Power transmission line galloping detection method and device based on gray level fluctuation field and gray level accumulation

Abstract

The application discloses a power transmission line galloping detection method and device based on gray scale fluctuation field and gray scale accumulation, and the method comprises the following steps: collecting a power transmission line field monitoring video; pre-processing the collected video, and establishing a power transmission line scene model based on video image information; calculating the average gray scale value of each pixel point in the video image, and constructing a random gray scale fluctuation field; performing difference processing on the video image frame by frame and the background model, performing binaryzation on the segmentation result by using the random gray scale fluctuation field and the 3sigma criterion, and segmenting the power transmission line; performing gray scale accumulation on the power transmission line segmentation result, and performing power transmission line motion mapping; statistically averaging the power transmission line motion amplitude by using a threading method, and obtaining an average galloping amplitude; comparing the average galloping amplitude with the amplitude when the power transmission line is static, and judging whether galloping occurs or not. The application can detect the galloping of the power transmission line without relying on interval rods, and a higher detection accuracy can be obtained.

Description

TECHNICAL FIELD

[0001] The application relates to a power transmission line galloping detection method and device based on a gray scale fluctuation field and gray scale accumulation, and belongs to the technical field of power transmission line detection and image processing. BACKGROUND

[0002] Power transmission line galloping is a low-frequency large-amplitude self-excited vibration phenomenon of a power transmission line caused by adverse weather conditions such as wind and snow. The phenomenon can cause problems such as bolt loosening, falling, power transmission line breakage, inter-phase tripping, power transmission line ablation and the like, and seriously affects the safe and stable operation of a power system.

[0003] At present, many scholars have proposed various solutions to the problem of power transmission line galloping detection. These solutions can be roughly divided into three categories: sensor technology, positioning technology and video online detection technology. (1) In the research paper “Research and application of power transmission line galloping monitoring system based on acceleration sensor” by Geng Liang, an acceleration sensor installed on the power transmission line is used to obtain the acceleration information of the power transmission line galloping, and the acceleration data is converted into displacement data of the power transmission line galloping, so as to realize the detection of the power transmission line galloping. (2) In the research paper “Power transmission line galloping monitoring system based on Beidou ground enhancement system” by Zhang Zihao, a Beidou positioning module installed on the power transmission line is used to collect the position information of the power transmission line, so as to obtain the displacement change value of the monitoring point to determine the galloping condition of the power transmission line. (3) In the research paper “Power transmission line galloping monitoring based on digital image processing technology” by Li Zhenjia, edge detection and contour extraction are used to detect the spacer bar of each frame, and then the edge points are sequentially found out for contour tracking, and the difference between the maximum value and the minimum value of the vertical coordinates of the same spacer bar is defined as the galloping amplitude of the power transmission line. (4) In the research paper “Power transmission line galloping detection based on spacer bar tracking” by Ren Jiaoying, the threading method is used to position the power transmission line, and the rotating projection positioning and Laws energy are used to position the spacer bar on the positioned power transmission line, and the KCF tracking algorithm is used to obtain the position of the spacer bar of each frame, so as to obtain the amplitude and frequency of the galloping.

[0004] Most of the current researches are to indirectly reflect the galloping condition of the power transmission line by analyzing the state of the spacer bar on the power transmission line, which is not suitable for the scene of the power transmission line without spacer bar separation. SUMMARY

[0005] In order to solve the above problems, the application provides a power transmission line galloping detection method and device based on a gray scale fluctuation field and gray scale accumulation, which can detect the galloping of the power transmission line without relying on the spacer bar, and can obtain a high detection accuracy.

[0006] The technical scheme adopted by the application to solve the technical problems is as follows:

[0007] In one aspect, the embodiment of the present application provides a power line galloping detection method based on gray level fluctuation field and gray level accumulation, comprising the following steps:

[0008] Collecting a power line field monitoring video;

[0009] Pretreating the collected video and establishing a power line scene model based on video image information;

[0010] Calculating the average gray level value of each pixel point in the video image and constructing a random gray level fluctuation field;

[0011] Differentially processing the video image frame by frame with the background model, binarizing the segmentation result by using the random gray level fluctuation field and 3σ criterion, and segmenting the power line;

[0012] Carrying out gray level accumulation on the power line segmentation result and carrying out power line motion mapping;

[0013] Statistically averaging the power line motion amplitude by using the threading method to obtain an average galloping amplitude;

[0014] Comparing the average galloping amplitude with the amplitude at rest to determine whether galloping occurs.

