Power transmission line foreign matter detection method, electronic device, and computer-readable storage medium

Abstract

The application is suitable for the technical field of electric power, and provides a power transmission line foreign matter detection method, device and electronic equipment. The power transmission line foreign matter detection method collects an image to be recognized containing a power transmission line to be detected at a preset position by using a drone, and then performs binaryzation processing on the image to be recognized to obtain a binaryzation image containing a power transmission line area. According to the boundary feature of the boundary line of the power transmission line area, whether foreign matter is attached to the power transmission line can be determined by matching the boundary feature with a preset boundary feature. The power transmission line foreign matter detection method can detect foreign matter on the power transmission line through the boundary feature of the power transmission line area, does not need to use a responsible neural network model, and has high detection efficiency.

Description

Technical Field

[0001] This application belongs to the field of power technology, and in particular relates to methods, devices and electronic equipment for detecting foreign objects in transmission lines. Background Technology

[0002] Foreign objects attached to power transmission lines can easily cause short circuits, leading to accidents such as power outages. Therefore, cleaning foreign objects from power transmission lines is essential for the maintenance of power distribution lines, and the detection of foreign objects during the cleaning process is the primary condition for the cleaning process.

[0003] However, due to the extremely wide distribution of power transmission lines, and the diverse and complex types of foreign objects adhering to these lines, the detection of foreign objects becomes quite difficult. Therefore, it is necessary to design a foreign object detection method for power transmission lines to improve the detection efficiency. Summary of the Invention

[0004] To overcome the problems existing in related technologies, embodiments of this application provide a method, apparatus and electronic equipment for detecting foreign objects in power transmission lines.

[0005] This application is achieved through the following technical solution:

[0006] In a first aspect, embodiments of this application provide a method for detecting foreign objects on power transmission lines, comprising: acquiring an image to be identified containing a power transmission line to be detected, the image to be identified being acquired by a drone at a preset location; performing binarization processing on the image to be identified to obtain a binarized image, the binarized image containing a power transmission line region, the binarized image including multiple first pixels with first gray values ​​and second pixels with second gray values, the region composed of the first pixels containing the power transmission line region; acquiring the boundary features of the boundary line of the power transmission line region, and determining whether the power transmission line is attached to a foreign object based on the boundary features and preset boundary features.

[0007] The aforementioned method for detecting foreign objects on power transmission lines involves using a drone to acquire an image of the transmission line to be detected at a predetermined location. This image is then binarized to obtain a binarized image of the region containing the transmission line. By matching the boundary features of this region with predetermined boundary features, the presence of foreign objects on the transmission line can be determined. This method detects foreign objects on power transmission lines using only the boundary features of the transmission line region, eliminating the need for complex neural network models and resulting in high detection efficiency.

[0008] In conjunction with the first aspect, in some embodiments, the binarization processing of the image to be identified to obtain a binarized image includes: scanning the image to be identified using a scanning window of a preset size; if the gray value of a pixel in the image to be identified corresponding to the center of the scanning window is less than the gray value of some surrounding pixels, then setting the gray value of the pixel corresponding to the center of the scanning window to a first gray value; if the gray value of a pixel in the image to be identified corresponding to the center of the scanning window is greater than the gray value of some surrounding pixels, then setting the gray value of the pixel corresponding to the center of the scanning window to a second gray value; for the outermost pixels in the image to be identified, comparing the gray value of the pixel with a gray value threshold, if the gray value of the pixel is greater than or equal to the gray value threshold, then setting the gray value of the pixel to a second gray value; if the gray value of the pixel is less than the gray value threshold, then setting the gray value of the pixel to a first gray value.

[0009] In conjunction with the first aspect, in some embodiments, the grayscale threshold is determined based on the time of acquisition of the image to be identified and the cloud cover, with each pair of time and cloud cover corresponding to a grayscale threshold; wherein, the stronger the sunlight corresponding to each pair of time and cloud cover, the smaller the grayscale threshold; and the weaker the sunlight corresponding to each pair of time and cloud cover, the larger the grayscale threshold.

[0010] In conjunction with the first aspect, in some embodiments, the process of determining the transmission line region includes: determining a plurality of target regions composed of pixels with a grayscale value of a second grayscale value; and determining the transmission line region based on the size, shape, and angles between the plurality of target regions.

