Method and system for identifying salting-out image on surface of offshore floating photovoltaic module

Abstract

The invention relates to a salting-out image recognition method and system for the surface of an offshore floating type photovoltaic module. The method comprises the steps that a salting-out data set for the surface of the photovoltaic module of an offshore floating type photovoltaic power station is acquired; performing image distortion correction processing on the collected photovoltaic module surface salt precipitation image to obtain a corrected picture; performing de-noising processing on the corrected picture to obtain a de-noised picture; enhancing the denoised picture by using a histogram equalization method to obtain an enhanced picture; and segmenting the enhanced picture by using a self-adaptive threshold binarization algorithm to obtain a processed salt precipitation image, and inputting the processed salt precipitation image into an image recognition algorithm to complete recognition. According to the method, multi-step processing including image distortion correction, denoising, enhancement and adaptive threshold binarization processing is carried out on the salt precipitation image on the surface of the photovoltaic module, interference caused by environmental factors, illumination changes and poor image quality can be effectively eliminated, and the image quality and the recognition accuracy are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of image recognition technology, and in particular to a method and system for recognizing salt precipitation images on the surface of a floating photovoltaic module at sea. Background Technology

[0002] In recent years, with the large-scale development of offshore photovoltaic (PV) power plants, the problem of surface contamination of PV modules has become increasingly prominent. The presence of surface contamination on PV panels leads to a decrease in power generation efficiency, necessitating timely cleaning and maintenance to ensure the normal operation of the PV power generation system. Salt precipitation, in particular, has a significant impact on the shading and power generation efficiency of PV modules. However, due to the unique situation of offshore PV power plants being located on the sea surface without any obstructions, image acquisition is easily affected by sunlight. During acquisition, the smooth surface of the PV panels and the potential for water surface fluctuations in the marine environment cause reflected light to be projected onto the acquired image, resulting in numerous influencing factors and distortion of the acquired data. This makes it difficult to accurately extract the features of salt precipitation from the image. In the marine environment, cameras may be affected by wind and waves, causing vibrations that blur the image and affect the identification of salt precipitation. Furthermore, different models of image acquisition equipment and different shooting angles can cause varying degrees of image distortion during shooting. Camera sensors themselves have inherent noise; under low-light conditions or when shooting with longer exposure times, the camera sensor receives more photons, which may lead to thermal or electronic noise, thus introducing image noise. These factors all affect the extraction of salt precipitation features, leading to inaccurate results. In the unique environment of offshore photovoltaic power plants, traditional image recognition algorithms are more susceptible to interference from external factors such as uneven lighting, resulting in fragmented segmentation results and blurred edges. Therefore, traditional image recognition algorithms are ineffective in identifying salt precipitation on the surface of photovoltaic modules in offshore photovoltaic power plants due to these factors. They may also fail to effectively represent image features in certain environments. Thus, a method and system are needed to effectively identify the salt precipitation features on the surface of photovoltaic modules in offshore floating photovoltaic power plants. Summary of the Invention

[0003] To address the aforementioned problems, the present invention aims to provide a method and system for recognizing salt precipitation images on the surface of floating photovoltaic modules at sea.

[0004] A method for salt precipitation image recognition on the surface of a floating photovoltaic module at sea, comprising: S1. Obtain the salt precipitation dataset on the surface of photovoltaic modules in offshore floating photovoltaic power stations; S2. Perform image distortion correction processing on the collected images of salt precipitation on the surface of photovoltaic modules to obtain the corrected images; S3. Denoise the corrected image to obtain the denoised image; S4. Use histogram mean squares to enhance the denoised image to obtain the enhanced image; S5. The enhanced image is segmented using an adaptive threshold binarization algorithm to obtain the processed salt precipitation image.

[0005] Preferably, in step S2, bicubic interpolation is used for distortion correction; wherein the bicubic interpolation formula is: in, This represents the i-th interpolation coefficient in bicubic interpolation. This represents the j-th interpolation coefficient in bicubic interpolation, and the target coordinates. The coordinates of the 16 nearest neighbors are , Indicates in Pixel value at the location, This represents the pixel value of the target coordinates after distortion correction.