[0015] As a possible implementation manner of the embodiment, the pretreating the collected video and establishing a power line scene model based on video image information comprises:

[0016] Pretreating the collected video by using median filtering and carrying out gray level processing on the image;

[0017] Statistically analyzing the gray level distribution of each pixel point and defining the gray level value with the highest probability as the gray level value of the current pixel point in the background model:

[0018] A(x,y)=I i (x,y) whenP i (x,y)=max(P1(x,y),P2(x,y),...,P n (x,y)) i=1,2,…,n

[0019] Wherein, A(x,y) represents the gray level value of the power line scene model at (x,y), I i (x,y) represents the gray level value of the pixel point (x,y) in the i-th frame, P i (x,y) represents the probability of the gray level value of the pixel point (x,y) in the i-th frame, and n represents the total frame number of the video.

[0020] As a possible implementation manner of the embodiment, the calculating the average gray level value of each pixel point in the video image and constructing a random gray level fluctuation field comprises:

[0021] Calculate the average gray value I of each pixel point (x, y) of the current video μ (x, y):

[0022]

[0023] wherein, I i (x, y) represents the gray value of pixel point (x, y) in the i-th frame;

[0024] The statistical situation of gray fluctuation is described by using the standard deviation of the gray value of the current video, so as to construct a random gray fluctuation field B(x, y):

[0025]

[0026] wherein, I μ (x, y) represents the average gray value of each pixel point.

[0027] As a possible implementation manner of the embodiment, the video image is frame by frame differentially processed with the background model, the segmentation result is binarized by using the random gray fluctuation field and the 3σ criterion, and the power transmission line is segmented, which comprises:

[0028] The video image is frame by frame differentially processed with the background model:

[0029] D i (x, y) = A(x, y) - I i (x, y)

[0030] wherein, I i (x, y) represents the gray value of pixel point (x, y) in the i-th frame, D i (x, y) represents the differential result at (x, y);

[0031] The segmentation result is binarized by using the random gray fluctuation field and the 3σ criterion, so as to obtain a binarization result E i (x, y):

[0032]

[0033] wherein, m is a multiple of σ, and takes values of 1, 2, 3, …, and σ is a general gray fluctuation situation of the whole video.

[0034] As a possible implementation manner of the embodiment, the σ is represented by using the mean value of the random gray fluctuation field:

[0035]

[0036] wherein, w and h respectively represent the width and height of the image.

[0037] As a possible implementation manner of the embodiment, the formula of the motion mapping F(x, y) of the transmission line is as follows:

[0038]

[0039] wherein, E i (x, y) represents the binarization result of the segmented video.

[0040] As a possible implementation manner of the embodiment, the statistical average of the motion amplitude of the transmission line by the threading method is used to obtain the average amplitude of the galloping, comprising:

[0041] The sky area is segmented from the binarization graph F(x, y) by using the method of the maximum connected domain;

[0042] The threading operation is performed on the segmented sky area of the transmission line by using the threading method, the number of white pixel points on each line is counted, and the average amplitude of the galloping is calculated:

[0043]

[0044] wherein, d represents the average amplitude of the galloping, L represents the number of the transmission lines in the video, l represents the number of the threads, N j,k represents the number of intersection points of the jth transmission line and the kth thread.

[0045] As a possible implementation manner of the embodiment, the comparison between the average amplitude of the galloping and the amplitude in the static state is used to judge whether the galloping occurs, comprising:

[0046] The static amplitude of the transmission line when the transmission line field monitoring video is collected is recorded as the average amplitude of the galloping d1, the average amplitude of the galloping d2 in the subsequent video image is calculated, and the distance d2-d1 of the galloping is calculated to judge whether the galloping occurs.

