CN111008967A - Insulator RTV coating defect identification method - Google Patents
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- CN111008967A CN111008967A CN201911226484.0A CN201911226484A CN111008967A CN 111008967 A CN111008967 A CN 111008967A CN 201911226484 A CN201911226484 A CN 201911226484A CN 111008967 A CN111008967 A CN 111008967A
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- 239000012212 insulator Substances 0.000 title claims abstract description 155
- 239000011248 coating agent Substances 0.000 title claims abstract description 96
- 238000000576 coating method Methods 0.000 title claims abstract description 96
- 230000007547 defect Effects 0.000 title claims abstract description 68
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000007781 pre-processing Methods 0.000 claims abstract description 11
- 238000004364 calculation method Methods 0.000 claims abstract description 10
- 238000003709 image segmentation Methods 0.000 claims abstract description 9
- 229910000831 Steel Inorganic materials 0.000 claims description 16
- 239000010959 steel Substances 0.000 claims description 16
- 238000001914 filtration Methods 0.000 claims description 10
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 description 6
- MXWJVTOOROXGIU-UHFFFAOYSA-N atrazine Chemical compound CCNC1=NC(Cl)=NC(NC(C)C)=N1 MXWJVTOOROXGIU-UHFFFAOYSA-N 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 229910052573 porcelain Inorganic materials 0.000 description 2
- 229920002379 silicone rubber Polymers 0.000 description 2
- 239000004945 silicone rubber Substances 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 150000003839 salts Chemical class 0.000 description 1
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T7/0002—Inspection of images, e.g. flaw detection
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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Abstract
The application belongs to the technical field of image processing, and particularly relates to a method for identifying defects of an insulator RTV coating. The existing insulator RTV coating defect identification method needs to be detected on site, and is complex to operate. The application provides a method for identifying insulator RTV coating defects, which comprises the following steps: 1) acquiring an image containing insulator RTV coating defects; 2) preprocessing the image containing the insulator RTV coating defects; so as to improve the accuracy of subsequent insulator image segmentation and calculation of the ratio of the falling area of the RTV coating. 3) And segmenting the preprocessed insulator image into a binary image, and segmenting an insulator region and an RTV coating defect region. According to the insulator RTV coating defect identification method, the insulator image is segmented, and a normal insulator and an RTV coating falling area can be obtained.
Description
Technical Field
The application belongs to the technical field of image processing, and particularly relates to a method for identifying defects of an insulator RTV coating.
Background
Room Temperature vulcanized Silicone Rubber (Room Temperature Silicone Rubber) is abbreviated as RTV anti-pollution flashover coating, the natural pollution accumulation phenomenon on the surface of electric porcelain of outdoor equipment of an electric power system is inevitable, with the development of national economy in China, the atmospheric pollution is increasingly serious, the pollution sources in various regions are continuously increased, and the salt density value range is increased year by year. On the contrary, the porcelain insulation configuration is generally low, the number of lines increases year by year, the number of pollution flashover prevention professionals is low, and the possibility of pollution flashover is higher and higher in consideration of the factors of overvoltage operation, unstable climate change, bird damage and the like.
Under severe weather conditions (such as fog, dew, rain, and the like), flashover can occur along the wet insulator surface, causing pollution flashover accidents of the power system. The pollution flashover threatens the safe and stable operation of a power system, light people influence local power supply, heavy people can influence a power grid, and even the whole power grid is cracked. The influence of the pollution flashover on a power supply system causes huge loss to national economy.
The insulator RTV coating defects are identified, so that the severity of the insulator RTV coating defects is effectively represented, and a theoretical basis is provided for whether the insulator needs to be re-coated with RTV coating or replaced. However, the existing insulator RTV coating defect identification method needs to be carried out on site for detection operation and is relatively complex.
Disclosure of Invention
1. Technical problem to be solved
Based on the identification of the insulator RTV coating defects, the severity of the insulator RTV coating defects is effectively represented, and a theoretical basis is provided for whether the insulator needs to be re-coated with RTV coating or replaced. However, the existing insulator RTV coating defect identification method needs to be carried out on site to detect and operate more complicated problems, and the application provides the insulator RTV coating defect identification method.
