CN114220009A - Infrared image-based wire windage yaw identification method and system - Google Patents
Infrared image-based wire windage yaw identification method and system Download PDFInfo
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Abstract
The application provides a wire windage yaw identification method and a system based on infrared images, and the method comprises the following steps: s1, acquiring an infrared image of the power transmission line in the area to be detected; s2, reading the infrared image, detecting the insulator region of the power transmission line based on a yolov5S target detection model, and further extracting the insulator region; s3, detecting the insulator region through an infrared temperature detection model, acquiring the temperature in the insulator region, and calculating the deflection degree of the insulator region through an image processing algorithm; and S4, judging whether the temperature of the insulator region accords with a normal value, judging whether the insulator region is in a normal state, and further judging whether the power transmission line generates windage yaw. By combining the infrared image with the visible light processing algorithm, whether the insulator has a windage yaw phenomenon or not can be judged more accurately.
Description
Technical Field
The application belongs to the technical field of power transmission lines, and particularly relates to a wire windage yaw identification method and system based on infrared images.
Background
The wind deflection accident of the power transmission line is one of the frequently-occurring power grid accidents, and often causes line tripping, wind deflection discharge, wire arc burning, disconnection and the like. Unlike lightning flashover and operational impulse flashover, most windage yaw flashover faults occur at operating voltages. Due to the continuity of wind in a certain time, the wind deflection flashover can not be successfully superposed after jumping off, thereby causing the outage of a power transmission line and seriously influencing the reliability of power supply of a power grid.
The wind deflection inter-hop fault is one of the most common wind damage types of the power transmission line, mainly comprises deflection of a lead and an insulator under the action of wind power, and discharge tripping caused by insufficient electrical gap distance. Windage tripping faults, sometimes referred to simply as windage faults. Windage yaw faults are often accompanied by severe weather conditions, windage yaw jumps are often caused by severe weather such as strong wind under the condition of working voltage, the success rate of superposition is low, and the power supply reliability is seriously influenced.
The windage yaw detection mainly detects whether the distance between an insulator string and a tower is larger than an electric air gap limit value and whether the distance between wires is normal. However, there is a certain error in the conventional method for acquiring video images of a power transmission line to detect windage yaw of the power transmission line.
In view of the above, it is very significant to provide a method and a system for identifying a windage yaw of a wire based on an infrared image.
Content of application
In order to solve the problem that a large error exists in the existing windage yaw detection of the power transmission line, the application provides a wire windage yaw identification method and system based on an infrared image so as to solve the technical defect problem existing in the windage yaw detection of the power transmission line.
In a first aspect, the present application provides a method for identifying a windage yaw of a wire based on an infrared image, including the following steps:
s1, acquiring an infrared image of the power transmission line in the area to be detected;
s2, reading the infrared image, detecting the insulator region of the power transmission line based on a yolov5S target detection model, and further extracting the insulator region;
s3, detecting the insulator region through an infrared temperature detection model, acquiring the temperature in the insulator region, and calculating the deflection degree of the insulator region through an image processing algorithm; and
s4, judging whether the temperature of the insulator region accords with a normal value or not, judging whether the insulator region is in a normal state or not, and further judging whether the power transmission line generates windage yaw or not.
The method comprises the steps of obtaining an infrared image of a power transmission line of a region to be detected through infrared image obtaining equipment, detecting a corresponding insulator region on the power transmission line by means of a yolov5s target detection model, analyzing whether the insulator has temperature abnormity or not through an infrared temperature detection model, calculating the deflection degree of the insulator through an image processing algorithm, and judging whether the insulator has a windage phenomenon or not more accurately by combining the infrared image with a visible light processing algorithm.
Preferably, the S2 detects the infrared image based on the yolov5S target detection model, and further includes:
s21, collecting a certain number of pictures of the insulator region, and manually marking by using an image marking tool Ldelme to obtain a data set;
s22, the acquired data set is recorded in a mode that 8: 2, dividing the ratio into a training set and a test set;
s23, reading the training set by using yolov5S for training, and obtaining a target detection model based on yolov 5S;
and S24, detecting a target detection model based on yolov5S by using the test set.