[0011] In conjunction with the first aspect, in some embodiments, the utility pole corresponds to a first preset pixel size and a first preset shape, and the power transmission line corresponds to a second preset pixel size and a second preset shape. The angle between the utility pole and the power transmission line support is a preset angle. Determining the power transmission line region based on the size, shape, and angle between the plurality of target regions includes: comparing the size of each target region with the first preset size and the second preset size; comparing the shape of each target region with the first preset shape and the second preset shape; determining the first target region corresponding to the utility pole and the second target region corresponding to the power transmission line; determining the angle between the first target region and the second target region; if the angle is equal to the preset angle, then the second target region is determined to be the region corresponding to the power transmission line.

[0012] In conjunction with the first aspect, in some embodiments, determining the angle between the first target area and the second target area includes: acquiring the shooting position and shooting angle of the drone; finding a preset angle corresponding to the shooting position and the shooting angle based on the shooting position and the shooting angle, and determining the angle information between the power pole and the transmission line, wherein the preset angle is the theoretical angle between the power pole and the transmission line at the shooting position and the shooting angle.

[0013] In conjunction with the first aspect, in some embodiments, obtaining the boundary features of the boundary line of the transmission line area and determining whether the transmission line is attached to foreign objects based on the boundary features and preset boundary features includes: determining the boundary features of the boundary line of the transmission line area, wherein the boundary features include: the boundary line is straight, the boundary line is a smooth curve, and the boundary line is a straight line or a curve with abrupt changes; if the boundary features indicate that the boundary line of the transmission line area is straight or a smooth curve, then it is determined that the transmission line is free of foreign objects; if the boundary features indicate that the boundary line of the transmission line area is a straight line or a curve with abrupt changes, then it is determined that the transmission line is attached to foreign objects, and the type of foreign object is determined based on the shape of the abrupt change portion.

[0014] In conjunction with the first aspect, in some embodiments, before binarizing the image to be identified, the method further includes scaling the image to be identified. Scaling the image to be identified includes: converting the image to be identified into a grayscale image; extracting pixels corresponding to preset rows and preset columns in the grayscale image; and combining the extracted pixels according to their positional relationships in the original image to generate a new image to be identified.

[0015] Secondly, embodiments of this application provide a foreign object detection device for power transmission lines, including an image acquisition module, a binarization processing module, and a foreign object recognition module. The image acquisition module acquires an image to be identified containing the power transmission line to be detected. The image to be identified is acquired by a drone at a preset location. The binarization processing module performs binarization processing on the image to be identified to obtain a binarized image. The binarized image contains a power transmission line region and includes multiple first pixels with first grayscale values ​​and second pixels with second grayscale values. The region composed of the first pixels contains the power transmission line region. The foreign object recognition module acquires the boundary features of the boundary line of the power transmission line region and determines whether a foreign object is attached to the power transmission line based on the boundary features and preset boundary features.

[0016] Thirdly, embodiments of this application provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the transmission line foreign object detection method as described in any of the first aspects.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the foreign object detection method for transmission lines as described in any of the first aspects.

[0018] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to execute the transmission line foreign object detection method described in any of the first aspects.

[0019] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this specification. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic flowchart of a foreign object detection method for power transmission lines provided in an embodiment of this application;

[0023] Figure 2 This is a schematic diagram of a utility pole and transmission line provided in one embodiment of this application;

[0024] Figure 3 This is a schematic flowchart of a foreign object detection method for power transmission lines provided in an embodiment of this application;

[0025] Figure 4 This is a schematic diagram of a scene where a scanning window scans an image to be recognized, according to an embodiment of this application.

[0026] Figure 5 This is a schematic diagram of the pixels corresponding to the scanning window provided in one embodiment of this application;

[0027] Figure 6This is a schematic diagram of the angle between a utility pole and a power transmission line provided in one embodiment of this application;

[0028] Figure 7 This is a flowchart illustrating the foreign object detection method for power transmission lines provided in the embodiments of this application;

[0029] Figure 8(a) and 8(b) This is a schematic diagram of the boundary line of the transmission line area provided in the embodiments of this application;

[0030] Figure 9(a) and 9(b) This is a schematic diagram of an image scaling scenario provided in an embodiment of this application;

[0031] Figure 10 This is a schematic diagram of the structure of the foreign object detection device for power transmission lines provided in the embodiments of this application;

[0032] Figure 11 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0033] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0034] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0035] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0036] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0037] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0038] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0039] The following provides a detailed description of the foreign object detection method for transmission lines in this application.

[0040] Figure 1 This is a schematic flowchart of a foreign object detection method for transmission lines provided in an embodiment of this application, with reference to... Figure 1 The method for detecting foreign objects in this transmission line is described in detail below:

[0041] Step 101: Obtain an image to be identified containing the transmission line to be detected. The image to be identified is obtained by a drone at a preset location.