2. Technical scheme
In order to achieve the above object, the present application provides a method for identifying defects of an insulator RTV coating, the method comprising the steps of:
1) acquiring an image containing insulator RTV coating defects;
2) preprocessing the image containing the insulator RTV coating defects so as to improve the accuracy of subsequent insulator image segmentation and calculation of the falling area ratio of the RTV coating;
3) and segmenting the preprocessed insulator image into a binary image, and segmenting an insulator region and an RTV coating defect region.
Another embodiment of the present application is: the image in the step 1) is from an aerial image shot in a close distance on site and containing an RTV coating defect or an insulator image shot in a laboratory and containing an RTV coating defect.
Another embodiment of the present application is: the insulator image type includes insulator model information. The color of the insulator body is not limited.
Another embodiment of the present application is: the preprocessing in the step 2) comprises graying the image and filtering the image median, removing noise, improving the image quality and improving the accuracy of the insulator image segmentation. And calculating the falling area ratio of the RTV coating of the insulator by counting the number of pixel points in the image.
Another embodiment of the present application is: the conversion formula of the image graying is as follows:
Y＝0.2989*R+0.5870*G+0.1140*B
RGB is the three components in a color image, R represents the red component, G represents the green component, and B represents the blue component. Y is a gray value of the gray image.
Another embodiment of the present application is: and 3) segmenting the preprocessed insulator image into a binary image by using a maximum inter-class variance method in the step 3), and segmenting an insulator sub-region and an RTV coating defect region.
Another embodiment of the present application is: the maximum inter-class variance method comprises traversing the value space of the image gray level, and calculating the inter-class variance corresponding to each threshold value to make the inter-class variance maximum, namely the optimal threshold value T; and selecting a threshold value T to carry out binarization on the gradient value of the image.
Another embodiment of the present application is: the method further comprises the steps of counting the number of black and white pixel points of the binary image and calculating the falling area ratio of the RTV coating of the insulator.
Another embodiment of the present application is: the falling area ratio of the RTV coating of the insulator is the ratio of the front surface or the back surface of the insulator to the insulating area of the insulator.
Because there is the steel cap part in the middle region of insulator, this part does not possess insulating properties, the final purpose of this patent is that calculation insulator RTV coating dropout area ratio, and this ratio is the proportion that insulator front or back account for insulator insulating zone, does not include insulator middle steel cap part region, and this kind of calculation mode can effectual sign insulator RTV coating defect's severity.
Another embodiment of the present application is: the calculation comprises the following steps:
a. calculating the coordinates (x, y) of the center of the insulator body in the binarized image, wherein x is (max (x)) + min (x))/2, and y is (max (y)) + min (y))/2;
b. calculating the radius R ═ (max (x) -min (x))/2 of the insulator in the binarized image;
c. calculating the radius R of the steel cap1＝R×d；
d. Filling the steel cap area into black by using a filling function, counting the number of black pixel points in the graph, and recording the number as A;
e. calculating the area of the steel cap region as S ═ pi R1 2；
f. Deleting the falling region of the insulator RTV coating in the image through a filtering function to make the whole insulator region black, counting the number of black pixel points in the image, and recording the number as B;
g. the Ratio of the insulator RTV coating peeling area to (B-A)/(B-S). times.100% is calculated.
The center coordinates of the insulator body are (x, y), the radius of the insulator is R, and the percentage of the diameter of the steel cap in the diameter of the insulator disc is d.
3. Advantageous effects
Compared with the prior art, the insulator RTV coating defect identification method provided by the application has the beneficial effects that:
according to the insulator RTV coating defect identification method, the insulator image is segmented, and a normal insulator and an RTV coating falling area can be obtained.
The application provides a pair of insulator RTV coating defect recognition method, through the image that contains insulator RTV coating defect of gathering, handle the discernment to the image, obtain insulator RTV coating defect and area of occupation thereof, easy operation need not go the scene in addition and just can realize the discernment to insulator RTV coating defect, represents the severity of insulator RTV coating defect through insulator RTV coating defect area of occupation.
Drawings
FIG. 1 is a schematic diagram of a method for identifying defects in an insulator RTV coating according to the present application;
fig. 2 is a schematic diagram of the results of the present application.