The traditional algorithm is easy to cause instability under the interference of external illumination and shadow, and the advanced deep learning yolov5 algorithm adopted by the method has better robustness and stability compared with the traditional image processing method through a large amount of target learning training.
Further preferably, the step S3 further includes establishing the infrared temperature detection model:
s31, acquiring infrared images of the insulator region shot at different time, same place and same angle, and classifying the infrared images according to time periods;
s32, marking the temperature of each point in the insulation sub-area in the infrared image through an unmanned aerial vehicle (SDK);
s33, further converting the infrared image into a gray image;
s34, fitting the gray value and the temperature value corresponding to each point through a naive Bayes algorithm;
and S35, establishing the infrared temperature detection model.
In order to avoid the influence of an external environment, three time periods of morning, noon and evening are selected to collect the infrared images, three models are correspondingly established, the shooting location and angle of the infrared image acquisition equipment are fixed during each collection, and the stability of infrared image acquisition is guaranteed to be good.
Further preferably, the naive Bayes formula in S34 is expressed as follows
Where T represents temperature, G represents gray scale value, the conditional distribution of temperature T can be expressed as,
obtaining a classification model of the gray value corresponding to the temperature,
classify(g)＝argmax p(T＝t)
further preferably, the image processing of the insulator region in S3 includes:
s311, preprocessing the extracted image of the insulator region; s312, segmenting the image of the insulator region, and further performing morphological processing; s313, extracting the framework of the insulator string in the insulator region, and further calculating to obtain end point coordinates of two ends of the framework; and S314, calculating the offset of the insulator string according to the endpoint coordinates, and further judging the offset degree of the insulator region.
Further preferably, in S312, the morphological processing includes a dilation operation and an erosion operation in mathematical morphology.
Preferably, in S313, the offset of the insulator string is calculated by using a windage yaw angle calculation formula, where the windage yaw angle calculation formula specifically includes:
wherein (x)1,y1)、(x2,y2) And the end point is the end point of the insulator string after windage yaw.
In a second aspect, the present application provides a wire windage yaw recognition system based on infrared images, the system including:
the infrared image acquisition module is configured for acquiring an infrared image of the power transmission line in the area to be detected;
the infrared image processing module is configured to detect and extract the insulator region, and perform preprocessing, segmentation processing and morphological processing on the extracted image of the insulator region;
the infrared temperature detection module is configured to detect the insulator region by using an infrared temperature detection model and acquire the temperature in the insulator region;
the framework extraction module is configured for extracting the framework of the insulator string in the insulator region;
the calculation module is configured to calculate end point coordinates of two ends of the framework and calculate the offset of the insulator string according to the end point coordinates;
and the judging module is configured to judge whether the temperature of the insulator region meets a normal value, judge whether the insulator region is in a normal state, and further judge whether the power transmission line generates windage yaw.
In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; storage means for storing one or more programs which, when executed by one or more processors, cause the one or more processors to carry out a method as described in any one of the implementations of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
Compared with the prior art, the beneficial results of this application lie in:
(1) the method comprises the steps of obtaining an infrared image of a power transmission line of a region to be detected through infrared image obtaining equipment, detecting a corresponding insulator region on the power transmission line by means of a yolov5s target detection model, analyzing whether the insulator has temperature abnormity or not through an infrared temperature detection model, calculating the deflection degree of the insulator through an image processing algorithm, and judging whether the insulator has a windage phenomenon or not more accurately by combining the infrared image with a visible light processing algorithm.
(2) The traditional algorithm is easy to cause instability under the interference of external illumination and shadow, and the advanced deep learning yolov5 algorithm adopted by the method has better robustness and stability compared with the traditional image processing method through a large amount of target learning training.
Drawings
The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and together with the description serve to explain the principles of the application. Other embodiments and many of the intended advantages of embodiments will be readily appreciated as they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale relative to each other. Like reference numerals designate corresponding similar parts.