[0042] The image to be identified can be captured by a camera carried by a drone. In one scenario, the drone can be controlled to move to a preset position and then capture an image containing the power lines and poles to be detected, such as... Figure 2 As shown.

[0043] For example, the preset location can be determined based on the geographical location and surrounding environment of the power transmission line to minimize the impact of the background in the image to be identified on the detection results. In one scenario, a drone can move to a preset position and preset distance from the power pole, and then acquire an image containing the power transmission line to be detected at a preset shooting angle (such as shooting from below or above).

[0044] In some embodiments, multiple first sensing devices can be installed on the utility pole, and a second sensing device can be installed on the drone. The orientation and distance of the drone relative to the utility pole can be determined through these sensing devices, thereby determining the location of the drone.

[0045] In one scenario, there can be four first sensing devices, which are not positioned on the same plane. After the drone flies near the utility pole, a second sensing device can send a signal, which is sensed by the four first sensing devices. Then, based on information such as the time of the four first sensing devices' response, the position of the second sensing device relative to the four first sensing devices is determined according to a relevant algorithm, thus obtaining information such as the drone's orientation and distance relative to the utility pole. The specific geographical location of the utility pole is known information and can be obtained through appropriate means.

[0046] In another scenario, the aforementioned multiple first sensors can be replaced by three, which can be located on the same horizontal plane. After the drone flies over the utility pole, the second sensor can send a signal, which is sensed by the three first sensors. Then, based on information such as the time when the three first sensors sense the signal, a relevant algorithm determines two positions of the second sensor relative to the three first sensors. These two positions are symmetrical about the horizontal plane on which the three first sensors are located. Finally, combined with the drone's altitude, information such as the drone's orientation and distance relative to the utility pole can be obtained.

[0047] Step 102: Binarize the image to be identified to obtain a binarized image, wherein the binarized image contains the power transmission line area.

[0048] The binarized image includes multiple first pixels with first gray values ​​and second pixels with second gray values, and the area composed of the first pixels includes the power transmission line area.

[0049] For example, a binarized image can be composed of a first pixel with a grayscale value of 255 and a second pixel with a grayscale value of 0. The pixels constituting the transmission line area and the pole area can be the first pixel. It should be noted that the grayscale value of 255 for the pixels constituting the transmission line area and the pole area is merely an illustrative example. Those skilled in the art can also set the grayscale value of the pixels constituting the transmission line area and the pole area to 0, that is, set the first grayscale value to 0 and the second grayscale value to 255, which is not limited thereto.

[0050] In some embodiments, the implementation of step 102 may include steps 201 to 204. See also Figure 3 The details are as follows:

[0051] Step 201: Scan the image to be identified using a scanning window of a preset size.

[0052] The size of the scanning window can also be the size of n*m pixels, where n is an integer greater than or equal to 3 and m is an integer greater than or equal to 3. n and m can be the same or different.

[0053] See Figure 4 For example, the scanning window can be 3*3 pixels in size. The image to be recognized is scanned using a scanning window of size 3*3 pixels. The scanning process can be as follows: the scanning window is divided into three parts: top, middle, and bottom. The middle part of the scanning window is aligned with the first row of pixels in the image to be recognized. Then, starting from the first pixel, each pixel in the first row is scanned one by one, and then the pixels in the next row are scanned.

[0054] Step 202: If the brightness value of the pixel corresponding to the center of the scanning window in the image to be identified is less than or equal to the brightness value of the surrounding pixels, then the gray value of the pixel corresponding to the center of the scanning window is set to the first gray value.

[0055] Step 203: If the brightness value of the pixel corresponding to the center of the scanning window in the image to be identified is greater than the brightness value of the surrounding pixels, then the gray value of the pixel corresponding to the center of the scanning window is set to the second gray value.

[0056] See Figure 5 Taking a 3x3 pixel scanning window as an example, the thick outline represents the scanning window. Pixels 1 to 9 within the scanning window are the nine pixels covered by the window, meaning the scanning window is currently scanning pixel 9 in the image to be recognized. Assuming the brightness values ​​of pixels 1 to 9 are l1 to l9 respectively, if the brightness value l5 of pixel 5 is less than or equal to the grayscale values ​​of the other eight pixels, then the grayscale value of pixel 5 is set to the first grayscale value, for example, 255. If the brightness value l5 of pixel 5 is less than or equal to the brightness values ​​of the other eight pixels, then the grayscale value of pixel 5 is set to the e-th grayscale value, for example, 0.

