A method and system for detecting surface defects of power equipment based on machine learning

Abstract

The present invention discloses a method and system for detecting surface defects of power equipment based on machine learning, which belongs to the field of defect detection technology. The method and system specifically include: setting a target area, obtaining infrared monitoring video and optical monitoring video of a transmission line in real time through a drone; performing frame processing on the infrared monitoring video according to a preset frame length, selecting any infrared image and inputting it into a preset equipment recognition model to obtain an equipment image, generating a positioning frame of any transmission line, obtaining the infrared temperature and positioning frame width of any transmission line, constructing a point map with the positioning frame width as the horizontal coordinate and the infrared temperature as the vertical coordinate, fitting the point map to obtain a fitting function, correcting the infrared temperature of any transmission line, determining a target infrared image, extracting an optical image corresponding to the target infrared image and substituting it into a preset defect recognition model to obtain an equipment defect recognition result of the transmission line.

Description

Technical Field

[0001] The present invention relates to the technical field of defect detection, and in particular to a method and system for detecting surface defects of power equipment based on machine learning. Background Art

[0002] Surface defect detection for power equipment is a crucial component of power grid safety and a key step in automated inspections by intelligent patrol systems. Surface defect detection technology can effectively improve the quality and efficiency of intelligent patrol inspections. Common approaches include: 1) Traditional algorithms: Traditional surface defect detection algorithms preprocess images to generate images that are easy to detect, then use statistical machine learning methods to extract image features to achieve defect detection. 2) Deep learning: Deep learning algorithms use deep convolutional neural networks (DCNNs) combined with SSD, Yolo, and other network methods to construct a coarse-to-fine cascade detection network, encompassing equipment location, defect detection, and classification.

[0003] In recent years, drone inspection technology has been gradually applied to the inspection of power transmission and distribution equipment. Drones collect real-time optical and infrared surveillance video of the inspection area, enabling remote monitoring of power transmission and distribution equipment. However, as the shooting distance increases, the area covered by each pixel in the infrared image also increases accordingly, resulting in a decrease in the proportion of the measured object in the field of view. This, in turn, reduces the quality of the infrared radiation output signal and reduces temperature measurement accuracy. Furthermore, the greater the distance, the greater the influence of air on infrared radiation, resulting in increased infrared temperature measurement errors, which may lead to misjudgments. Consequently, a large amount of computing power is often required to check the optical surveillance video, resulting in low actual efficiency. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for detecting surface defects of power equipment based on machine learning to solve the following technical problems:

[0005] In recent years, drone inspection technology has been gradually applied to the inspection of power transmission and distribution equipment. Drones collect real-time optical and infrared surveillance video of the inspection area, enabling remote monitoring of power transmission and distribution equipment. However, as the shooting distance increases, the area covered by each pixel in the infrared image also increases accordingly, resulting in a decrease in the proportion of the measured object in the field of view. This in turn reduces the quality of the infrared radiation output signal and the temperature measurement accuracy. Furthermore, the greater the distance, the greater the influence of air on infrared radiation, resulting in increased infrared temperature measurement errors, which may lead to misjudgments. Consequently, a large amount of computing power is often required to check the optical surveillance video, resulting in low efficiency.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for detecting surface defects of power equipment based on machine learning, characterized by comprising the following steps:

[0008] S1: Set the target area, obtain the power transmission lines in the target area and mark them as the inspection route of the drone. The drone can obtain infrared monitoring video and optical monitoring video of the power transmission lines in real time.

[0009] S2: Framing the infrared surveillance video according to a preset frame length to obtain an infrared image sequence y1, y2, ..., yN. Selecting any infrared image and inputting it into a preset device recognition model to obtain a device image, generating a positioning frame of any transmission line from the device image, obtaining the infrared temperature and positioning frame width of any transmission line, constructing a point map with the positioning frame width as the abscissa and the infrared temperature as the ordinate, and fitting the point map to obtain a fitting function T(D). The fitting function T(D) is used to fit the relationship between the infrared temperature and the positioning frame width.

[0010] S3, correcting the infrared temperature of any transmission line according to the fitting function T(D), determining the target infrared image according to the corrected infrared temperature, extracting the optical image corresponding to the target infrared image and marking it as the target optical image, substituting the target optical image into the preset defect recognition model to obtain the equipment defect recognition result of the transmission line.