Fig. 1 is a schematic flowchart of a method for identifying a windage yaw of a wire based on an infrared image according to an embodiment of the present application;
fig. 2 is a schematic diagram of an infrared image of a power transmission line in a wire windage yaw identification method based on an infrared image according to an embodiment of the present application;
fig. 3 is a flowchart illustrating processing of an infrared image of an insulator in a method for identifying a windage yaw of a conductor based on an infrared image according to an embodiment of the present application;
fig. 4 is a schematic diagram illustrating extraction of an insulator framework in a wire windage yaw identification method based on an infrared image according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a system of a wire windage yaw recognition method based on infrared images according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a computer device suitable for implementing an electronic apparatus according to an embodiment of the present application.
Detailed Description
In the following detailed description, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration specific embodiments in which the application may be practiced. In this regard, directional terminology, such as "top," "bottom," "left," "right," "up," "down," etc., is used with reference to the orientation of the figures being described. Because components of embodiments can be positioned in a number of different orientations, the directional terminology is used for purposes of illustration and is in no way limiting. It is to be understood that other embodiments may be utilized and logical changes may be made without departing from the scope of the present application. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present application is defined by the appended claims.
In a first aspect, the present application provides a method for identifying a windage yaw of a wire based on an infrared image, including the following steps:
fig. 1 shows a flow chart of a wire windage yaw identification method based on infrared images according to an embodiment of the present application, as shown in fig. 1.
S1, acquiring an infrared image of the power transmission line in the area to be detected;
the infrared image in this embodiment is obtained by shooting through infrared image shooting equipment, including but not limited to infrared camera, unmanned aerial vehicle etc.
S2, reading the infrared image, detecting the insulator region of the power transmission line based on a yolov5S target detection model, and further extracting the insulator region;
in a specific embodiment, the S2 detects the infrared image based on the yolov5S target detection model, and further includes:
s21, collecting a certain number of pictures of the insulator region, and manually marking by using an image marking tool Ldelme to obtain a data set;
s22, the acquired data set is recorded in a mode that 8: 2, dividing the ratio into a training set and a test set;
s23, reading the training set by using yolov5S for training, and obtaining a target detection model based on yolov 5S;
and S24, detecting a target detection model based on yolov5S by using the test set.
The traditional algorithm is easy to cause instability under the interference of external illumination and shadow, and the advanced deep learning yolov5 algorithm adopted by the method has better robustness and stability compared with the traditional image processing method through a large amount of target learning training.
Compared with other target detection methods, the Yolov5s algorithm has the following advantages: by adopting self-adaptive picture scaling, the calculation amount can be reduced during operation, and the operation speed is improved; a Focus structure, wherein an original 608 × 3 image is input into the Focus structure, and is changed into a feature map of 304 × 12 by adopting a slicing operation, and is finally changed into a feature map of 304 × 32 by performing a convolution operation of 32 convolution kernels; the CSP structure integrates the change of the gradient into the characteristic diagram from beginning to end, reduces the calculated amount and can ensure the accuracy; the Neck part adopts a CSP2 structure designed by using CSPnet for reference to enhance the capability of network feature fusion; in Yolov5, GIOU _ Loss is used as a Loss function of a Bounding box, the minimum area of the closure areas of the two frames is calculated, IoU is calculated, the proportion of the areas, which do not belong to the two frames, in the closure areas to the closure areas is calculated, and finally IoU is used for subtracting the proportion to obtain GIoU
S3, detecting the insulator region through an infrared temperature detection model, acquiring the temperature in the insulator region, and calculating the deflection degree of the insulator region through an image processing algorithm; and
s3, establishing the infrared temperature detection model:
s31, acquiring infrared images of the insulator region shot at different time, same place and same angle, and classifying the infrared images according to time periods;
s32, marking the temperature of each point in the insulation sub-area in the infrared image through an unmanned aerial vehicle (SDK);
s33, further converting the infrared image into a gray image;
s34, fitting the gray value and the temperature value corresponding to each point through a naive Bayes algorithm;
the naive bayes formula is expressed in S34 as follows:
where T represents temperature, G represents gray scale value, the conditional distribution of temperature T can be expressed as,
obtaining a classification model of the gray value corresponding to the temperature,
classify(g)＝argmax p(T＝t)
and S35, establishing the infrared temperature detection model.