[0047] On the other hand, the embodiment of the present application provides a transmission line galloping detection device based on a gray scale fluctuation field and a gray scale accumulation, comprising:

[0048] A video collection module is used to collect the transmission line field monitoring video.

[0049] A transmission line scene modeling module is used to pre-process the collected video and establish a transmission line scene model based on the video image information.

[0050] A random gray scale fluctuation field construction module is used to calculate the average gray scale value of each pixel point in the video image and construct a random gray scale fluctuation field.

[0051] A transmission line segmentation module is used to perform the difference processing on the video image frame by frame and the background model, perform the binarization on the segmentation result by using the random gray scale fluctuation field and the 3σ criterion, and segment the transmission line.

[0052] a gray scale accumulation module configured to perform gray scale accumulation on the power line segmentation result and perform motion mapping of the power line;

[0053] an average galloping amplitude calculation module configured to statistically average the power line motion amplitude by using the threading method to obtain an average galloping amplitude;

[0054] a power line galloping judgment module configured to compare the average galloping amplitude with the amplitude at rest to determine whether galloping occurs.

[0055] In a third aspect, a computer device is provided, which includes a processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the AGV simulation device is running, the processor and the memory communicate through the bus, and the processor executes the machine readable instructions to perform the steps of any of the power line galloping detection methods based on the gray scale fluctuation field and the gray scale accumulation.

[0056] In a fourth aspect, a storage medium is provided, which stores a computer program, when the computer program is run by a processor, the steps of any of the power line galloping detection methods based on the gray scale fluctuation field and the gray scale accumulation are performed.

[0057] The technical scheme of the embodiment of the present application can have the following beneficial effects:

[0058] The video online monitoring technology adopted by the present application does not need to install corresponding equipment on the power line, does not affect the line, and can detect the galloping of the power line and calculate the galloping amplitude without relying on the spacer.

[0059] The present application only maps the motion of the power line to achieve the purpose of power line motion detection, and can obtain high detection accuracy without complex feature extraction of the power line.

[0060] The present application adopts the background difference method to segment the power line, combines the random gray scale fluctuation field and the 3σ criterion to binarize the segmentation result, and eliminates the influence of the gray scale fluctuation phenomenon caused by camera shooting on the power line segmentation.

[0061] The present application can detect the galloping of the power line and calculate the galloping amplitude without relying on the spacer, and can obtain high detection accuracy without complex feature extraction of the power line. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 is a flowchart of a power line galloping detection method based on a gray scale fluctuation field and gray scale accumulation according to an exemplary embodiment;

[0063] Figure 2 Fig. 1 is a schematic diagram of a power line galloping detection device based on gray level fluctuation field and gray level accumulation according to an exemplary embodiment;

[0064] Figure 3 Fig. 2 is a flow chart of power line galloping detection using the device according to the present application according to an exemplary embodiment;

[0065] Fig. 4(a), Fig. 4(b) and Fig. 4(c) are modeling effect diagrams of power line scenes according to an exemplary embodiment (wherein Fig. 4(a) is an image of the 20th frame of a video, Fig. 4(b) is an image of the 65th frame of a video, and Fig. 4(c) is a constructed mode background model);

[0066] Figure 5 Fig. 5 is a random gray level fluctuation field effect diagram according to an exemplary embodiment;

[0067] Figure 6 Fig. 6 is a distribution histogram of gray level fluctuation according to an exemplary embodiment;

[0068] Fig. 7(a), Fig. 7(b) and Fig. 7(c) are power line segmentation result diagrams according to an exemplary embodiment (wherein Fig. 7(a) is a mode background model, Fig. 7(b) is a current frame input, and Fig. 7(c) is a differential binarization of the current frame);

[0069] Fig. 8(a) and Fig. 8(b) are cumulative gray level model construction effect diagrams according to an exemplary embodiment (wherein Fig. 8(a) is a differential binarization of a current frame, and Fig. 8(b) is a cumulative effect of differential binarization of each frame of a video);

[0070] Fig. 9(a) and Fig. 9(b) are threading method schematic diagrams according to an exemplary embodiment (wherein Fig. 9(a) is a segmented sky area, and Fig. 9(b) is a threading method schematic diagram). DETAILED DESCRIPTION