[0006] Preferably, in S3, a 3*3 filtering template is constructed with any point on the corrected image as the center. The pixel at the center of the filtering template and the pixel values ​​around it are sorted, and the value of the center pixel is replaced with the median value as the denoised pixel. The corrected image is then traversed to obtain the denoised image.

[0007] Preferably, step S5, segmenting the enhanced image using an adaptive threshold binarization algorithm to obtain the processed salt precipitation image, includes: The denoised image is converted to grayscale to obtain a grayscale image. Calculate the global threshold based on the pixel values ​​in the grayscale image; In a grayscale image, pixels with values ​​greater than the global threshold are set to white, and pixels with values ​​less than the global threshold are set to black.

[0008] Preferably, in step S5, the grayscale conversion formula for the image is: gray = 0.299 * R + 0.587 * G + 0.114 * B R, G, and B represent the red, green, and blue components in a color image, respectively, while gray represents the grayscale value in a grayscale image.

[0009] Preferably, in step S5, the calculation process for the global threshold is as follows: Set an initial threshold and use the initial threshold to divide the grayscale image into two grayscale levels; Calculate the average of two gray levels; Take the average of the two gray levels as the new threshold, and re-divide the grayscale image into two gray levels. Iterate until convergence to obtain the global threshold.

[0010] This invention also provides a salt precipitation image recognition system for the surface of a floating photovoltaic module at sea, comprising: The dataset acquisition module is used to acquire the salt precipitation dataset on the surface of photovoltaic modules in offshore floating photovoltaic power plants. The distortion correction module is used to perform image distortion correction processing on the collected images of salt precipitation on the surface of photovoltaic modules to obtain corrected images; The noise reduction module is used to process the corrected image to obtain a denoised image. The enhancement module is used to enhance the denoised image using the histogram averaging method to obtain the enhanced image; The segmentation module is used to segment the enhanced image using an adaptive threshold binarization algorithm to obtain the processed salt precipitation image.

[0011] The present invention also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, characterized in that the computer program, when executed by the processor, implements the steps in the above-described method for salt precipitation image recognition on the surface of a floating marine photovoltaic module.

[0012] The present invention also provides a storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps in the above-described method for recognizing salt precipitation images on the surface of a floating marine photovoltaic module.

[0013] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention relates to a method for recognizing salt precipitation images on the surface of a floating photovoltaic module at sea. Compared with the prior art, this invention performs multi-step processing on the salt precipitation images on the surface of the photovoltaic module, including image distortion correction, noise reduction, enhancement, and adaptive threshold binarization, which can effectively eliminate interference caused by environmental factors, changes in illumination, and poor image quality, and significantly improve image quality and recognition accuracy.

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart of a method for recognizing salt precipitation images on the surface of a floating photovoltaic module provided by the present invention; Figure 2 A schematic diagram of a salt precipitation image recognition system on the surface of a floating photovoltaic module provided by the present invention. Detailed Implementation

[0017] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0018] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0019] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0020] Please see Figure 1 A method for recognizing salt precipitation images on the surface of a floating photovoltaic module at sea, comprising: S1. Obtain the salt precipitation dataset on the surface of photovoltaic modules in offshore floating photovoltaic power stations; Due to the unique geographical location of offshore floating photovoltaic power stations, manual inspection is too difficult. Therefore, satellites or drones are usually used for data collection during routine inspections.

[0021] S2. Perform image distortion correction processing on the collected images of salt precipitation on the surface of photovoltaic modules to obtain the corrected images; S2 includes: S2-1. Calculate the distortion parameters of the camera or lens. Distortion parameter models are usually used to describe the degree and type of distortion.

[0022] S2-2. Perform distortion correction on the image based on the calculated distortion parameters. Bicubic interpolation is used for distortion correction. Bicubic interpolation calculates the interpolation result by weighting the 16 nearest neighbor pixels around the target pixel, resulting in a smoother interpolation effect to correct image pixel shifts and distortions caused by distortion.