[0011] As a further solution of the present invention: In S2, the specific process of generating a positioning frame of any transmission line is as follows:

[0012] Grayscale processing is performed on the device image to obtain a device grayscale image, edge detection is performed on the device grayscale image to obtain several edge areas, any edge area is selected and edge extraction is performed to obtain edge features, straight line features are extracted from the edge features and the Douglas–ρeucker algorithm is used to simplify the straight line features; any straight line segment is selected and the length and direction of the straight line segment are calculated to obtain the length and direction of all straight line segments, all straight line segments in the preset direction interval [0, 180] are obtained, the preset direction interval is divided into several sub-direction intervals according to a preset step size, and the straight line eigenvalue ρ and eigenvalue in any sub-direction interval are calculated in turn. Where m is the number of lines in sub-direction interval j, n is the number of all sub-direction intervals, and qi is the length of any line segment;

[0013] If the straight line eigenvalue ρ of any sub-direction interval is greater than the preset threshold, the sub-direction interval is marked as the target direction interval; the direction difference between the target direction intervals is calculated, and if the direction difference is 90 degrees, the edge area is marked as the background area; if there is no 90-degree difference between the target direction intervals, the edge area is marked as the transmission line area, a minimum circumscribed rectangle is drawn around the transmission line area, and the pixel rectangle is marked as the positioning frame of the transmission line.

[0014] As a further solution of the present invention: in S2, the infrared temperature of the transmission line is the highest temperature value in the transmission line area.

[0015] As a further solution of the present invention: S2 further includes screening the infrared image, and the specific screening process is:

[0016] Obtain the infrared temperature of all transmission lines in any infrared image, calculate the range C and temperature dispersion K corresponding to the infrared image, use the temperature dispersion as the horizontal coordinate and the range as the vertical coordinate to generate feature points corresponding to all infrared images, select any feature point as the center, set the clustering control radius R, obtain the feature point density ρ within the control radius R of any feature point, calculate the mean feature point density Avgρ, if there is any feature point with a density ρ greater than Avgρ within the radius R of the feature point, then use the feature point as the core point and generate a category cluster, generate several category clusters, obtain the number of feature points in any category cluster, select the category cluster with the largest number of feature points and mark it as the target category cluster, use the infrared image corresponding to the target category cluster as the target infrared image, obtain the infrared temperature of the transmission line and the positioning box width in any target infrared image for function fitting;

[0017] The specific calculation formula is:

[0018] C = MAXT - MINT;

[0019]

[0020] Where n is the total number of transmission lines in the infrared image, MAXT is the maximum temperature of the transmission lines in the infrared image, and MINT is the minimum temperature of the transmission lines in the infrared image.

[0021] As a further solution of the present invention, the specific process of setting the cluster control radius R is as follows:

[0022] Taking any feature point as the center, calculate the Euclidean distance d between the feature point and any other feature point, sum each Euclidean distance to get u, and get the control radius R based on the value u. The calculation formula is as follows:

[0023]

[0024] Among them, u is the sum of the Euclidean distance data values of all feature points, and d is the Euclidean distance between any feature points.

[0025] As a further solution of the present invention, if there is a non-core point in any category cluster and the density ρ within the control radius R is also greater than Avgρ, the category cluster generated by the non-core point is merged with the original category cluster to generate several category clusters.

[0026] As a further solution of the present invention: in S4, the specific process of determining the target infrared image is:

[0027] S11, determine the standard positioning frame width D 标 , obtain the positioning frame width Da and infrared temperature Ta of any transmission line, calculate D 标 The difference △H from Da is substituted into the fitting function T(D) to calculate the fitting temperature T', and the current infrared temperature Ta of the transmission line is summed with the fitting temperature T' to obtain the corrected temperature Tb;

[0028] S12, repeat the above step S11 to obtain the corrected temperature Tb of all transmission lines in any infrared image, and calculate the corrected temperature difference between any transmission line and all other transmission lines in turn. If any corrected temperature difference is greater than or equal to a preset threshold, the infrared image is calibrated as a target infrared image.

[0029] A system for detecting surface defects of power equipment based on machine learning, characterized by comprising:

[0030] The data acquisition module is used to set a target area, acquire the power transmission lines within the target area and mark them as the inspection route of the drone, and use the drone to obtain infrared monitoring video and optical monitoring video of the power transmission lines in real time;

[0031] A data analysis module is configured to perform frame processing on the infrared surveillance video according to a preset frame length to obtain an infrared image sequence y1, y2, ..., yN; select any infrared image and input it into a preset device recognition model to obtain a device image; generate a positioning frame of any transmission line from the device image; obtain the infrared temperature and positioning frame width of any transmission line; construct a point map with the positioning frame width as the horizontal coordinate and the infrared temperature as the vertical coordinate; and fit the point map to obtain a fitting function T(D); the fitting function T(D) is used to fit the relationship between the infrared temperature and the positioning frame width;

[0032] The result generation module is used to correct the infrared temperature of any transmission line according to the fitting function T(D), determine the target infrared image according to the corrected infrared temperature, extract the optical image corresponding to the target infrared image and mark it as the target optical image, substitute the target optical image into the preset defect recognition model, and obtain the equipment defect recognition result of the transmission line.