[0057] Considering that power transmission lines are located outdoors and exposed to sunlight most of the time, the images acquired for identification are likely to be affected by sunlight. For example, some areas of the power transmission line in the image may be under strong sunlight, while other areas may be under weak sunlight, resulting in uneven illumination. Therefore, if a single grayscale threshold is used to binarize all pixels, the resulting binarized image is likely to be distorted and unable to accurately identify the power transmission line area. To address this technical problem, the inventors employ a scanning window to scan and compare each pixel in the image to be identified, setting the grayscale value of each pixel to either a first grayscale value or a second grayscale value, thereby eliminating the influence of uneven illumination.

[0058] For example, information such as the time when the drone collected the image to be identified and the cloud cover can be used to determine whether there is uneven lighting in the image to be identified.

[0059] For example, time can be divided into three stages. The first stage is in the morning, when the sunlight is weak and there is basically no uneven lighting as mentioned above. The second stage is in the evening and at night, when the sunlight is weak and there is basically no uneven lighting as mentioned above. The third stage is when the sunlight is better, and there is a high probability that there will be uneven lighting as mentioned above.

[0060] For example, cloud cover can be determined based on the weather conditions at the time. If it is a sunny day, then the cloud cover is likely to be low, and there is a high probability of uneven sunlight exposure. If it is a cloudy, overcast, rainy, or snowy day, then the cloud cover is likely to be high, and there is virtually no uneven sunlight exposure.

[0061] If the aforementioned uneven lighting condition is determined to exist, steps 201 to 203 can be performed to binarize the image to be recognized. If the aforementioned uneven lighting condition is determined not to exist, a grayscale threshold can be used to binarize each pixel of the image to be recognized.

[0062] Step 204: For the outermost pixel in the image to be identified, compare the gray value of the pixel with a gray value threshold. If the gray value of the pixel is greater than or equal to the gray value threshold, set the gray value of the pixel to a second gray value; if the gray value of the pixel is less than the gray value threshold, set the gray value of the pixel to a first gray value.

[0063] In the process of binarizing the image to be recognized using the methods in steps 201 to 203, the outermost pixels may not be binarized. Therefore, a grayscale threshold can be used to binarize the outermost pixels.

[0064] For example, the aforementioned grayscale threshold can be determined based on the time of image acquisition and cloud cover, with each pair of time and cloud cover corresponding to a grayscale threshold. Specifically, the stronger the sunlight corresponding to each pair of time and cloud cover, the smaller the grayscale threshold; conversely, the weaker the sunlight corresponding to each pair of time and cloud cover, the larger the grayscale threshold. Determining the grayscale threshold based on the time of image acquisition and cloud cover can further reduce the impact of strong light irradiation.

[0065] For example, time can be divided into three stages. The first stage is morning, when the sunlight is weak, so the corresponding grayscale threshold can be larger, such as greater than 127. The second stage is evening and night, when the sunlight is weak, so the corresponding grayscale threshold can be larger, such as greater than 127. The third stage is when the sunlight is better, so the corresponding grayscale threshold can be relatively smaller, such as less than 127.

[0066] For example, cloud cover can be determined based on the weather conditions at the time. If it is a sunny day, the cloud cover is determined to be low and the sunlight is strong, so the corresponding grayscale threshold can be relatively small, such as less than 127. If it is a cloudy, overcast, rainy, or snowy day, the cloud cover is determined to be high and the sunlight is weak, so the corresponding grayscale threshold can be relatively large, such as greater than 127.

[0067] It should be noted that step 204 can be executed in parallel with step 201, step 204 can be executed before step 201, or step 204 can be executed after step 201, or step 204 can be executed at the same time as step 201, and there are no restrictions on this.

[0068] In some embodiments, the process of determining the transmission line region may include: determining a plurality of target regions composed of pixels with a grayscale value of a second grayscale value; and determining the transmission line region based on the size, shape, and angles between the plurality of target regions.

[0069] For example, in a binarized image, a utility pole can correspond to a first preset pixel size and a first preset shape, and a power transmission line can correspond to a second preset pixel size and a second preset shape. The angle between the utility pole and the power transmission line support can be a preset angle. The preset pixel size can be the number of pixels corresponding to the width and length of the utility pole or power transmission line, and the preset shape can be the shape formed by the pixels corresponding to the utility pole or power transmission line. In this embodiment, based on these dimensions, shapes, and angles of the utility pole and power transmission line, the utility pole and power transmission line can be identified in the binarized image.