Fig. 2 shows a schematic diagram of an infrared image of a power transmission line in a wire windage yaw identification method based on an infrared image according to an embodiment of the present application, as shown in fig. 2.
In a specific embodiment, in order to avoid the influence of an external environment, three time periods of morning, noon and evening are selected for acquiring the infrared images, three models are correspondingly established, the shooting place and angle of the infrared image acquisition equipment are fixed during each acquisition, and the stability of infrared image acquisition is guaranteed to be good.
In the following, the model establishment mode in a certain time period is taken as an example, and the processing in other time periods is the same. And taking 1000 infrared pictures shot in the time period, extracting ten thousand temperature points by using the SDK of the unmanned aerial vehicle, converting the infrared pictures into a gray-scale map, and fitting the gray-scale value corresponding to each point with the temperature value through a Bayesian algorithm.
Fig. 3 shows a flowchart of processing an infrared image of an insulator in a method for identifying a windage yaw of a conductor based on an infrared image according to an embodiment of the present application, as shown in fig. 3.
The image processing of the insulator region in S3 includes:
s311, preprocessing the extracted image of the insulator region;
s312, segmenting the image of the insulator region, and further performing morphological processing;
specifically, the preprocessing in this embodiment mainly refers to denoising processing of an image of the insulator region. Since the images shot by the unmanned aerial vehicle have interference mainly comprising Gaussian noise and salt and pepper noise, the image preprocessing stage uses median filtering and PED filtering for processing.
The segmentation process mainly refers to gray scale morphology segmentation, because the image-processed insulator picture may have interference influence such as iron tower, power transmission line, background vegetation, etc., in order to extract the insulator from the interference, the gray scale morphology segmentation is mainly used for processing.
In the specific embodiment S312, the morphological processing includes a dilation operation and a erosion operation in the mathematical morphology. The dilation operation is implemented by an Opencv function scale (), the function uses a specified kernel element to dilate the source image, the kernel has a definable anchor point called kernel center point, during the dilation operation, the maximum pixel value of the kernel coverage area is obtained and replaces the pixel of the anchor point, and the maximum value is obtained by the following formula:
the erosion operation is realized by an Opencv function (enode ()), the function uses a specified kernel element to erode a source image, the kernel has a definable anchor point called kernel center point, during the erosion operation, the minimum pixel value of the kernel coverage area is obtained and replaces the pixel of the anchor point, and the minimum value is obtained by the following formula:
fig. 4 is a schematic diagram illustrating extraction of an insulator framework in a wire windage yaw identification method based on an infrared image according to an embodiment of the present application, as shown in fig. 4.
S313, extracting the framework of the insulator string in the insulator region, and further calculating to obtain end point coordinates of two ends of the framework;
specifically, the framework of the insulator string can be obtained after the steps are carried out, and then coordinates of end points at two ends of the insulator string are obtained through calculation. And setting the width and height of the image of the insulator region as w and h, and traversing to obtain the pseudo codes of the upper end points as follows:
the obtained i, j is the coordinate of the upper end point, and the lower end point can be obtained by the same principle.
In S313, the offset of the insulator string is calculated by using a windage yaw angle calculation formula, where the windage yaw angle calculation formula specifically includes:
wherein (x)1,y1)、(x2,y2) And the end point is the end point of the insulator string after windage yaw.
And S314, calculating the offset of the insulator string according to the endpoint coordinates, and further judging the offset degree of the insulator region.
S4, judging whether the temperature of the insulator region accords with a normal value or not, judging whether the insulator region is in a normal state or not, and further judging whether the power transmission line generates windage yaw or not.
Fig. 5 shows a schematic diagram of a system of a wire windage yaw identification method based on infrared images according to an embodiment of the present application, as shown in fig. 5.