[0033] Beneficial effects of the present invention:

[0034] The present invention establishes an inspection route for a drone, collects infrared surveillance video and optical surveillance video of the inspection area in real time, and performs temperature correction by fitting a function between the positioning frame width and infrared temperature in the infrared image. It can be understood that the positioning frame width represents the shooting distance between the transmission line and the drone. The closer the shooting distance, the larger the positioning frame width, and the farther the shooting distance, the smaller the positioning frame width. At the same time, the transmission line is a three-phase device, so the surface temperature difference method can be used to compare and analyze the temperatures of corresponding parts of similar devices. When the hot spot characteristics of the infrared image of a phase device are significantly different from those of the other two phases, it can be determined that the phase is suspected to be faulty. However, during the actual shooting process, the shooting distance of each transmission line is different, so there is an infrared temperature error. The present invention constructs a point map with the positioning frame width as the horizontal axis and the infrared temperature as the vertical axis, and fits the point map to obtain a fitting function. The infrared temperature is corrected according to the fitting function, reducing the influence of environmental factors and shooting distance on the infrared temperature, improving the accuracy of the infrared temperature data, and then determining the target infrared image based on the corrected infrared temperature for preliminary defect screening, while the optical image is used for detail confirmation and defect confirmation. The combination of the two can more comprehensively assess the status of transmission line equipment and avoid misjudgments caused by a single image type. It first identifies possible risks of high temperature, fire and electric sparks through infrared monitoring, and then further accurately identifies them through optical images, which not only improves inspection efficiency but also saves computing power. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below with reference to the accompanying drawings.

[0036] Figure 1 This is a flow chart of a method for detecting surface defects of power equipment based on machine learning according to the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0038] See also Figure 1 As shown, the present invention is a method for detecting surface defects of power equipment based on machine learning, comprising the following steps:

[0039] S1: Set the target area, obtain the power transmission lines in the target area and mark them as the inspection route of the drone. The drone can obtain infrared monitoring video and optical monitoring video of the power transmission lines in real time.

[0040] S2: Framing the infrared surveillance video according to a preset frame length to obtain an infrared image sequence y1, y2, ..., yN. Selecting any infrared image and inputting it into a preset device recognition model to obtain a device image, generating a positioning frame of any transmission line from the device image, obtaining the infrared temperature and positioning frame width of any transmission line, constructing a point map with the positioning frame width as the abscissa and the infrared temperature as the ordinate, and fitting the point map to obtain a fitting function T(D). The fitting function T(D) is used to fit the relationship between the infrared temperature and the positioning frame width.

[0041] S3, correcting the infrared temperature of any transmission line according to the fitting function T(D), determining the target infrared image according to the corrected infrared temperature, extracting the optical image corresponding to the target infrared image and marking it as the target optical image, substituting the target optical image into the preset defect recognition model to obtain the equipment defect recognition result of the transmission line.

[0042] The present invention establishes an inspection route for a drone, collects infrared surveillance video and optical surveillance video of the inspection area in real time, and performs temperature correction by fitting a function between the positioning frame width and infrared temperature in the infrared image. It can be understood that the positioning frame width represents the shooting distance between the transmission line and the drone. The closer the shooting distance, the larger the positioning frame width, and the farther the shooting distance, the smaller the positioning frame width. At the same time, the transmission line is a three-phase device, so the surface temperature difference method can be used to compare and analyze the temperatures of corresponding parts of similar devices. When the hot spot characteristics of the infrared image of a phase device are significantly different from those of the other two phases, it can be determined that the phase is suspected to be faulty. However, during the actual shooting process, the shooting distance of each transmission line is different, so there is an infrared temperature error. The present invention constructs a point map with the positioning frame width as the horizontal axis and the infrared temperature as the vertical axis, and fits the point map to obtain a fitting function. The infrared temperature is corrected according to the fitting function, reducing the influence of environmental factors and shooting distance on the infrared temperature, improving the accuracy of the infrared temperature data, and then determining the target infrared image based on the corrected infrared temperature for preliminary defect screening, while the optical image is used for detail confirmation and defect confirmation. The combination of the two can more comprehensively assess the status of transmission line equipment and avoid misjudgments caused by a single image type. It first identifies possible risks of high temperature, fire and electric sparks through infrared monitoring, and then further accurately identifies them through optical images, which not only improves inspection efficiency but also saves computing power.

