a non-backlight image, preprocessing the original image by adopting a preset frontlighting image preprocessing algorithm to obtain a preprocessed image;
and S4, carrying out marine target detection based on the preprocessed image to obtain a detection result.
According to the invention, by carrying out backlight judgment on the original image and then respectively carrying out image preprocessing on the backlight image and the non-backlight image by adopting different preprocessing modes, the pertinence of preprocessing on the backlight image is effectively enhanced, so that the accuracy of the preprocessed image obtained by the method is improved, and the accuracy of detection on the offshore target is further improved.
Preferably, the determining whether the original image is a backlight image includes:
converting the original image into a gray image;
acquiring a gray level histogram of a gray level image;
normalizing the gray level histogram to obtain a normalized histogram S, S ═ S1,s2,…,s256)；
Calculating the standard deviation of the normalized histogram S:
wherein, devst(S) denotes the standard deviation of the normalized histogram S, StExpressing the total number of pixel points contained in the t-th gray level;
sea-sky-line detection is carried out on the gray level image, and the gray level image is divided into a sky area image and a sea surface area image based on the sea-sky-line detection;
converting the sky area image into a binary image;
performing connected domain detection on the binary image to obtain the total number num of pixel points contained in the connected domain with the maximum average pixel value in the binary imagemax；
Calculating the proportion of pixel points between the connected domain with the maximum average pixel value and the sky image:
wherein prop represents a proportion of pixel points between the maximum connected domain and the sky image, numskyRepresenting the total number of pixel points contained in the sky image;
calculating the backlight index of the original image:
wherein bklidx represents a backlight index, stdev, of an original imagestStandard value, prop, representing the standard deviation of a preset normalized histogramstRepresenting a preset proportional standard value, C representing a preset constant coefficient, α and β representing preset weight parameters, α + β being 1;
and judging whether bklidx is larger than a preset backlight index judgment threshold, if so, indicating that the original image is a backlight image, and if not, indicating that the original image is not the backlight image.
When the backlight image is judged, the accurate backlight judgment result can be comprehensively obtained by mainly considering two aspects of pixel value distribution of the gray level histogram and a connected domain with the maximum average pixel value of the sky area. When the backlight is used, under a general condition, the pixel value of the pixel point in the sky area is generally larger than that of the sea area, so that the variance of the histogram is relatively large, and whether the backlight is used or not can be judged according to the variance. Therefore, the invention also considers from the aspect of the connected domain of the sky image, and the connected domain where the sun is located is the connected domain with the largest average pixel value, so the degree of the backlight can be judged by the proportion of the pixel points between the connected domain and the sky image, and the larger the proportion is, the more the backlight is, thereby ensuring that whether the image is correctly judged.
Preferably, the preprocessing the original image by using a preset backlight image preprocessing algorithm to obtain a preprocessed image includes:
converting the original image into a gray image;
carrying out gray reversal processing on the gray image by adopting a conversion filter to obtain a reversed image;
and carrying out segmentation processing on the reversed image by adopting an image segmentation algorithm to obtain a preprocessed image.
When shooting in the backlight, the pixel point of the target area is often smaller than the pixel value of the pixel point of the sea wave of the reflected sunlight, so that the detection is difficult. Therefore, the method and the device convert the original pixel points with smaller gray values into the pixel points with larger gray values and convert the original pixel points with larger gray values into the pixel points with smaller gray values through the process of setting gray inversion, so that the influence degree of the pixel points in the target area by the pixel points of the sea waves of the reflected sunlight can be effectively reduced. The reverse image is segmented, so that the number of pixel points participating in the subsequent target detection process is reduced, and the identification speed of the method is improved.
Preferably, the converting the original image into a grayscale image includes:
and converting the original image into a gray image by adopting a weighted average value method.
Preferably, the performing a gray inversion process on the gray image by using a transform filter to obtain an inverted image includes:
the transform filter is constructed from the following construction functions:
wherein x and y represent the abscissa and ordinate, δ, of a pixel point in a gray scale image1Denotes a first reversal parameter, δ2Denotes a second reversal parameter, δ1∈(c1+0.16h,c1+0.17h)，δ2＝c2-c3exp(-3.8)h，c1、c2、c3Respectively representing a preset first constant coefficient, a preset second constant coefficient and a preset third constant coefficient, wherein h represents the number of rows of pixel points contained in the gray level image; c. C1Has a value range of (0.49,0.59), c2Has a value range of (2.69,2.81), c3The value range of (1) is (5.99, 6.19); cvp (x, y) denotes a construction function;
constructing a transform filter of size qxq using the construction function;
and performing convolution processing on the gray-scale image by using a transformation filter to obtain an inverted image.
The process of constructing the transformation filter is the same as that of constructing the Gaussian filter, only the construction function is different, the transformation filter constructed by the construction function can suppress the highlight area, and simultaneously improves the pixel value of the pixel point in the dark area, so that the pixel value distribution of the pixel point is more uniform, and the content of the detail information of the reversed image is favorably improved. Meanwhile, the filter constructed by the invention also has a certain filtering function, and can effectively reduce the noise in the reversed image.