[0071] The present application is further described below in conjunction with the accompanying drawings and embodiments:

[0072] For purposes of clarity, the present application will be described with reference to exemplary implementations thereof that are illustrated in the drawings. The following detailed description is presented in terms of specific embodiments or examples which implement the application. As such, the detailed description serves to illustrate the application and should not be taken as limiting. In the following description, numerous specific details are discussed to provide a thorough understanding of the present application. However, one of ordinary skill in the relevant art will recognize that the application can be practiced without one or more of the specific details. In other instances, well-known methods, procedures, components, and networks have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0073] Since the video online monitoring technology does not need to install corresponding equipment on the power transmission line and does not affect the line, the application provides a power transmission line galloping detection method based on a random gray level fluctuation field and a cumulative gray level model.

[0074] As shown in Figure 1 , the application provides a power transmission line galloping detection method based on a random gray level fluctuation field and a cumulative gray level model, which comprises the following steps:

[0075] Collecting a power transmission line field monitoring video;

[0076] Pretreating the collected video and establishing a power transmission line scene model based on video image information;

[0077] Calculating the average gray level value of each pixel point in the video image and constructing a random gray level fluctuation field;

[0078] Differentially processing the video image frame by frame with the background model, binarizing the segmentation result by using the random gray level fluctuation field and the 3σ criterion, and segmenting the power transmission line;

[0079] Performing gray level accumulation on the power transmission line segmentation result and performing motion mapping of the power transmission line;

[0080] Statistically averaging the power transmission line motion amplitude by using the threading method to obtain an average galloping amplitude;

[0081] Comparing the average galloping amplitude with the amplitude when the power transmission line is static to determine whether galloping occurs.

[0082] As a possible implementation manner of the present embodiment, the pretreating the collected video and establishing a power transmission line scene model based on video image information comprises:

[0083] Pretreating the collected video by using median filtering to eliminate the interference of noise and performing gray level processing on the image;

[0084] The gray scale distribution of each pixel point is counted, and the gray scale value with the highest probability is defined as the gray scale value of the current pixel point in the background model, and the formula is as follows:

[0085] A(x,y) = I i (x,y) whenP i (x,y) = max(P1(x,y), P2(x,y),..., P n (x,y)) i = 1, 2,..., n

[0086] Wherein, A(x,y) represents the gray scale value of the power line scene model at (x,y), I i (x,y) represents the gray scale value of pixel point (x,y) in the i-th frame, P i (x,y) represents the probability of the gray scale value of pixel point (x,y) in the i-th frame, and n represents the total number of frames of the video.

[0087] As a possible implementation manner of the embodiment, the average gray scale value of each pixel point in the video image is calculated, and a random gray scale fluctuation field is constructed, including:

[0088] The average gray scale value I μ (x,y) of each pixel point (x,y) of the current video is calculated, and the formula is as follows:

[0089]

[0090] Wherein, I i (x,y) represents the gray scale value of pixel point (x,y) in the i-th frame;

[0091] The statistical situation of gray scale fluctuation is described by using the standard deviation of the gray scale value of the current video, so as to construct a random gray scale fluctuation field B(x,y), and the formula is as follows:

[0092]

[0093] Wherein, I μ (x,y) represents the average gray scale value of each pixel point.

[0094] As a possible implementation manner of the embodiment, the video image is frame by frame and the background model is differentially processed, the segmentation result is binarized by using the random gray scale fluctuation field and the 3σ criterion, and the power line is segmented, including:

[0095] The video image is frame by frame and the background model is differentially processed, and the formula is as follows:

[0096] D i (x,y) = A(x,y) - I i (x,y)

[0097] wherein, I i (x,y) represents the gray value of pixel point (x,y) in the i-th frame, D i (x,y) represents the difference result at (x,y).

[0098] The segmentation result is binarized by using a random gray fluctuation field and a 3σ criterion to obtain a binarization result E i (x,y), and the formula is as follows:

[0099]

[0100] wherein, m is a multiple of σ, and takes values of 1, 2, 3,..., and σ is a general gray fluctuation of the entire video, and is represented by using the mean value of the random gray fluctuation field.