[0043] It is worth noting that scratches, wear or broken strands on the surface of the conductors in overhead transmission lines will destroy their integrity, increase resistance and cause local overheating. In addition, exposed conductors are prone to oxidation reactions, which further aggravate temperature anomalies. Therefore, for transmission lines with surface defects, local overheating will occur. At the same time, overhead lines are three-phase equipment, that is, the voltage of each transmission line is the same, so the actual temperature of each transmission line is roughly the same. However, the transmission lines in actual overhead transmission lines are not on the same horizontal plane, and the drone is inspected and photographed at a fixed height. Therefore, it is affected by the shooting distance and the actual environment, and there will be infrared temperature measurement errors, resulting in misjudgment. Therefore, the present invention determines all transmission line data during the inspection process, and uses the positioning frame width as the horizontal coordinate and the infrared temperature as the vertical coordinate to construct a point map, fit the point map, obtain a fitting function, and obtain the relationship between the infrared temperature and the positioning frame width.

[0044] According to the fitting function, the corrected temperature Tb of all transmission lines in any infrared image is obtained, and the corrected temperature difference between any transmission line and all other transmission lines is calculated in turn. If any corrected temperature difference is greater than or equal to a preset threshold, the infrared image is calibrated as a target infrared image. It can be understood that the overhead line is a three-phase device, that is, the voltage of each transmission line is the same, so the actual temperature of each transmission line is roughly the same. When there is a temperature difference greater than or equal to the preset threshold, it indicates that there is an abnormality in the corresponding transmission line in the infrared image. Therefore, the infrared image is calibrated as a target infrared image, and then the optical image corresponding to the target infrared image is extracted, and the target optical image is substituted into the preset defect recognition model to obtain the equipment defect recognition result of the transmission line. The preset defect recognition model is a convolutional neural network (CNN) model based on deep learning technology. The model can automatically extract features from infrared images and complete classification and recognition. It will not be repeated here, thereby greatly reducing the recognition computing power and improving the recognition efficiency.

[0045] In a preferred embodiment of the present invention, the specific process of generating a positioning frame of any transmission line in S2 is as follows:

[0046] Grayscale processing is performed on the device image to obtain a device grayscale image, edge detection is performed on the device grayscale image to obtain several edge areas, any edge area is selected and edge extraction is performed to obtain edge features, straight line features are extracted from the edge features and the Douglas–ρeucker algorithm is used to simplify the straight line features; any straight line segment is selected and the length and direction of the straight line segment are calculated to obtain the length and direction of all straight line segments, all straight line segments whose directions are within the preset direction interval [0, 180] are obtained, the preset direction interval is divided into several sub-direction intervals according to a preset step size, and the straight line eigenvalue ρ and eigenvalue in any sub-direction interval are calculated in turn. Where m is the number of lines in sub-direction interval j, n is the number of all sub-direction intervals, and qi is the length of any line segment;

[0047] If the straight line eigenvalue ρ of any sub-direction interval is greater than the preset threshold, the sub-direction interval is marked as the target direction interval; the direction difference between the target direction intervals is calculated, and if the direction difference is 90 degrees, the edge area is marked as the background area; if there is no 90-degree difference between the target direction intervals, the edge area is marked as the transmission line area, a minimum circumscribed rectangle is drawn around the transmission line area, and the pixel rectangle is marked as the positioning frame of the transmission line.

[0048] In another preferred embodiment of the present invention, in S2, the infrared temperature of the transmission line is the highest temperature value in the transmission line area.

