A method for evaluating the underwater antifouling effect of a fiber web and a computer-readable storage medium

Abstract

The embodiment of the present invention provides a method for evaluating the underwater antifouling effect of a fiber mesh, comprising the following steps: selecting a photo containing a fiber mesh sample, cutting the surrounding area of ​​the fiber mesh sample; reading the fiber mesh image, and converting the read true color image into a grayscale image, and outputting a matrix; determining the grayscale value of the pollutant and screening the grayscale image matrix, outputting a matrix composed of 0 and 1, and selecting random points in the matrix according to the size of the screened matrix; establishing a new matrix in the matrix with each random point as a vertex; counting each new matrix and calculating the percentage of the polluted area; counting the percentage of the polluted area of ​​all new matrices, outputting a standard curve, and analyzing to obtain the distribution, uniformity and pollution degree information of the percentage of the polluted area. The present invention automatically and objectively obtains the evaluation result of the underwater antifouling property of the fiber mesh sample, and solves the problem that the evaluation of the underwater antifouling property of the fiber mesh sample in the industry is too subjective and the results are inaccurate.

Description

Technical Field

[0001] The invention relates to a method for evaluating the underwater performance of a fiber mesh sample, and in particular to a method for evaluating the underwater antifouling effect of a fiber mesh and a computer-readable storage medium. Background Art

[0002] Fiber materials have been widely used in underwater applications due to their good flexibility and excellent quality-strength ratio. Due to long-term immersion or immersion in a humid, salty environment, aquatic organisms will grow on the fiber net, blocking the mesh, destroying the rope structure, reducing the strength of the net, and increasing the weight of the net. Therefore, underwater antifouling performance is an important performance that determines the underwater use effect and life of the fiber net. The main indicators for evaluating its performance include: the type of fouling organisms, the number of fouling organisms attached, and the area covered by the fouling organisms attached.

[0003] At present, there is no national standard or industry standard for the evaluation of the antifouling performance of fiber materials in underwater environments. The industry generally refers to the provisions of GB / T 5370-2007 Shallow Sea Immersion Test Method for Antifouling Paint Samples. Currently, the antifouling effect of antifouling samples is visually evaluated. The evaluation principle is to count and record the types of different fouling organisms and the corresponding number of attached colonies, or the percentage of area covered by seaweed, mud, etc.

[0004] The counting of colonies of fouling organisms and the assessment of the degree of fouling only consider the number, not the size of the colonies, which makes the assessment results subject to human subjective influence. On the fiber net, the area covered by seaweed and mud depends on the fiber net structure, making it difficult to give an accurate result during the assessment. These two aspects make the assessment of the underwater antifouling effect of the fiber net inaccurate due to subjective factors. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method for evaluating the underwater antifouling effect of a fiber mesh and a computer-readable storage medium to solve the problem in the industry that the underwater antifouling evaluation of a fiber mesh sample is too subjective and the results are inaccurate.

[0006] The embodiment of the present invention provides a method for evaluating the underwater antifouling effect of a fiber web, comprising the following steps:

[0007] Step 1: Determine the fiber mesh image area that needs to be evaluated for underwater antifouling properties, select a photo containing a fiber mesh sample, and use image editing tools to crop the area around the fiber mesh sample, leaving only the fiber mesh sample as much as possible;

[0008] Step 2: Use the imread function in MatLab to read the fiber mesh image, and use the rgb2gray function to convert the read true color image into a grayscale image, and output a matrix with c columns and r rows;

[0009] Step 3: According to the pollution color depth, light saturation and brightness of the original image, determine the gray value of the pollutant and filter the gray image matrix, and output a matrix composed of 0 and 1, where 0 represents unpolluted and 1 represents polluted;

[0010] Step 4: According to the size of the screened matrix, select a random point in the matrix; take each random point as a vertex, and establish a new matrix in the matrix with the number of columns c and the number of rows being 1 / 10 of the grayscale image matrix column number c and 1 / 10 of the grayscale image matrix row number r respectively;

[0011] Step 5: Count the number of points n with a value of 1 in each new matrix. i , the percentage of polluted area in the matrix is ​​s i The calculation is as follows:

[0012]

[0013] Step 6: Count the percentage of contaminated area s of all new matrices, output the standard curve, and obtain the distribution of the percentage of contaminated area, the uniformity of the contaminated area, and the degree of contamination information based on the standard curve analysis.