[0101] As a possible implementation manner of the embodiment, the σ is a general gray fluctuation of the entire video, and is represented by using the mean value of the random gray fluctuation field, and the formula is as follows:

[0102]

[0103] wherein, w and h respectively represent the width and height of the image.

[0104] As a possible implementation manner of the embodiment, the formula of the motion mapping F(x,y) of the transmission line is as follows:

[0105]

[0106] wherein, E i (x,y) represents the binarization result of the segmented video.

[0107] As a possible implementation manner of the embodiment, the average dance amplitude is obtained by using the threading method to statistically average the transmission line motion amplitude, and the average dance amplitude includes:

[0108] The sky area is segmented from the binarization graph F(x,y) by using the maximum connected domain method;

[0109] The transmission line in the segmented sky area is operated by using the threading method, the number of white pixel points on each line is counted, and the formula for calculating the average dance amplitude is as follows:

[0110]

[0111] wherein, d represents the average dance amplitude, L represents the number of transmission lines in the video, l represents the number of threads, N j,k represents the number of intersection points of the j-th transmission line and the k-th thread.

[0112] As a possible implementation manner of the embodiment, the comparison between the average dancing amplitude and the amplitude in the static state is used to determine whether the dancing occurs, that is, the average dancing amplitude d1 in the initialization is recorded in the initialization according to the above method, the static amplitude of the current transmission line is represented, the average dancing amplitude d2 in the subsequent detection is calculated, the amplitude of the transmission line in the detection is represented, and the comparison between d1 and d2 can determine whether the dancing occurs and calculate the dancing distance d2-d1.

[0113] As shown in Figure 2 The embodiment of the application provides a transmission line dancing detection device based on a random gray scale fluctuation field and a cumulative gray scale model, which comprises:

[0114] A video acquisition module is configured to acquire a transmission line field monitoring video.

[0115] A transmission line scene modeling module is configured to pre-process the acquired video and establish a transmission line scene model based on video image information.

[0116] A random gray scale fluctuation field construction module is configured to calculate the average gray scale value of each pixel point in the video image and construct a random gray scale fluctuation field.

[0117] A transmission line segmentation module is configured to perform frame-by-frame difference processing on the video image and the background model, perform binaryzation on the segmentation result by using the random gray scale fluctuation field and the 3σ criterion, and segment the transmission line.

[0118] A gray scale accumulation module is configured to perform gray scale accumulation on the transmission line segmentation result and perform motion mapping of the transmission line.

[0119] An average dancing amplitude calculation module is configured to statistically average the transmission line motion amplitude by using the threading method to obtain the average dancing amplitude.

[0120] A transmission line dancing determination module is configured to compare the average dancing amplitude with the amplitude in the static state to determine whether the dancing occurs.

[0121] As shown in Figure 3 The process of the device for detecting the transmission line dancing by using the real-time acquired transmission line field monitoring video comprises the following steps:

[0122] Step 1: As shown in FIG. 4, the acquired video is pre-processed, and the transmission line scene model is established by using statistical information.

[0123] In the first step, the acquired video is pre-processed by using the median filter to eliminate the interference of noise and perform gray scale processing on the image.

[0124] Second step, statistics of each pixel point gray distribution, and the probability of the highest gray value defined as the background model in the current pixel gray value, formula as follows:

[0125] A(x,y) = I i (x,y) whenP i (x,y) = max(P1(x,y), P2(x,y),..., P n (x,y)) i = 1, 2,..., n

[0126] Where, A(x,y) represents the power transmission line scene model in (x,y) at the gray value, I i (x,y) represents the pixel point (x,y) in the i frame of the gray value, P i (x,y) represents the pixel point (x,y) in the i frame of the gray value appears probability, n represents the total frame number of video.