In a second aspect, the present application provides a wire windage yaw recognition system based on infrared images, the system including:
the infrared image acquisition module is configured for acquiring an infrared image of the power transmission line in the area to be detected;
the infrared image processing module is configured to detect and extract the insulator region, and perform preprocessing, segmentation processing and morphological processing on the extracted image of the insulator region;
the infrared temperature detection module is configured to detect the insulator region by using an infrared temperature detection model and acquire the temperature in the insulator region;
the framework extraction module is configured for extracting the framework of the insulator string in the insulator region;
the calculation module is configured to calculate end point coordinates of two ends of the framework and calculate the offset of the insulator string according to the end point coordinates;
and the judging module is configured to judge whether the temperature of the insulator region meets a normal value, judge whether the insulator region is in a normal state, and further judge whether the power transmission line generates windage yaw.
The infrared image of the power transmission line of the area to be detected is obtained through infrared image obtaining equipment, the corresponding insulator region on the power transmission line is detected through the yolov5s target detection model, whether temperature abnormity exists in the insulator is analyzed through the infrared temperature detection model, the deflection degree of the insulator is calculated through an image processing algorithm, and whether windage yaw phenomenon occurs in the insulator can be accurately judged through combining the infrared image and the visible light processing algorithm.
Referring now to fig. 6, a schematic diagram of a computer device 600 suitable for use in implementing an electronic device (e.g., the server or terminal device shown in fig. 1) according to an embodiment of the present application is shown. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 6, the computer apparatus 600 includes a Central Processing Unit (CPU)601 and a Graphics Processing Unit (GPU)602, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)603 or a program loaded from a storage section 609 into a Random Access Memory (RAM) 606. In the RAM 604, various programs and data necessary for the operation of the apparatus 600 are also stored. The CPU 601, GPU602, ROM 603, and RAM 604 are connected to each other via a bus 605. An input/output (I/O) interface 606 is also connected to bus 605.
The following components are connected to the I/O interface 606: an input portion 607 including a keyboard, a mouse, and the like; an output section 608 including a display such as a Liquid Crystal Display (LCD) and a speaker; a storage section 609 including a hard disk and the like; and a communication section 610 including a network interface card such as a LAN card, a modem, or the like. The communication section 610 performs communication processing via a network such as the internet. The driver 611 may also be connected to the I/O interface 606 as needed. A removable medium 612 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 611 as necessary, so that a computer program read out therefrom is mounted into the storage section 609 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via the communication section 610, and/or installed from the removable media 612. The computer programs, when executed by a Central Processing Unit (CPU)601 and a Graphics Processor (GPU)602, perform the above-described functions defined in the methods of the present application.
It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable medium or any combination of the two. The computer readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or any combination of the foregoing. More specific examples of the computer readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based devices that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present application may be implemented by software or hardware. The modules described may also be provided in a processor.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement a method of wire windage yaw recognition.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A wire windage yaw identification method based on infrared images is characterized by comprising the following steps:
s1, acquiring an infrared image of the power transmission line in the area to be detected;
s2, reading the infrared image, detecting the insulator region of the power transmission line based on a yolov5S target detection model, and further extracting the insulator region;
s3, detecting the insulator region through an infrared temperature detection model, acquiring the temperature in the insulator region, and calculating the deflection degree of the insulator region through an image processing algorithm; and
s4, judging whether the temperature of the insulator region accords with a normal value or not, judging whether the insulator region is in a normal state or not, and further judging whether the power transmission line generates windage yaw or not.
2. The method for identifying the windage yaw of the lead wire based on the infrared image as claimed in claim 1, wherein S2 detects the infrared image based on yolov5S target detection model, and further comprises:
s21, collecting a certain number of pictures of the insulator region, and manually marking by using an image marking tool Ldelme to obtain a data set;
s22, the acquired data set is recorded in a mode that 8: 2, dividing the ratio into a training set and a test set;
s23, reading the training set by using yolov5S for training, and obtaining a target detection model based on yolov 5S;
and S24, detecting a target detection model based on yolov5S by using the test set.
3. The method for identifying the windage yaw of the wire based on the infrared image as claimed in claim 2, wherein S3 further includes establishing the infrared temperature detection model:
s31, acquiring infrared images of the insulator region shot at different time, same place and same angle, and classifying the infrared images according to time periods;
s32, marking the temperature of each point in the insulation sub-area in the infrared image through an unmanned aerial vehicle (SDK);
s33, further converting the infrared image into a gray image;
s34, fitting the gray value and the temperature value corresponding to each point through a naive Bayes algorithm;
and S35, establishing the infrared temperature detection model.