Preferably, the segmenting the reversed image by using an image segmentation algorithm to obtain a preprocessed image includes:
using a mean iterative segmentation algorithm to segment the reverse image to obtain a foreground image and a background image;
acquiring a set S of corresponding pixel points of the foreground image in the reverse image;
and forming a preprocessed image by using the pixel points in the set S.
Besides the mean iterative segmentation algorithm, other algorithms capable of realizing foreground and background separation, such as a region growing algorithm, and the like, are also possible. Because the reversal image is generated on the basis of the gray image, each pixel point in the reversal image can find the pixel point with the same relative coordinate in the gray image, and similarly, the foreground image can also find the corresponding pixel point in the gray image.
Preferably, the preprocessing the original image by using a preset front-lighting image preprocessing algorithm to obtain a preprocessed image includes:
converting the original image into a gray image;
acquiring an area extreme point image in the gray level image;
adjusting the area extreme point to obtain an adjusted area extreme point image;
and enhancing the gray level image based on the adjusted regional extreme point image to obtain a preprocessed image.
According to the embodiment of the invention, the gray level image is enhanced by constructing the regional extreme point image, so that the difference between the target to be detected and the peripheral pixel points can be enhanced, the influence of waves on subsequent target identification can be effectively reduced, and the noise reduction effect is realized.
Preferably, the acquiring the area extreme point image in the grayscale image includes:
a computational convolution template is constructed as follows:
wherein, lcb represents convolution template, A multiplied by A represents the size of convolution window, b represents preset constant coefficient, and b is less than A;
performing convolution calculation on each pixel point in the gray image by using the lcb to obtain a convolution result:
jc＝lcb*gray
wherein jc represents a convolution result, gray represents a gray image, and x represents a convolution operation;
and judging whether the convolution result jc is larger than a preset convolution result threshold value, if so, taking the pixel points corresponding to the convolution result as area extreme points, and acquiring all the area extreme points from the gray level image to form an area extreme point image.
The purpose of the area extreme point image is to select the pixel point with the largest difference with the peripheral pixel points, because generally in the gray level image, the difference between the pixel point of the target area and the peripheral pixel points is larger, however, the pixel point of the wave area also has the characteristic, the difference is that the difference between the pixel point of the wave area and the peripheral pixel points is generally smaller than that of the target area, therefore, the accuracy of obtaining the pixel point belonging to the target area in the area extreme point image can be further improved by setting a threshold value.
Preferably, the adjusting the local extreme point to obtain the adjusted local extreme point image includes:
adjusting the extreme points of the regions in the following way:
wherein q' (x, y) represents the adjusted pixel value of the area extreme point at the coordinate (x, y), q (x, y) represents the adjusted pixel value of the area extreme point at the coordinate (x, y) and q (x, y) represents the adjusted pixel value of the area extreme point at the coordinate (x, y) or the coordinate (x, y) represents the area extreme point at the coordinate (x, y) corresponding to the areamiAnd q ismaRespectively representing the minimum value and the maximum value of the pixel values of the area extreme points in the neighborhood of K multiplied by K of the area extreme points at the coordinates (x, y), B representing a preset adjusting parameter, sh representing a value-taking function, and if q (x, y) -qmaIs equal to 0, sh (q (x, y) -qma) Is 1, otherwise sh (q (x, y) -q)ma) Has a value of-1.
The purpose of the adjustment processing is to consider that the average pixel value of the pixel points in the target area is possibly lower than the pixel points in the wave area, so that through the adjustment processing, the difference between the pixel points in the wave area and the peripheral pixel points is generally smaller than that in the target area, and after the adjustment processing, the difference between the pixel points in the target area and the peripheral pixel points can be further improved, and the subsequent accurate identification of the offshore target is facilitated.
Preferably, the enhancing the grayscale image based on the adjusted regional extreme point image to obtain a preprocessed image includes:
the gray level image is enhanced in the following way:
afgray(x,y)＝BL(x,y)×gray(x,y)
wherein, gray (x, y) represents the pixel value at the coordinate (x, y) in the gray image, BL represents the enhancement coefficient, affray (x, y) represents the pixel value after the enhancement processing is performed on gray (x, y),
BL (x, y) represents an enhancement coefficient, and is obtained by:
if it is
Less than 1, then
If it is
Then
If it is
Greater than 2, BL (x, y) is equal to 1, where h1、h2、h3Represents a predetermined proportionality coefficient, h1+h2+h3＝1。
In the enhancing process, because the enhancing coefficient is related to the image of the extreme point of the region, the pixel points related to the target region can be further enhanced, and thus the difference between the pixel points of the target region and the wave pixel points is increased.