[0127] Step 2: calculate the average gray value of each pixel point, construct random gray fluctuation field;

[0128] First step, calculate the average gray value of each pixel point (x,y) of the current video I μ (x,y), formula as follows:

[0129]

[0130] Where, I i (x,y) represents the pixel point (x,y) in the i frame of the gray value;

[0131] Second step, as shown in Figure 5 And Figure 6 , using the gray value standard deviation of the current video to describe the statistical situation of gray fluctuation, thereby constructing random gray fluctuation field B(x,y), formula as follows:

[0132]

[0133] Where, I μ (x,y) represents the average gray value of each pixel point.

[0134] Step 3: as shown in Figure 7, frame by frame and background model difference, the segmentation result is binarized by using random gray fluctuation field and 3σ criterion, and the power transmission line is segmented;

[0135] First step, frame by frame and background model difference, formula as follows:

[0136] D i (x,y) = A(x,y) - I i (x,y)

[0137] wherein (x, y) represents the gray value of pixel point (x, y) in the i-th frame, and (x, y) represents the differential result at (x, y);

[0138] Secondly, the segmentation result is binarized by using a random gray fluctuation field and a 3σ criterion to obtain a binarization result E i (x, y), and the formula is as follows:

[0139]

[0140] wherein m is a multiple of σ, and the value is 1, 2, 3, …, σ is a general gray fluctuation of the whole video, and is represented by using the mean value of the random gray fluctuation field, and the formula is as follows:

[0141]

[0142] wherein w and h respectively represent the width and height of the image.

[0143] Step 4: As shown in FIG. 8, the gray accumulation of the transmission line segmentation result of a video is performed to realize the motion mapping of the transmission line, and the formula is as follows:

[0144]

[0145] wherein E i (x, y) represents the binarization result of the segmented video.

[0146] Step 5: The motion amplitude of the transmission line is statistically averaged by using the threading method to obtain the average amplitude of the dancing;

[0147] Firstly, as shown in FIG. 9(a), the sky area is segmented by using the maximum connected domain method in the binarization graph F(x, y);

[0148] Secondly, as shown in FIG. 9(b), the threading operation is performed on the segmented transmission line of the sky area by using the threading method, the number of white pixel points on each line is counted, and the formula is as follows:

[0149]

[0150] wherein d represents the average amplitude of the dancing, L represents the number of transmission lines in the video, l represents the number of threads, and N j,k represents the number of intersection points of the j-th transmission line and the k-th thread.

[0151] Step 6: Comparing with the amplitude in static state to determine whether the galloping occurs, that is, initializing in the camera installation according to the above method, recording the average galloping amplitude d1, indicating the static amplitude of the current power transmission line, calculating the average galloping amplitude d2 in the subsequent detection, indicating the amplitude of the power transmission line in the detection, comparing d1 with d2, so as to determine whether the galloping occurs and calculate the distance of the galloping d2-d1.

[0152] The 146 segments of 15s video are divided into 292 segments of 7-8s video, and the detection accuracy P w , false positive rate E w and false negative rate L w are used to evaluate the results. Different types of spacer rods have different detection accuracies. The experimental results for different cases are shown in Table 1.

[0153] Table 1: Detection results on different types of power transmission lines

[0154]

[0155] In a third aspect, a computer device includes a processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the computer device is running, the processor and the memory communicate through the bus, and the processor executes the machine readable instructions to perform the steps of the power transmission line galloping detection method based on the random gray scale fluctuation field and the cumulative gray scale model as described above.

[0156] The computer device provided by the embodiment of the application includes a processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the device is running, the processor and the memory communicate through the bus, and the processor executes the machine readable instructions to perform the steps of the power transmission line galloping detection method based on the random gray scale fluctuation field and the cumulative gray scale model as described above.

[0157] Specifically, the above-mentioned memory and processor can be general memory and processor, which are not specifically limited here, and when the processor runs the computer program stored in the memory, the power transmission line galloping detection method based on the random gray scale fluctuation field and the cumulative gray scale model can be executed.

[0158] Those skilled in the art can understand that the structure of the computer device does not constitute a limitation on the computer device, and can include more or fewer components than the illustration, or combine certain components, or split certain components, or different component arrangements.