[0049] In another preferred embodiment of the present invention, the step S2 further includes screening the infrared image, and the specific screening process is as follows:

[0050] Obtain the infrared temperature of all transmission lines in any infrared image, calculate the range C and temperature dispersion K corresponding to the infrared image, use the temperature dispersion as the horizontal coordinate and the range as the vertical coordinate to generate feature points corresponding to all infrared images, select any feature point as the center, set the clustering control radius R, obtain the feature point density ρ within the control radius R of any feature point, calculate the mean feature point density Avgρ, if there is any feature point with a density ρ greater than Avgρ within the radius R of the feature point, then use the feature point as the core point and generate a category cluster, generate several category clusters, obtain the number of feature points in any category cluster, select the category cluster with the largest number of feature points and mark it as the target category cluster, use the infrared image corresponding to the target category cluster as the target infrared image, obtain the infrared temperature of the transmission line and the positioning box width in any target infrared image for function fitting;

[0051] The specific calculation formula is:

[0052] C = MAXT - MINT;

[0053]

[0054] Where n is the total number of transmission lines in the infrared image, MAXT is the maximum temperature of the transmission lines in the infrared image, and MINT is the minimum temperature of the transmission lines in the infrared image.

[0055] In another preferred embodiment of the present invention, the specific process of setting the cluster control radius R is:

[0056] Taking any feature point as the center, calculate the Euclidean distance d between the feature point and any other feature point, sum each Euclidean distance to get u, and get the control radius R based on the value u. The calculation formula is as follows:

[0057]

[0058] Among them, u is the sum of the Euclidean distance data values of all feature points, and d is the Euclidean distance between any feature points.

[0059] In another preferred embodiment of the present invention, if there is a non-core point in any category cluster and the density ρ within the control radius R is also greater than Avgρ, the category cluster generated by the non-core point is merged with the original category cluster to generate several category clusters.

[0060] In another preferred embodiment of the present invention, in S4, the specific process of determining the target infrared image is:

[0061] S11, determine the standard positioning frame width D 标 , obtain the positioning frame width Da and infrared temperature Ta of any transmission line, calculate D 标 The difference △H from Da is substituted into the fitting function T(D) to calculate the fitting temperature T', and the current infrared temperature Ta of the transmission line is summed with the fitting temperature T' to obtain the corrected temperature Tb;

[0062] S12, repeat the above step S11 to obtain the corrected temperature Tb of all transmission lines in any infrared image, and calculate the corrected temperature difference between any transmission line and all other transmission lines in turn. If any corrected temperature difference is greater than or equal to a preset threshold, the infrared image is calibrated as a target infrared image.

[0063] A system for detecting surface defects of power equipment based on machine learning, comprising:

[0064] The data acquisition module is used to set a target area, acquire the power transmission lines within the target area and mark them as the inspection route of the drone, and use the drone to obtain infrared monitoring video and optical monitoring video of the power transmission lines in real time;

[0065] A data analysis module is configured to perform frame processing on the infrared surveillance video according to a preset frame length to obtain an infrared image sequence y1, y2, ..., yN; select any infrared image and input it into a preset device recognition model to obtain a device image; generate a positioning frame of any transmission line from the device image; obtain the infrared temperature and positioning frame width of any transmission line; construct a point map with the positioning frame width as the horizontal coordinate and the infrared temperature as the vertical coordinate; and fit the point map to obtain a fitting function T(D); the fitting function T(D) is used to fit the relationship between the infrared temperature and the positioning frame width;

[0066] The result generation module is used to correct the infrared temperature of any transmission line according to the fitting function T(D), determine the target infrared image according to the corrected infrared temperature, extract the optical image corresponding to the target infrared image and mark it as the target optical image, substitute the target optical image into the preset defect recognition model, and obtain the equipment defect recognition result of the transmission line.

[0067] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for detecting surface defects of power equipment based on machine learning, characterized in that: The following steps are involved: S1: Set the target area, obtain the power transmission lines in the target area and mark them as the inspection route of the drone. The drone can obtain infrared monitoring video and optical monitoring video of the power transmission lines in real time. S2: Framing the infrared surveillance video according to a preset frame length to obtain an infrared image sequence y1, y2, ..., yN. Selecting any infrared image and inputting it into a preset device recognition model to obtain a device image, generating a positioning frame of any transmission line from the device image, obtaining the infrared temperature and positioning frame width of any transmission line, constructing a point map with the positioning frame width as the abscissa and the infrared temperature as the ordinate, and fitting the point map to obtain a fitting function T(D). The fitting function T(D) is used to fit the relationship between the infrared temperature and the positioning frame width. S3, correcting the infrared temperature of any transmission line according to the fitting function T(D), determining a target infrared image according to the corrected infrared temperature, extracting an optical image corresponding to the target infrared image and marking it as a target optical image, substituting the target optical image into a preset defect recognition model to obtain an equipment defect recognition result for the transmission line; In S3, the specific process of determining the target infrared image is as follows: S11, determine the standard positioning frame width D 标 , obtain the positioning frame width Da and infrared temperature Ta of any transmission line, calculate D 标 The difference △H from Da is substituted into the fitting function T(D) to calculate the fitting temperature T', and the current infrared temperature Ta of the transmission line is summed with the fitting temperature T' to obtain the corrected temperature Tb; S12, repeat the above step S11 to obtain the corrected temperature Tb of all transmission lines in any infrared image, and calculate the corrected temperature difference between any transmission line and all other transmission lines in turn. If any corrected temperature difference is greater than or equal to a preset threshold, the infrared image is calibrated as a target infrared image.