[0023] S2-3. Let the pixel value at coordinates (x, y) in the original image be f(x, y), and the coordinates of the 16 nearest neighbors around the target coordinates (x', y') be f(x, y'). n , y m The corresponding pixel value is f(x). n , y m ), where n and m take values ​​of -1, 0, and 1, respectively. Based on the correction effect and requirements, the bicubic interpolation formula is as follows: in, This represents the i-th interpolation coefficient in bicubic interpolation. This represents the j-th interpolation coefficient in bicubic interpolation, and the target coordinates. The coordinates of the 16 nearest neighbors are , Indicates in Pixel value at the location, These represent the pixel values ​​of the target coordinates after distortion correction. These coefficients are calculated using the B-spline function.

[0024] S2-4. Adjust the distortion correction parameters according to the correction effect and requirements to achieve the best effect after correction. Preview the processing effect to determine the image quality.

[0025] S3. Denoise the corrected image to obtain the denoised image; Furthermore, S3 includes: S3-1. Define a 3x3 filter template. Sort the values ​​of the center pixel and its surrounding pixels, and replace the center pixel's value with the median (the value in the middle of the sorted sequence). The formula for median filtering is as follows: Suppose we apply a medium filter at position (i, j), which includes the following permutation: abcdefghi The value of the center pixel: F(i, j) = median(e, {a, b, c, d, f, g, h, i}) (math type) Here, `median()` represents taking the median of a set of values. This formula means sorting the pixel values ​​around the center pixel position (i, j) and taking the median value as the center pixel's value. This median filtering rule is applied to each pixel during the filtering process.

[0026] S4. Use histogram mean squares to enhance the denoised image to obtain the enhanced image; S5. The enhanced image is segmented using an adaptive threshold binarization algorithm to obtain the processed salt precipitation image.

[0027] S5 specifically includes: S5-1. Convert a grayscale image to a grayscale matrix: Convert the original color image to a grayscale image to obtain a grayscale matrix.

[0028] The formula for converting an image to grayscale is as follows: gray = 0.299 * R + 0.587 * G + 0.114 * B Where R, G, and B are the red, green, and blue components in the color image, respectively, and gray is the gray value in the grayscale image.

[0029] S5-2. Determine whether to use the global threshold binarization algorithm. Set the initial threshold T1 as an initial value, which is here set to the initial average value of the image grayscale. The average grayscale value is calculated by summing the pixel grayscale values ​​in a local region and dividing by the number of pixels in that region.

[0030] During the iterative calculation of the threshold, the T value is continuously updated so that the average values ​​of the two gray levels gradually approach each other, thereby reaching the optimal global threshold. The following steps are repeated iteratively until convergence: Calculate the average of the two gray levels: M1 is the average of pixel values ​​less than or equal to T, and M2 is the average of pixel values ​​greater than T. Calculate the new threshold T as (M1 + M2) / 2. S5-3, Binarize each pixel: if the pixel value is greater than T, set it to 255 (white); otherwise, set it to 0 (black).

[0031] S5-4. Output Binary Image: After binarizing all pixels according to the threshold, the final binary salt precipitation image is obtained for subsequent salt precipitation image recognition.

[0032] This invention performs multi-step processing on the salt precipitation image on the surface of photovoltaic modules, including image distortion correction, denoising, enhancement, and adaptive threshold binarization, which can effectively eliminate interference caused by environmental factors, light changes, and poor image quality, and significantly improve image quality and recognition accuracy.

[0033] Please see Figure 2 The present invention also provides a salt precipitation image recognition system for the surface of a floating photovoltaic module at sea, comprising: The dataset acquisition module is used to acquire the salt precipitation dataset on the surface of photovoltaic modules in offshore floating photovoltaic power plants. The distortion correction module is used to perform image distortion correction processing on the collected images of salt precipitation on the surface of photovoltaic modules to obtain corrected images; The noise reduction module is used to process the corrected image to obtain a denoised image. The enhancement module is used to enhance the denoised image using the histogram averaging method to obtain the enhanced image; The segmentation module is used to segment the enhanced image using an adaptive threshold binarization algorithm to obtain the processed salt precipitation image.

[0034] Compared with the prior art, the beneficial effects of the salt precipitation image recognition system on the surface of a floating photovoltaic module provided by the present invention are the same as the beneficial effects of the salt precipitation image recognition method on the surface of a floating photovoltaic module described in the above technical solution, and will not be repeated here.