[0159] In some embodiments, the computer device can further include a touch screen which can be used to display a graphical user interface (e.g., a start interface of an application) and receive user operations for the graphical user interface (e.g., a start operation for the application). The touch screen can include a display panel and a touch panel. The display panel can be configured in the form of an LCD (Liquid Crystal Display), an OLED (Organic Light-Emitting Diode), or the like. The touch panel can collect contact or non-contact operations of a user thereon or therearound, and generate a pre-set operation instruction, for example, operations of the user using a finger, a stylus, or any suitable object or accessory on or near the touch panel. In addition, the touch panel can include two parts of a touch detection device and a touch controller. The touch detection device detects the touch position and posture of the user, and detects a signal generated by the touch operation, and transmits the signal to the touch controller. The touch controller receives the touch information from the touch detection device, converts the touch information into information that can be processed by a processor, and sends the information to the processor. The touch controller can also receive and execute commands from the processor. In addition, the touch panel can be implemented in various types such as a resistive type, a capacitive type, an infrared type, and a surface acoustic wave type, or any technology developed in the future. Further, the touch panel can cover the display panel. The user can operate on or near the touch panel covering the display panel according to the graphical user interface displayed on the display panel. After the touch panel detects the operation thereon or therearound, the touch panel transmits the operation to the processor to determine the user input. Then, the processor provides a corresponding visual output on the display panel in response to the user input. In addition, the touch panel and the display panel can be implemented as two independent components or integrated.

[0160] Corresponding to the above application starting method, the embodiment of the present application further provides a storage medium, and the storage medium stores a computer program. When the computer program is run by a processor, the steps of any power line galloping detection method based on random gray scale fluctuation field and cumulative gray scale model are executed.

[0161] The application starting device provided by the embodiment of the present application can be specific hardware on the device or software or firmware installed on the device. The device provided by the embodiment of the present application has the same implementation principle and technical effects as the foregoing method embodiments. For brevity and conciseness, the part not mentioned in the device embodiment is referred to the corresponding content in the foregoing method embodiments. Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above can be referred to the corresponding process in the foregoing method embodiments, which will not be described herein.

[0162] Those skilled in the art will appreciate that embodiments of the application can be supplied as a method, a system, or a computer program product. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be in the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage media, etc.) embodying computer readable program code.

[0163] In the embodiments provided by the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. The embodiments described above are merely exemplary, and should not be used to limit the present application. For example, the division of the modules is only a logical function division, and there can be another division manner in actual implementation; for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the modules can be indirect coupling or communication connection through some interfaces, or electrical, mechanical or other forms.

[0164] The modules illustrated as separated components can or can not be physically separated, and the components illustrated as modules can or can not be physical modules, i.e., can be located in one place, or can be distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0165] In addition, each functional module in the embodiments provided by the present application can be integrated in a processing module, or each module can exist physically independently, or two or more modules can be integrated in one module.

[0166] The present application is described with reference to flowcharts and / or block diagrams according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce the functions described in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The apparatus that implements the functions specified in one block or multiple blocks.

[0167] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 The functions specified in the flow or flows and / or blocks Figure 1 The functions specified in the flow or flows and / or blocks

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flow Figure 1 The functions specified in the flow or flows and / or blocks Figure 1 The functions specified in the flow or flows and / or blocks