Detailed Description
Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings, and it will be apparent to those skilled in the art from this detailed description that the present application can be practiced. Features from different embodiments may be combined to yield new embodiments, or certain features may be substituted for certain embodiments to yield yet further preferred embodiments, without departing from the principles of the present application.
The OTSU algorithm is also called a maximum inter-class difference method, sometimes called as the atrazine algorithm, proposed by atrazine in 1979, is considered as an optimal algorithm for threshold selection in image segmentation, is simple in calculation, and is not affected by image brightness and contrast, so that the OTSU algorithm is widely applied to digital image processing. The image is divided into a background part and a foreground part according to the gray characteristic of the image. The variance is a measure of the uniformity of the gray distribution, and the larger the inter-class variance between the background and the foreground is, the larger the difference between the two parts constituting the image is, and when part of the foreground is mistaken for the background or part of the background is mistaken for the foreground, the difference between the two parts is reduced. Thus, a segmentation that maximizes the inter-class variance means that the probability of false positives is minimized.
Referring to fig. 1-2, the application provides a method for identifying defects of an insulator RTV coating, which includes the following steps:
1) acquiring an image containing insulator RTV coating defects;
2) preprocessing the image containing the insulator RTV coating defects;
3) and segmenting the preprocessed insulator image into a binary image, and segmenting an insulator region and an RTV coating defect region.
Further, the image in the step 1) is from an aerial image of the insulator containing the RTV coating defect in a close distance on site or a laboratory shot image of the insulator containing the RTV coating defect.
Further, the insulator image type includes insulator model information.
Further, the preprocessing in the step 2) includes graying the image and filtering the image median, so as to remove noise, improve the image quality and improve the accuracy of the insulator image segmentation.
The premise of calculating the defect area ratio of the RTV coating of the insulator is to determine the region where the insulator is located, namely, the original image needs to be segmented, so that the insulator region is separated from the background. Because the original image may have noise or other interference factors of different degrees, which affect the accuracy of subsequent insulator image segmentation identification, a series of preprocessing operations need to be performed on the original image before the image segmentation operation is performed, and the purpose of image preprocessing is to reduce noise, improve image quality, and retain RTV coating defect information in the original image as much as possible. The preprocessing method adopted by the application is divided into image graying and image median filtering. The two methods can reduce interference and improve calculation efficiency for the later insulator image segmentation work.
Image graying
The acquired original image is generally a color image, and the process of converting the color image into a gray image is called image graying processing. The color of each pixel in the color image is determined by R, G, B three components, and the range of each component is 0-255, so the color image needs to be converted into a gray image to reduce subsequent calculation amount, and the description of the gray image can still reflect the distribution and the characteristics of the chromaticity and the brightness level of the image like the color image.
Image median filtering
Due to the variability of the illumination conditions when the images are acquired, the acquired images have different degrees of noise, and the noise can make the gray values uniformly and continuously distributed in the images suddenly become larger or smaller at a certain point, so that the algorithm identifies false RTV coating defects. In order to reduce the influence of noise and improve the image quality, the grayscale image needs to be subjected to median filtering. The median filtering is to replace the value of one point in the digital image with the median of each point value in one field of the point, and to change the pixel with larger difference of the gray value of the surrounding pixels to the value close to the value of the surrounding pixels, thereby eliminating the isolated noise point, and achieving the purposes of removing the noise and protecting the edge information of the insulator.
Further, the conversion formula of the image graying is as follows:
Y＝0.2989*R+0.5870*G+0.1140*B
where R represents a red component, G represents a green component, and B represents a blue component. Y is a gray value of the gray image.
Further, in the step 3), the preprocessed insulator image is segmented into a binary image by using a maximum inter-class variance method, and an insulator sub-region and an RTV coating defect region are segmented.
Furthermore, the maximum inter-class variance method comprises traversing the value space of the image gray level, and calculating the inter-class variance corresponding to each threshold value to make the inter-class variance maximum, namely the optimal threshold value T; and selecting a threshold value T to carry out binarization on the gradient value of the image.
Further, the number of black and white pixel points of the binary image is counted, and the falling area ratio of the insulator RTV coating is calculated.