2. The method for detecting surface defects of power equipment based on machine learning according to claim 1, characterized in that: In the above S2, the infrared temperature of the transmission line is the highest temperature value in the transmission line area.

3. The method for detecting surface defects of power equipment based on machine learning according to claim 2, characterized in that: The step S2 also includes screening the infrared image. The specific screening process is as follows: Obtain the infrared temperature of all transmission lines in any infrared image, calculate the range C and temperature dispersion K corresponding to the infrared image, use the temperature dispersion as the horizontal coordinate and the range as the vertical coordinate to generate feature points corresponding to all infrared images, select any feature point as the center, set the clustering control radius R, obtain the feature point density ρ1 within the control radius R of any feature point, calculate the mean feature point density Avgρ, if there is any feature point with a density ρ1 greater than Avgρ within the radius R of the feature point, then use the feature point as the core point and generate a category cluster, generate several category clusters, obtain the number of feature points in any category cluster, select the category cluster with the largest number of feature points and mark it as the target category cluster, use the infrared image corresponding to the target category cluster as the target infrared image, obtain the infrared temperature of the transmission line and the positioning box width in any target infrared image for function fitting; The specific calculation formula is: C = MAXT - MINT; Where n is the total number of transmission lines in the infrared image, MAXT is the maximum temperature of the transmission lines in the infrared image, and MINT is the minimum temperature of the transmission lines in the infrared image.

4. The method for detecting surface defects of power equipment based on machine learning according to claim 3, characterized in that: The specific process of setting the cluster control radius R is: Taking any feature point as the center, calculate the Euclidean distance d between the feature point and any other feature point, sum each Euclidean distance to get u, and get the control radius R based on the value u. The calculation formula is as follows: Among them, u is the sum of the Euclidean distance data values of all feature points, and d is the Euclidean distance between any feature points.

5. The method for detecting surface defects of power equipment based on machine learning according to claim 3, characterized in that: It also includes merging the category cluster generated by the non-core point with the original category cluster to generate several category clusters if the density ρ2 within the control radius R of any category cluster is also greater than Avgρ.

6. A detection system for surface defects of power equipment based on machine learning, characterized in that: include: The data acquisition module is used to set a target area, acquire the power transmission lines within the target area and mark them as the inspection route of the drone, and use the drone to obtain infrared monitoring video and optical monitoring video of the power transmission lines in real time; A data analysis module is configured to perform frame processing on the infrared surveillance video according to a preset frame length to obtain an infrared image sequence y1, y2, ..., yN; select any infrared image and input it into a preset device recognition model to obtain a device image; generate a positioning frame of any transmission line from the device image; obtain the infrared temperature and positioning frame width of any transmission line; construct a point map with the positioning frame width as the horizontal coordinate and the infrared temperature as the vertical coordinate; and fit the point map to obtain a fitting function T(D); the fitting function T(D) is used to fit the relationship between the infrared temperature and the positioning frame width; A result generation module is used to correct the infrared temperature of any transmission line according to the fitting function T(D), determine the target infrared image according to the corrected infrared temperature, extract the optical image corresponding to the target infrared image and mark it as the target optical image, substitute the target optical image into a preset defect recognition model, and obtain the equipment defect recognition result of the transmission line; In the result generation module, the specific process of determining the target infrared image is as follows: S11, determine the standard positioning frame width D 标 , obtain the positioning frame width Da and infrared temperature Ta of any transmission line, calculate D 标 The difference △H from Da is substituted into the fitting function T(D) to calculate the fitting temperature T', and the current infrared temperature Ta of the transmission line is summed with the fitting temperature T' to obtain the corrected temperature Tb; S12, repeat the above step S11 to obtain the corrected temperature Tb of all transmission lines in any infrared image, and calculate the corrected temperature difference between any transmission line and all other transmission lines in turn. If any corrected temperature difference is greater than or equal to a preset threshold, the infrared image is calibrated as a target infrared image.

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