[0169] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the present application. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A power line galloping detection method based on gray level fluctuation field and gray level accumulation, comprising the following steps: collecting a power line field monitoring video; preprocessing the collected video and establishing a power line scene model based on video image information; calculating the average gray level value of each pixel point in the video image to construct a random gray level fluctuation field; differentially processing the video image frame by frame with the background model, binarizing the segmentation result using the random gray level fluctuation field and the 3σ criterion, and segmenting the power line; performing gray level accumulation on the power line segmentation result to map the power line movement; statistically averaging the power line movement amplitude using the threading method to obtain the average galloping amplitude; comparing the average galloping amplitude with the amplitude when the power line is stationary to determine whether galloping occurs; the preprocessing of the collected video and the establishment of the power line scene model based on the video image information comprise: preprocessing the collected video using median filtering and performing gray scale processing on the image; Statistical gray distribution of each pixel, and the probability of the highest gray value is defined as the background model A The gray value of the current pixel i =1,2,…,n in, Represents the transmission line scene model in ( x , y ), Represents a pixel ( x , y ) i Grayscale value of the frame, Represents a pixel ( x , y ) i The probability of the gray value of the frame appearing, n Indicates the total number of frames in the video; the calculation of the average gray level value of each pixel point in the video image to construct a random gray level fluctuation field comprises: calculating the average gray level value of each pixel point in the current video; using the gray level standard deviation of the current video to describe the statistical situation of the gray level fluctuation to construct a random gray level fluctuation field; the differential processing of the video image frame by frame with the background model, the binarization of the segmentation result using the random gray level fluctuation field and the 3σ criterion, and the segmentation of the power line comprise: differentially processing the video image frame by frame with the background model; in, Represents a pixel ( x , y ) i Grayscale value of the frame, express( x , y ) difference results at ; The segmentation result is binarized using a random gray level fluctuation field and a 3σ criterion to obtain a binarized result : wherein m is a multiple of σ, and the value is 1, 2, 3, …, and σ is a general gray level fluctuation of the entire video; σ is represented by the mean value of the random gray level fluctuation field: wherein w and h represent the width and height of the image, respectively.

2. The galloping detection method based on gray level rising field and gray level accumulation of power transmission line according to claim 1, characterized in that, the formula of the average gray level value of each pixel point in the current video is: in, Indicates the average grayscale value of each pixel (x, y) in the current video. Represents a pixel ( x , y ) i Grayscale value of the frame; the formula of the random gray level fluctuation field is: wherein, represents the constructed random gray scale fluctuation field, represents the average gray scale value of each pixel point.

3. The method of claim 1, wherein the method is characterized by: the formula of the power line movement mapping F(x, y) is as follows: wherein, represents the binarization result of the divided video. 4.The power line galloping detection method based on gray level fluctuation field and gray level accumulation according to claim 1, the statistical averaging of the power line movement amplitude using the threading method to obtain the average galloping amplitude comprises: segmenting the sky area in the binarized image F(x, y) using the maximum connected domain method; performing threading operation on the segmented power line in the sky area using the threading method, counting the number of white pixel points on each line, and calculating the average galloping amplitude: wherein, d represents the average amplitude of the dance, L represents the number of power lines in the current video, represents the number of threads, represents the number of intersections between the first j power line and the first k thread.

5. The method for galloping detection of power transmission lines based on gray level rising field and gray level accumulation according to claim 1, characterized in that, the comparison of the average galloping amplitude with the amplitude when the power line is stationary to determine whether galloping occurs comprises: recording the power line stationary amplitude when the power line field monitoring video is collected as the average galloping amplitude d1, calculating the power line average galloping amplitude d2 in the subsequent video image, and calculating the galloping distance d2-d1 to determine whether galloping occurs.

6. A galloping detection device for a power transmission line based on a gray scale fluctuation field and a gray scale accumulation, characterized by The device for implementing the power line galloping detection method based on gray level fluctuation field and gray level accumulation according to any one of claims 1-5 comprises: a video collection module for collecting a power line field monitoring video; a power line scene modeling module for preprocessing the collected video and establishing a power line scene model based on video image information; A random gray scale fluctuation field construction module is configured to calculate the average gray scale value of each pixel point in a video image and construct a random gray scale fluctuation field; A power transmission line segmentation module is configured to perform frame-by-frame difference processing on the video image and the background model, perform binaryzation on the segmentation result by using the random gray scale fluctuation field and the 3σ criterion, and segment the power transmission line; A gray scale accumulation module is configured to perform gray scale accumulation on the power transmission line segmentation result and perform motion mapping of the power transmission line; An average dance amplitude calculation module is configured to statistically average the motion amplitude of the power transmission line by using the threading method to obtain the average dance amplitude; A power transmission line dance judgment module is configured to compare the average dance amplitude with the amplitude when the power transmission line is static to determine whether the power transmission line dances.

7. A computer device, characterized by A computer device includes a processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the computer device runs, the processor and the memory communicate through the bus, and the processor executes the machine readable instructions to perform the steps of the power transmission line dance detection method based on the gray scale fluctuation field and the gray scale accumulation as claimed in any one of claims 1-5.

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