Furthermore, the ratio of the falling area of the RTV coating of the insulator to the insulating area of the insulator is the ratio of the front surface or the back surface of the insulator to the insulating area of the insulator.
Further, the calculating comprises the steps of:
a. calculating the coordinates (x, y) of the center of the insulator body in the binarized image, wherein x is (max (x)) + min (x))/2, and y is (max (y)) + min (y))/2;
b. calculating the radius R ═ (max (x) -min (x))/2 of the insulator in the binarized image;
c. calculating the radius R of the steel cap1＝R×d；
d. Filling the steel cap area into black by using a filling function, counting the number of black pixel points in the graph, and recording the number as A;
e. calculating the area of the steel cap region as S ═ pi R1 2；
f. Deleting the falling region of the insulator RTV coating in the image through a filtering function to make the whole insulator region black, counting the number of black pixel points in the image, and recording the number as B;
g. the Ratio of the insulator RTV coating peeling area to (B-A)/(B-S). times.100% is calculated.
The center coordinates of the insulator body are (x, y), the radius of the insulator is R, and the percentage of the diameter of the steel cap in the diameter of the insulator disc is d.
In fig. 2, the original image is a color image.
After the original image is preprocessed, the insulator region needs to be segmented from the image in the next step. The preprocessed insulator region can be well segmented from the image by using an Otsu threshold segmentation algorithm, pixel levels are divided into a plurality of classes by setting a threshold value by utilizing the difference of the insulator and the background on gray scale, whether the gray scale value of each pixel point in the image meets the requirement of the threshold value is judged, and whether the pixel point in the image belongs to a target region is determined, namely, the insulator and the background are separated. When the insulator RTV coating falls off, the falling region can expose the insulator body, the color difference between the insulator body and the RTV coating is large, namely the gray scale difference between the insulator body and the RTV coating in an image is large, and therefore a normal insulator region and the RTV coating falling region can be distinguished by using an Otsu threshold segmentation algorithm.
The Otsu threshold segmentation algorithm steps are as follows:
setting the size of the gray picture as w x h, namely, the number of pixels of the picture as w x h, setting the segmentation threshold of the foreground and the background as T, setting all pixel points with the gray value smaller than T as the foreground, and setting all pixel points with the gray value larger than T as the background. Let the ratio of foreground pixels to image be ω 0, and the average gray scale thereof be μ 0, and the ratio of background pixels to image be ω 1, and the average gray scale thereof be μ 1. The overall average gray level of the image is mu, and the inter-class variance is g, then:
ω0+ω1＝1 (1)
μ＝ω0×μ0+ω1×μ1 (2)
the available between-class variance is:
g＝ω0×(μ0-μ)2+ω1×(μ1-μ)2(3)
the simultaneous expression is as follows:
g＝ω0×ω1×(μ0-μ1)2(4)
traversing the value space of the image gray level, and calculating the inter-class variance corresponding to each threshold value to make the inter-class variance g maximum, namely the optimal threshold value T.
Selecting a threshold value T to carry out binarization on the gradient value of the image:
the image g (x, y) obtained by dividing the image f (x, y) can be obtained from the equation (5).
Because there is the steel cap part in the middle region of insulator, this part does not possess insulating properties, the final purpose of this patent is that the insulator RTV coating area that calculates accounts for the ratio, and this accounts for the proportion of insulator front or back in insulator insulating region, does not include insulator middle steel cap part region, and this kind of calculation mode can effectual sign insulator RTV coating defect's severity.
And (3) processing the original image by the preprocessing method and an Otsu threshold segmentation algorithm to obtain a binary image, wherein the normal insulator region in the binary image is represented as black, and the RTV coating stripping region and the background are white.
According to the insulator RTV coating defect identification method, the insulator image is segmented, and a normal insulator and an RTV coating falling area can be obtained.
The application provides a pair of insulator RTV coating defect recognition method, through the image that contains insulator RTV coating defect of gathering, handle the discernment to the image, obtain insulator RTV coating defect and area of occupation thereof, easy operation need not go the scene in addition and just can realize the discernment to insulator RTV coating defect, represents the severity of insulator RTV coating defect through insulator RTV coating defect area of occupation.