[0035] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps in the above-described method for recognizing salt precipitation images on the surface of a floating marine photovoltaic module. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present invention are the same as the beneficial effects of the above-described method for recognizing salt precipitation images on the surface of a floating marine photovoltaic module, and will not be elaborated here.

[0036] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method of salt efflorescence image recognition on the surface of a marine floating photovoltaic assembly, characterized in that, The method comprises the following steps: S1, acquiring a salt precipitation data set on the surface of a photovoltaic component of a marine floating photovoltaic power station; S2, performing image distortion correction processing on the collected salt precipitation image on the surface of the photovoltaic component to obtain a corrected picture; S3, performing denoising processing on the corrected picture to obtain a denoised picture; S4, using a histogram equalization method to enhance the denoised picture to obtain an enhanced picture; S5, using an adaptive threshold binarization algorithm to segment the enhanced picture to obtain a processed salt precipitation image, and inputting the processed salt precipitation image into an image recognition algorithm to complete recognition.

2. A method of salt pattern image recognition on the surface of a floating photovoltaic assembly at sea according to claim 1, characterized in that, In the S2, a bicubic interpolation processing method is used for distortion correction; wherein the bicubic interpolation formula is: wherein, denotes the i-th interpolation coefficient in the bi-cubic interpolation, denotes the j-th interpolation coefficient in the bi-cubic interpolation, the target coordinate the 16 nearest neighbor coordinates around the target coordinate are , denotes the pixel value at the position , denotes the pixel value of the target coordinate after distortion correction.

3. A method of salt pattern recognition on the surface of a floating photovoltaic assembly at sea according to claim 1, characterized in that, In the S3, taking any point on the corrected picture as the center, a 3*3 size filter template is constructed, the pixel values of the center and its surrounding pixels are sorted, the value of the center pixel is replaced by the median value as the denoised pixel point, and the denoised picture is obtained by traversing the corrected picture.

4. A method of salt pattern recognition on the surface of a floating photovoltaic assembly at sea according to claim 1, characterized in that, The S5 uses an adaptive threshold binarization algorithm to segment the enhanced picture to obtain a processed salt precipitation image, which comprises: performing gray scale processing on the denoised picture to obtain a gray scale image; calculating a global threshold value according to the pixel value on the gray scale image; setting the corresponding pixel points with pixel values greater than the global threshold value on the gray scale image as white, and setting the corresponding pixel points with pixel values less than the global threshold value as black.

5. A method of salt pattern recognition on the surface of a floating photovoltaic assembly at sea according to claim 4, characterized in that, In the S5, the gray scale processing formula of the picture is: gray = 0.299 * R + 0.587 * G + 0.114 * B R, G and B are respectively the red, green and blue components in the color image, and gray is the gray scale value in the gray scale image.

6. A method of salt pattern recognition on the surface of a floating photovoltaic assembly at sea according to claim 4, characterized in that, In the S5, the calculation process of the global threshold value is: setting an initial threshold value, using the initial threshold value to divide the gray scale image into two gray scale levels; calculating the average values of the two gray scale levels; taking the average value of the average values of the two gray scale levels as a new threshold value, re-dividing the gray scale image into two gray scale levels, and iterating to convergence to obtain the global threshold value.

7. A salt efflorescence image recognition system for a photovoltaic assembly surface on a floating offshore platform, characterized in that, The method comprises the following steps: a data set acquisition module for acquiring a salt precipitation data set on the surface of a photovoltaic component of a marine floating photovoltaic power station; a distortion correction module for performing image distortion correction processing on the collected salt precipitation image on the surface of the photovoltaic component to obtain a corrected picture; a denoising module for performing denoising processing on the corrected picture to obtain a denoised picture; an enhancement module for using a histogram equalization method to enhance the denoised picture to obtain an enhanced picture; a segmentation module for using an adaptive threshold binarization algorithm to segment the enhanced picture to obtain a processed salt precipitation image.

8. An electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that, The computer program is executed by the processor to implement the steps of the salt precipitation image recognition method on the surface of the marine floating photovoltaic component according to any one of claims 1-6.

9. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the steps of the salt precipitation image recognition method on the surface of the marine floating photovoltaic component according to any one of claims 1-6.

Images