[0014] In an embodiment of the present invention, the number of columns c and the number of rows r are both rounded down to integers.

[0015] In one embodiment of the present invention, in step 4, the grayscale image matrix is ​​divided into 4 regions, and the direction of establishing a new matrix by random points in each region follows the following rule:

[0016] Area A: The randomly selected point is the upper left vertex of the matrix, the number of matrix columns is +1 / 10 the number of grayscale image matrix columns c, and the number of matrix rows is +1 / 10 the number of grayscale image matrix rows r;

[0017] Area B: The randomly selected point is the lower left vertex of the matrix, the number of matrix columns is +1 / 10 the number of grayscale image matrix columns, and the number of matrix rows is -1 / 10 the number of grayscale image matrix rows r;

[0018] Area C: The randomly selected point is the lower right vertex of the matrix, the number of matrix columns is -1 / 10 the number of grayscale image matrix columns, and the number of matrix rows is -1 / 10 the number of grayscale image matrix rows r;

[0019] Area D: The randomly selected point is the upper right vertex of the matrix, the number of matrix columns is -1 / 10 the number of grayscale image matrix columns, and the number of matrix rows is +1 / 10 the number of grayscale image matrix rows r.

[0020] In one embodiment of the present invention, in step one, the fiber mesh sample photo is selected from a photo with a monotonous and uniform background color, few obstructions, uniform lighting, and sufficient lighting.

[0021] In one embodiment of the present invention, in step 4, at least 10,000 points or 0.05% of all pixel points are selected to meet the requirements of data statistics.

[0022] In one embodiment of the present invention, in step three, the grayscale value of the pollutant ranges from 0 to 255. When the grayscale value is less than 100, it represents contamination and outputs 1. When the grayscale value is greater than 100, it represents no contamination and outputs 0.

[0023] In one embodiment of the present invention, in step six, the percentage of the contaminated area is set at intervals of 5%.

[0024] The embodiment of the present invention further discloses a computer-readable storage medium storing a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0025] The embodiment of the present invention avoids subjective influences such as human factors, and can provide more and more systematic statistical data analysis compared to the shallow sea immersion test method for antifouling paint samples in GB / T 5370-2007, and can provide statistical data analysis results for the degree of contamination, distribution, and uniformity of the entire fiber mesh sample, thereby helping experimenters to obtain better, more accurate, and more objective evaluation results and conclusions of the underwater antifouling properties of the fiber mesh sample. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the regional distribution of the grayscale image matrix of the fiber mesh sample according to the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] As shown in the figure, an embodiment of the present invention provides a method for evaluating the underwater antifouling effect of a fiber mesh sample, based on computer image processing, comprising the following steps:

[0029] Step 1: Determine the fiber mesh image area that needs to be evaluated for underwater antifouling properties, select a photo containing a fiber mesh sample, and use image editing tools to crop the area around the fiber mesh sample, leaving only the fiber mesh sample as much as possible;

[0030] Step 2: Use the imread function in MatLab to read the fiber mesh image, and use the rgb2gray function to convert the read true color image into a grayscale image, and output a matrix with c columns and r rows. Specifically, output a matrix with c columns and r rows of 4608;

[0031] Step 3: According to the pollution color depth, light saturation and brightness of the original image, determine the gray value of the pollutant and filter the gray image matrix, and output a matrix composed of 0 and 1, where 0 represents unpolluted and 1 represents polluted;

[0032] Step 4: Select 10,000 random points in the matrix, take each random point as a vertex, and establish a new matrix in the matrix with the number of columns and rows being 1 / 10 of the number of grayscale image matrix columns c and 1 / 10 of the number of grayscale image matrix rows r, respectively. The number of columns and rows of the new matrix is ​​460 and 345, respectively. In other embodiments, select at least 10,000 points or 0.05% of all pixel points, so that the number is large enough to meet the requirements of data statistics.

[0033] Step 5: Count the number of points n with a value of 1 in each new matrix. i , the percentage of polluted area in the matrix is ​​s i The calculation is as follows:

[0034]

[0035] Step 6: Count the percentage of contaminated area s of all new matrices, output the standard curve, and obtain the distribution of the percentage of contaminated area, the uniformity of the contaminated area, and the degree of contamination information based on the standard curve analysis.

[0036] In one embodiment of the present invention, the number of columns c and the number of rows r are both rounded down to integers. For example, if the number of columns c of the original matrix output is 4608 and the number of rows r is 3456, the number of columns of the new matrix is ​​460 and the number of rows is 345.