[0070] For example, determining the transmission line area based on the size, shape, and angles between the plurality of target areas may include: comparing the size of each target area with the first preset size and the second preset size, comparing the shape of each target area with the first preset shape and the second preset shape, determining the first target area corresponding to the pole and the second target area corresponding to the transmission line; determining the angle between the first target area and the second target area, and if the angle is equal to the preset angle, then determining the second target area as the area corresponding to the transmission line.

[0071] In one scenario, by comparing the dimensions of each target region with a first preset size and a second preset size, regions composed of pixels representing features of utility poles and transmission lines can be initially identified. Then, the shapes of the initially identified regions can be compared with a first preset shape and a second preset shape to determine the first target region corresponding to the utility pole and the second target region corresponding to the transmission line.

[0072] In another scenario, by comparing the shapes of each target region with a first preset shape and a second preset shape, regions composed of pixels representing features of utility poles and transmission lines can be initially identified. Then, the dimensions of the initially identified regions can be compared with a first preset size and a second preset size to determine the first target region corresponding to the utility pole and the second target region corresponding to the transmission line.

[0073] In some embodiments, the process of determining the angle between the first target area and the second target area may include: obtaining the shooting position and shooting angle of the drone; finding a preset angle corresponding to the shooting position and the shooting angle based on the shooting position and the shooting angle, and determining the angle information between the power pole and the transmission line, wherein the preset angle is the theoretical angle between the power pole and the transmission line at the shooting position and the shooting angle.

[0074] The method for obtaining the drone's shooting position has already been explained in step 101 and will not be repeated here. The drone's shooting angle can be preset, for example, two shooting angles can be preset, corresponding to overhead and upward shooting respectively. If the drone's altitude when acquiring the image to be identified is lower than the height of the power line, the preset shooting angle corresponding to upward shooting will be used; if the drone's altitude when acquiring the image to be identified is higher than the height of the power line, the preset shooting angle corresponding to overhead shooting will be used.

[0075] In this embodiment, multiple sets of correspondences between shooting positions, shooting angles, and the angle between the utility pole and the power transmission line can be pre-set. That is, after determining the drone's shooting position and shooting angle, the angle between the utility pole and the power transmission line corresponding to that shooting position and angle can be determined by querying the aforementioned correspondences. Figure 6 The angle α between the pole and the transmission line shown in the figure.

[0076] Step 103: Obtain the boundary features of the boundary line of the transmission line area, and determine whether the transmission line is attached to foreign objects based on the boundary features and the preset boundary features.

[0077] See Figure 7 In some embodiments, the implementation process of step 103 may include steps 301 to 303, as detailed below:

[0078] Step 301: Determine the boundary characteristics of the boundary line of the transmission line area. The boundary characteristics include: the boundary line is a straight line, the boundary line is a smooth curve, and the boundary line is a straight line or a curve with abrupt changes.

[0079] Step 302: If the boundary feature indicates that the boundary line of the transmission line area is straight or smooth curve, then it is determined that there are no foreign objects attached to the transmission line.

[0080] Step 303: If the boundary feature indicates that the boundary line of the transmission line area is a straight line or a curve with a sudden change, then it is determined that there is a foreign object attached to the transmission line, and the type of foreign object is determined according to the shape of the abrupt change.

[0081] Referring to Figure 8(a), the boundary line of the determined transmission line area is a straight line, indicating that there are no foreign objects on the transmission line. As shown in Figure 8(b), the boundary line of the determined transmission line area is a straight line with an abrupt change, indicating that there are foreign objects on the transmission line. The location of the foreign object on the transmission line is the location of the abrupt change.

[0082] For example, when there is abrupt change in the boundary line of a transmission line area, the size of the abrupt region can be obtained. Then, this region size is compared with a preset region threshold. If the region size is greater than or equal to the threshold, the abrupt region is determined to be a foreign object; if the region size is less than the threshold, it is determined not to be a foreign object. For instance, an image can be input into the algorithm model, which then outputs the result indicating whether a foreign object exists in the transmission line. The algorithm model can be trained using image samples labeled with foreign object regions.

[0083] For example, the region threshold may include values ​​such as the number of pixels, the length and width of the region. The number of pixels, the length and width of the region can be determined based on a sample image, which is an image of a power transmission line containing foreign objects.

[0084] Furthermore, in this embodiment, the type of foreign object can be determined based on the shape of the abrupt change. For example, if the shape of the abrupt change is the same as or similar to that of a bird, the foreign object can be identified as a bird standing on the power transmission line, which generally requires no further action. If the shape of the abrupt change is the same as or similar to that of debris entangled on the power transmission line, the foreign object can be identified as debris entangled on the power transmission line. For debris entangled on the power transmission line that requires handling, the location of the debris (e.g., pole number) can be sent to the maintenance terminal so that maintenance personnel can perform maintenance.