4. The infrared image-based wire windage yaw recognition method as claimed in claim 3, wherein the naive Bayes formula is expressed as follows in S34
Where T represents temperature, G represents gray scale value, the conditional distribution of temperature T can be expressed as,
obtaining a classification model of the gray value corresponding to the temperature,
classify(g)＝argmax p(T＝t)
5. the infrared image-based lead windage yaw recognition method of claim 4, wherein the image processing of the insulator region in S3 comprises:
s311, preprocessing the extracted image of the insulator region;
s312, segmenting the image of the insulator region, and further performing morphological processing;
s313, extracting the framework of the insulator string in the insulator region, and further calculating to obtain end point coordinates of two ends of the framework;
and S314, calculating the offset of the insulator string according to the endpoint coordinates, and further judging the offset degree of the insulator region.
6. The infrared image-based wire windage yaw recognition method of claim 5, wherein in S312, the morphological processing comprises a dilation operation and a erosion operation in mathematical morphology.
7. The infrared image-based wire windage yaw recognition method according to claim 6, wherein in S313, the offset of the insulator string is calculated by using a windage yaw angle calculation formula, and the windage yaw angle calculation formula specifically comprises:
wherein (x)1,y1)、(x2,y2) And the end point is the end point of the insulator string after windage yaw.
8. A wire windage yaw recognition system based on infrared images is characterized by comprising:
the infrared image acquisition module is configured for acquiring an infrared image of the power transmission line in the area to be detected;
the infrared image processing module is configured to detect and extract the insulator region, and perform preprocessing, segmentation processing and morphological processing on the extracted image of the insulator region;
the infrared temperature detection module is configured to detect the insulator region by using an infrared temperature detection model and acquire the temperature in the insulator region;
the framework extraction module is configured for extracting the framework of the insulator string in the insulator region;
the calculation module is configured to calculate end point coordinates of two ends of the framework and calculate the offset of the insulator string according to the end point coordinates;
and the judging module is configured to judge whether the temperature of the insulator region meets a normal value, judge whether the insulator region is in a normal state, and further judge whether the power transmission line generates windage yaw.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114878005A (en) * | 2022-04-28 | 2022-08-09 | 广州中科云图智能科技有限公司 | Temperature detection method, device, system, electronic equipment and storage medium |
| CN115421006A (en) * | 2022-08-17 | 2022-12-02 | 广州科易光电技术有限公司 | Insulator string defect detection method, equipment terminal and storage medium |
| CN116256586A (en) * | 2023-05-10 | 2023-06-13 | 广东电网有限责任公司湛江供电局 | Overheat detection method and device for power equipment, electronic equipment and storage medium |
| CN117629079A (en) * | 2024-01-26 | 2024-03-01 | 智洋创新科技股份有限公司 | Power transmission wire windage yaw monitoring method and device based on data analysis and calibration object |
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2021
- 2021-12-09 CN CN202111498676.4A patent/CN114220009A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114878005A (en) * | 2022-04-28 | 2022-08-09 | 广州中科云图智能科技有限公司 | Temperature detection method, device, system, electronic equipment and storage medium |
| CN115421006A (en) * | 2022-08-17 | 2022-12-02 | 广州科易光电技术有限公司 | Insulator string defect detection method, equipment terminal and storage medium |
| CN116256586A (en) * | 2023-05-10 | 2023-06-13 | 广东电网有限责任公司湛江供电局 | Overheat detection method and device for power equipment, electronic equipment and storage medium |
| CN117629079A (en) * | 2024-01-26 | 2024-03-01 | 智洋创新科技股份有限公司 | Power transmission wire windage yaw monitoring method and device based on data analysis and calibration object |
| CN117629079B (en) * | 2024-01-26 | 2024-05-10 | 智洋创新科技股份有限公司 | Power transmission wire windage yaw monitoring method and device based on data analysis and calibration object |
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