[0037] In one embodiment of the present invention, in step 4, the grayscale image matrix is ​​divided into four regions, which effectively avoids the problem that the newly established matrix overflows the grayscale image matrix boundary. The direction of the new matrix established by the random points in each region follows the following rule:

[0038] Area A: The randomly selected point is the upper left vertex of the matrix, the number of matrix columns is +1 / 10 the number of grayscale image matrix columns c, the number of matrix rows is +1 / 10 the number of grayscale image matrix rows r, the new matrix has 460 columns and 345 rows;

[0039] Area B: The randomly selected point is the lower left vertex of the matrix. The number of matrix columns is +1 / 10 of the grayscale image matrix columns, and the number of matrix rows is -1 / 10 of the grayscale image matrix row number r. The new matrix has 460 columns and 345 rows.

[0040] Area C: The randomly selected point is the lower right vertex of the matrix. The number of matrix columns is -1 / 10 of the grayscale image matrix columns, and the number of matrix rows is -1 / 10 of the grayscale image matrix row number r. The new matrix has 460 columns and 345 rows.

[0041] Area D: The randomly selected point is the upper right vertex of the matrix. The number of matrix columns is -1 / 10 of the grayscale image matrix columns, and the number of matrix rows is +1 / 10 of the grayscale image matrix row number r. The new matrix has 460 columns and 345 rows.

[0042] In one embodiment of the present invention, in step one, the selected fiber mesh sample photo should preferably be a photo with a monotonous and uniform background color, few obstructions, uniform lighting, and sufficient lighting to reduce interference.

[0043] In one embodiment of the present invention, in step 4, enough random points are selected to meet the requirements of data statistics.

[0044] In one embodiment of the present invention, in step three, the grayscale value of the pollutant ranges from 0 to 255. When the grayscale value is less than 100, it represents contamination and outputs 1. When the grayscale value is greater than 100, it represents no contamination and outputs 0.

[0045] In one embodiment of the present invention, in step six, the percentage of the contaminated area is set at intervals of 5%.

[0046] The embodiment of the present invention further discloses a computer-readable storage medium storing a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0047] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the underwater antifouling effect of a fiber web, characterized in that: The following steps are involved: Step 1: determine the fiber web image area that needs to be evaluated for underwater antifouling properties, select a photo containing a fiber web sample, and cut out the area surrounding the fiber web sample, leaving only the fiber web sample; Step 2: Use the imread function in MatLab to read the fiber mesh image, and use the rgb2gray function to convert the read true color image into a grayscale image, and output a matrix with c columns and r rows; Step 3: According to the pollution color depth, light saturation and brightness of the original image, determine the gray value of the pollutant and filter the gray image matrix, and output a matrix composed of 0 and 1, where 0 represents unpolluted and 1 represents polluted; Step 4: Select random points in the matrix according to the size of the screened matrix; With each random point as a vertex, a new matrix is ​​established in the matrix with the number of columns and rows being 1 / 10 the number of columns c and 1 / 10 the number of rows r of the grayscale image matrix respectively; Step 5: Count the number of points n with a value of 1 in each new matrix. i , the percentage of polluted area in the matrix is ​​s i The calculation is as follows: Step 6: Count the percentage of contaminated area s of all new matrices, output the standard curve, and obtain the distribution of the percentage of contaminated area, the uniformity of the contaminated area, and the degree of contamination information according to the standard curve analysis; In step three, the grayscale value range of the pollutant is 0-255. When the grayscale value is less than 100, it means it is polluted and output is 1. When the grayscale value is greater than 100, it means it is not polluted and output is 0.

2. A method for evaluating the underwater antifouling effect of a fiber web as claimed in claim 1, characterized in that: The number of columns c and the number of rows r are both rounded down to integers.

3. A method for evaluating the underwater antifouling effect of a fiber web as claimed in claim 1, characterized in that: In step one, the fiber mesh sample photo is selected to have a monotonous and uniform background color, few obstructions, uniform lighting, and sufficient lighting.

4. A method for evaluating the underwater antifouling effect of a fiber web as claimed in claim 1, characterized in that: In step 4, at least 10,000 points or 0.05% of all pixels are selected to meet the requirements of data statistics.

5. The method for evaluating the underwater antifouling effect of a fiber web according to claim 1, characterized in that: In step six, the percentage of contaminated area is set at 5% intervals.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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