[0085] In some embodiments, based on Figure 1In the illustrated embodiment, prior to step 101, the aforementioned method for detecting foreign objects in transmission lines may further include a step of scaling the image to be identified. By scaling the image to be identified, the resolution (i.e., the number of pixels) of the image to be identified can be reduced without affecting subsequent image recognition. Therefore, scaling the image to be identified reduces the number of pixels, reduces the workload of subsequent image processing, and improves the efficiency of subsequent image processing.

[0086] For example, scaling the image to be identified may include: converting the image to be identified into a grayscale image; extracting pixels corresponding to preset rows and preset columns in the grayscale image; and combining the extracted pixels according to their positional relationship in the original image to generate a new image to be identified.

[0087] The preset rows can be odd-numbered, even-numbered, or rows corresponding to other rules; the preset columns can be odd-numbered, even-numbered, or columns corresponding to other rules. For example, for rows near the edge of the image to be recognized, a row of pixels can be extracted every two rows; for rows near the center of the image to be recognized, a row of pixels can be extracted every other row. Similarly, for columns near the edge of the image to be recognized, a column of pixels can be extracted every two columns; for columns near the center of the image to be recognized, a column of pixels can be extracted every other column.

[0088] Typically, near the edges of the image to be recognized, there is a lot of background content other than utility poles and power lines. Since utility poles and power lines are usually located close to the center of the image, the above method can be used to further reduce the number of pixels in the scaled image, thereby improving the speed and efficiency of subsequent image processing.

[0089] Referring to Figure 9(a), the pixels corresponding to even rows and even columns in the grayscale image are extracted; according to the positional relationship of each extracted pixel in the original image, the extracted pixels are combined to generate a new image to be recognized, as shown in Figure 9(b).

[0090] The aforementioned method for detecting foreign objects on power transmission lines involves using a drone to acquire an image of the transmission line to be detected at a predetermined location. This image is then binarized to obtain a binarized image of the region containing the transmission line. By matching the boundary features of this region with predetermined boundary features, the presence of foreign objects on the transmission line can be determined. This method detects foreign objects on power transmission lines using only the boundary features of the transmission line region, eliminating the need for complex neural network models and resulting in high detection efficiency.

[0091] The aforementioned method for detecting foreign objects on power transmission lines can be executed by a drone, a cloud platform, or a server. A drone and a cloud platform or server can constitute a system for detecting foreign objects on power transmission lines. Users can send commands to the drone through the cloud platform or server, and the drone will execute these commands to acquire images of the power transmission line. The drone can then send these images to the cloud platform or server, which will use them to perform foreign object detection on the power transmission line. Alternatively, the drone can process the images to perform foreign object detection on the power transmission line.

[0092] In addition, historical data can be used to focus inspections on transmission lines in a specific area. For example, the inspection frequency can be increased for transmission lines with a higher probability of detecting foreign objects, while the inspection frequency can be decreased for transmission lines with a lower probability of detecting foreign objects. This can optimize resources and improve the efficiency of foreign object detection on transmission lines.

[0093] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0094] Corresponding to the foreign object detection method for transmission lines described in the above embodiments, Figure 10 A structural block diagram of the foreign object detection device for power transmission lines provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0095] See Figure 10 The foreign object detection device for transmission lines in this embodiment may include an image acquisition module 401, a binarization processing module 402, and a foreign object detection module 403.

[0096] The image acquisition module 401 is used to acquire an image to be identified containing the transmission line to be detected. The image to be identified is acquired by a drone at a preset location.

[0097] The binarization processing module 402 is used to perform binarization processing on the image to be identified to obtain a binarized image. The binarized image contains a power transmission line region. The binarized image includes a plurality of first pixels with first gray values ​​and second pixels with second gray values. The region composed of the first pixels contains the power transmission line region.

[0098] The foreign object detection module 403 is used to acquire the boundary features of the boundary line of the transmission line area, and determine whether the transmission line is attached to foreign objects based on the boundary features and preset boundary features.

[0099] Optionally, the binarization processing module 402 can be used to: scan the image to be recognized using a scanning window of a preset size; if the gray value of a pixel in the image to be recognized corresponding to the center of the scanning window is less than the gray value of some surrounding pixels, then set the gray value of the pixel corresponding to the center of the scanning window to a first gray value; if the gray value of a pixel in the image to be recognized corresponding to the center of the scanning window is greater than the gray value of some surrounding pixels, then set the gray value of the pixel corresponding to the center of the scanning window to a second gray value; for the outermost pixels in the image to be recognized, compare the gray value of the pixel with a gray value threshold; if the gray value of the pixel is greater than or equal to the gray value threshold, then set the gray value of the pixel to a second gray value; if the gray value of the pixel is less than the gray value threshold, then set the gray value of the pixel to a first gray value.