Although the present application has been described above with reference to specific embodiments, those skilled in the art will recognize that many changes may be made in the configuration and details of the present application within the principles and scope of the present application. The scope of protection of the application is determined by the appended claims, and all changes that come within the meaning and range of equivalency of the technical features are intended to be embraced therein.
Claims (10)
1. The method for identifying the defects of the insulator RTV coating is characterized by comprising the following steps: the method comprises the following steps:
1) acquiring an image containing insulator RTV coating defects;
2) preprocessing the image containing the insulator RTV coating defects;
3) and segmenting the preprocessed insulator image into a binary image, and segmenting an insulator region and an RTV coating defect region.
2. The insulator RTV coating defect identification method of claim 1, characterized in that: the image in the step 1) is from an aerial image shot in a close distance on site and containing an RTV coating defect or an insulator image shot in a laboratory and containing an RTV coating defect.
3. The insulator RTV coating defect identification method of claim 2, characterized in that: the insulator image type includes insulator model information.
4. The insulator RTV coating defect identification method of claim 1, characterized in that: the preprocessing in the step 2) comprises graying the image and filtering the image median, removing noise, improving the image quality and improving the accuracy of the insulator image segmentation.
5. The insulator RTV coating defect identification method of claim 4, characterized in that: the conversion formula of the image graying is as follows:
Y＝0.2989*R+0.5870*G+0.1140*B
where R represents a red component, G represents a green component, B represents a blue component, and Y is a gray value of a gray image.
6. The insulator RTV coating defect identification method of claim 1, characterized in that: and 3) segmenting the preprocessed insulator image into a binary image by using a maximum inter-class variance method in the step 3), and segmenting an insulator sub-region and an RTV coating defect region.
7. The insulator RTV coating defect identification method of claim 6, characterized in that: the maximum inter-class variance method comprises traversing the value space of the image gray level, and calculating the inter-class variance corresponding to each threshold value to make the inter-class variance maximum, namely the optimal threshold value T; and selecting a threshold value T to carry out binarization on the gradient value of the image.
8. The insulator RTV coating defect identification method of any one of claims 1 to 7, characterized in that: the method further comprises the steps of counting the number of black and white pixel points of the binary image and calculating the falling area ratio of the RTV coating of the insulator.
9. The insulator RTV coating defect identification method of claim 8, characterized in that: the falling area ratio of the RTV coating of the insulator is the ratio of the front surface or the back surface of the insulator to the insulating area of the insulator.
10. The insulator RTV coating defect identification method of claim 8, characterized in that: the calculation comprises the following steps:
a. calculating the coordinates (x, y) of the center of the insulator body in the binarized image, wherein x is (max (x)) + min (x))/2, and y is (max (y)) + min (y))/2;
b. calculating the radius R ═ (max (x) -min (x))/2 of the insulator in the binarized image;
c. calculating the radius R of the steel cap1＝R×d；
d. Filling the steel cap area into black by using a filling function, counting the number of black pixel points in the graph, and recording the number as A;
e. calculating the area of the steel cap region as S ═ pi R1 2；
f. Deleting the falling region of the insulator RTV coating in the image through a filtering function to make the whole insulator region black, counting the number of black pixel points in the image, and recording the number as B;
g. the Ratio of the insulator RTV coating peeling area to (B-A)/(B-S). times.100% is calculated.
The center coordinates of the insulator body are (x, y), the radius of the insulator is R, and the percentage of the diameter of the steel cap in the diameter of the insulator disc is d.
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| CN111638218A (en) * | 2020-05-14 | 2020-09-08 | 哈尔滨工程大学 | Method for detecting surface defects of coating |
| CN111739032A (en) * | 2020-06-22 | 2020-10-02 | 北京市测绘设计研究院 | Greening area calculation method and system based on city street view |
| CN116679171A (en) * | 2023-05-15 | 2023-09-01 | 江苏云峰科技股份有限公司 | Insulation state judging system of insulation piece of wind power generation switch |
| CN116679171B (en) * | 2023-05-15 | 2023-11-10 | 江苏云峰科技股份有限公司 | Insulation state judging system of insulation piece of wind power generation switch |
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