Preferably, the marine target detection based on the preprocessed image to obtain a detection result includes:
and inputting the preprocessed image into a pre-trained neural network model for recognition to obtain a detection result.
On the other hand, the invention also provides an offshore target detection system based on the image recognition technology, which comprises an acquisition module, a judgment module, a preprocessing module and a detection module;
the acquisition module is used for acquiring an original image to be subjected to marine target detection;
the judging module is used for judging whether the original image is a backlight image;
the acquisition module is used for preprocessing the original image by adopting a preset backlight image preprocessing algorithm when the original image is a backlight image to obtain a preprocessed image;
the method comprises the steps of obtaining an original image, and preprocessing the original image by adopting a preset front-light image preprocessing algorithm when the original image is a non-backlight image to obtain a preprocessed image;
the detection module is used for carrying out marine target detection based on the preprocessed image to obtain a detection result.
While embodiments of the invention have been shown and described, it will be understood by those skilled in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (6)
1. A marine target detection method based on an image recognition technology is characterized by comprising the following steps:
s1, acquiring an original image to be subjected to marine target detection;
s2, judging whether the original image is a backlight image;
s3, if the original image is a backlight image, preprocessing the original image by adopting a preset backlight image preprocessing algorithm to obtain a preprocessed image;
if the original image is a non-backlight image, preprocessing the original image by adopting a preset frontlighting image preprocessing algorithm to obtain a preprocessed image;
and S4, carrying out marine target detection based on the preprocessed image to obtain a detection result.
2. The method for detecting the marine target based on the image recognition technology as claimed in claim 1, wherein the determining whether the original image is a backlight image comprises:
converting the original image into a gray image;
acquiring a gray level histogram of a gray level image;
normalizing the gray level histogram to obtain a normalized histogram S, S ═ S1,s2,…,s256)；
Calculating the standard deviation of the normalized histogram S:
wherein, devst(S) denotes the standard deviation of the normalized histogram S, StExpressing the total number of pixel points contained in the t-th gray level;
sea-sky-line detection is carried out on the gray level image, and the gray level image is divided into a sky area image and a sea surface area image based on the sea-sky-line detection;
converting the sky area image into a binary image;
performing connected domain detection on the binary image to obtain the total number num of pixel points contained in the connected domain with the maximum average pixel value in the binary imagemax；
Calculating the proportion of pixel points between the connected domain with the maximum average pixel value and the sky image:
wherein prop represents a proportion of pixel points between the maximum connected domain and the sky image, numskyRepresenting the total number of pixel points contained in the sky image;
calculating the backlight index of the original image:
wherein bklidx represents a backlight index, stdev, of an original imagestStandard value, prop, representing the standard deviation of a preset normalized histogramstRepresenting a preset proportional standard value, C representing a preset constant coefficient, α and β representing preset weight parameters, α + β being 1;
and judging whether bklidx is larger than a preset backlight index judgment threshold, if so, indicating that the original image is a backlight image, and if not, indicating that the original image is not the backlight image.
3. The method for detecting marine targets based on image recognition technology as claimed in claim 1, wherein the preprocessing the original image by using a preset backlight image preprocessing algorithm to obtain a preprocessed image comprises:
converting the original image into a gray image;
carrying out gray reversal processing on the gray image by adopting a conversion filter to obtain a reversed image;
and carrying out segmentation processing on the reversed image by adopting an image segmentation algorithm to obtain a preprocessed image.
4. The method for detecting the marine target based on the image recognition technology as claimed in claim 1, wherein the preprocessing the original image by using a preset front-lighting image preprocessing algorithm to obtain a preprocessed image comprises:
converting the original image into a gray image;
acquiring an area extreme point image in the gray level image;
adjusting the area extreme point to obtain an adjusted area extreme point image;
and enhancing the gray level image based on the adjusted regional extreme point image to obtain a preprocessed image.
5. The offshore target detection method based on the image recognition technology as claimed in claim 3, wherein the offshore target detection based on the preprocessed image to obtain the detection result comprises:
and inputting the preprocessed image into a pre-trained neural network model for recognition to obtain a detection result.
6. A marine target detection system based on an image recognition technology is characterized by comprising an acquisition module, a judgment module, a preprocessing module and a detection module;
the acquisition module is used for acquiring an original image to be subjected to marine target detection;
the judging module is used for judging whether the original image is a backlight image;
the acquisition module is used for preprocessing the original image by adopting a preset backlight image preprocessing algorithm when the original image is a backlight image to obtain a preprocessed image;
the method comprises the steps of obtaining an original image, and preprocessing the original image by adopting a preset front-light image preprocessing algorithm when the original image is a non-backlight image to obtain a preprocessed image;
the detection module is used for carrying out marine target detection based on the preprocessed image to obtain a detection result.
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| CN116758508A (en) * | 2023-08-18 | 2023-09-15 | 四川蜀道新能源科技发展有限公司 | Pavement marking detection method, system and terminal based on pixel difference expansion processing |
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