[0100] Optionally, the grayscale threshold is determined based on the time of acquisition of the image to be identified and the cloud cover, with each pair of time and cloud cover corresponding to a grayscale threshold; wherein, the stronger the sunlight corresponding to each pair of time and cloud cover, the smaller the grayscale threshold; and the weaker the sunlight corresponding to each pair of time and cloud cover, the larger the grayscale threshold.

[0101] Optionally, the process of determining the transmission line area includes: determining multiple target areas composed of pixels with a grayscale value of a second grayscale value; and determining the transmission line area based on the size, shape, and angle between the multiple target areas.

[0102] Optionally, the utility pole corresponds to a first preset pixel size and a first preset shape, the power transmission line corresponds to a second preset pixel size and a second preset shape, and the angle between the utility pole and the power transmission line support is a preset angle.

[0103] The step of determining the transmission line area based on the size, shape, and angles between the plurality of target areas includes: comparing the size of each target area with the first preset size and the second preset size, comparing the shape of each target area with the first preset shape and the second preset shape, determining the first target area corresponding to the pole and the second target area corresponding to the transmission line; determining the angle between the first target area and the second target area, and if the angle is equal to the preset angle, then determining the second target area as the area corresponding to the transmission line.

[0104] Optionally, determining the angle between the first target area and the second target area includes: acquiring the shooting position and shooting angle of the drone; finding a preset angle corresponding to the shooting position and shooting angle based on the shooting position and shooting angle, and determining the angle information between the power pole and the power transmission line, wherein the preset angle is the theoretical angle between the power pole and the power transmission line at the shooting position and shooting angle.

[0105] Optionally, the foreign object detection module 403 can be specifically used to: determine the boundary characteristics of the boundary line of the transmission line area, the boundary characteristics including: the boundary line is straight, the boundary line is a smooth curve, and the boundary line is a straight line or a curve with a sudden change; if the boundary characteristics indicate that the boundary line of the transmission line area is straight or a smooth curve, then it is determined that there are no foreign objects attached to the transmission line; if the boundary characteristics indicate that the boundary line of the transmission line area is a straight line or a curve with a sudden change, then it is determined that there are foreign objects attached to the transmission line, and the type of foreign object is determined according to the shape of the abrupt change.

[0106] Optionally, the aforementioned foreign object detection device for transmission lines may further include an image scaling module. The image scaling module is used to: convert the image to be identified into a grayscale image, extract the pixels corresponding to preset rows and preset columns in the grayscale image, and combine the extracted pixels according to their positional relationship in the original image to generate a new image to be identified, so that the binarization processing module 402 performs binarization processing on the new image to be identified.

[0107] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0108] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0109] This application also provides an electronic device, see [link to relevant documentation] Figure 11 The electronic device 500 may include at least one processor 510 and a memory 520, wherein the memory 520 stores a computer program executable on the at least one processor 510, and the processor 510 executes the computer program to implement the steps in any of the above method embodiments, for example... Figure 1 Steps 101 to 103 in the illustrated embodiment. Alternatively, when the processor 510 executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 10 The functions of modules 401 to 403 are shown.

[0110] For example, a computer program may be divided into one or more modules / units, one or more of which are stored in memory 520 and executed by processor 510 to complete this application. The one or more modules / units may be a series of computer program segments capable of performing a specific function, which describe the execution process of the computer program in electronic device 500.

[0111] Those skilled in the art will understand that Figure 11 This is merely an example of an electronic device and does not constitute a limitation on the electronic device. It may include more or fewer components than shown, or combinations of certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0112] The processor 510 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0113] The memory 520 can be an internal storage unit of the electronic device or an external storage device, such as a plug-in hard drive, a smart media card (SMC), a secure digital card (SD), or a flash card. The memory 520 is used to store the computer program and other programs and data required by the electronic device. The memory 520 can also be used to temporarily store data that has been output or will be output.

[0114] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0115] The foreign object detection method for power transmission lines provided in this application embodiment can be applied to electronic devices such as drones, servers, cloud platforms, computers, wearable devices, vehicle-mounted devices, tablets, laptops, netbooks, personal digital assistants (PDAs), augmented reality (AR) / virtual reality (VR) devices, and mobile phones. This application embodiment does not impose any restrictions on the specific type of electronic device.

[0116] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the various embodiments of the above-described transmission line foreign object detection method.

[0117] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps in the various embodiments of the above-described transmission line foreign object detection method.

[0118] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to an electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0119] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0120] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0121] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0122] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for detecting foreign objects in power transmission lines, characterized in that, include: Acquire an image to be identified containing the transmission line to be detected, the image being acquired by a drone at a preset location; The image to be identified is binarized to obtain a binarized image. The binarized image contains a power transmission line region. The binarized image includes multiple first pixels with first gray values ​​and second pixels with second gray values. The region composed of the first pixels contains the power transmission line region. Obtain the boundary features of the boundary line of the transmission line area, and determine whether the transmission line is attached to foreign objects based on the boundary features and preset boundary features; The step of binarizing the image to be identified to obtain a binarized image includes: The image to be identified is scanned using a scanning window of a preset size; If the gray value of the pixel in the image to be identified that corresponds to the center of the scanning window is less than the gray value of the surrounding pixels, then the gray value of the pixel corresponding to the center of the scanning window is set to the first gray value. If the gray value of the pixel corresponding to the center of the scanning window in the image to be identified is greater than the gray value of the surrounding pixels, then the gray value of the pixel corresponding to the center of the scanning window is set to the second gray value. For the outermost pixel in the image to be identified, the gray value of the pixel is compared with a gray value threshold. If the gray value of the pixel is greater than or equal to the gray value threshold, the gray value of the pixel is set to a second gray value; if the gray value of the pixel is less than the gray value threshold, the gray value of the pixel is set to a first gray value. The grayscale threshold is determined based on the time of acquisition of the image to be identified and the cloud cover, with each pair of time and cloud cover corresponding to a grayscale threshold; Among them, the stronger the sunlight corresponding to each pair of time and cloud cover, the smaller the grayscale threshold; the weaker the sunlight corresponding to each pair of time and cloud cover, the larger the grayscale threshold.

2. The method for detecting foreign objects in transmission lines as described in claim 1, characterized in that, The process of determining the transmission line area includes: Determine multiple target regions formed by pixels with a grayscale value of the second grayscale value; The transmission line area is determined based on the size, shape, and relative angles of the multiple target areas.

3. The method for detecting foreign objects in transmission lines as described in claim 2, characterized in that, The utility pole corresponds to a first preset pixel size and a first preset shape, and the power transmission line corresponds to a second preset pixel size and a second preset shape. The angle between the utility pole and the power transmission line support is a preset angle. Determining the transmission line area based on the size, shape, and relative angles of the plurality of target areas includes: The size of each target area is compared with the first preset pixel size and the second preset pixel size, and the shape of each target area is compared with the first preset shape and the second preset shape to determine the first target area corresponding to the pole and the second target area corresponding to the transmission line. Determine the angle between the first target area and the second target area. If the angle is equal to the preset angle, then determine that the second target area is the area corresponding to the transmission line.

4. The method for detecting foreign objects in transmission lines as described in claim 3, characterized in that, Determining the angle between the first target region and the second target region includes: Obtain the drone's shooting position and shooting angle; Based on the shooting position and the shooting angle, a preset angle corresponding to the shooting position and the shooting angle is found to determine the angle information between the pole and the transmission line. The preset angle is the theoretical angle between the pole and the transmission line at the shooting position and the shooting angle.

5. The method for detecting foreign objects in transmission lines as described in claim 1, characterized in that, The step of acquiring the boundary features of the boundary line of the transmission line area, and determining whether the transmission line is attached to foreign objects based on the boundary features and preset boundary features, includes: The boundary characteristics of the boundary line of the transmission line area are determined, including: the boundary line is a straight line, the boundary line is a smooth curve, and the boundary line is a straight line or a curve with abrupt changes. If the boundary feature indicates that the boundary line of the transmission line area is straight or smooth curve, then it is determined that there are no foreign objects attached to the transmission line. If the boundary feature indicates that the boundary line of the transmission line area is a straight line or a curve with a sudden change, then it is determined that there is a foreign object attached to the transmission line, and the type of foreign object is determined according to the shape of the abrupt change.

6. The method for detecting foreign objects in transmission lines as described in claim 1, characterized in that, Before binarizing the image to be identified, the method further includes scaling the image to be identified; The scaling of the image to be identified includes: Convert the image to be identified into a grayscale image; Extract the pixels corresponding to the preset rows and preset columns in the grayscale image; Based on the positional relationship of each extracted pixel in the original image, the extracted pixels are combined to generate a new image to be recognized.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 6.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

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