CN114241364A - Method for quickly calibrating foreign object target of overhead transmission line - Google Patents
Method for quickly calibrating foreign object target of overhead transmission line Download PDFInfo
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Abstract
The invention discloses a method for quickly calibrating a foreign object target of an overhead transmission line, and provides a method for quickly calibrating a foreign object target of an overhead transmission line, aiming at the problem that the conventional tool and method are difficult to efficiently and safely remove the foreign object of the overhead transmission line. Firstly, a foreign object video image is obtained in real time, the video image is processed and identified, a target area is selected, and the foreign object target is rapidly identified and calibrated by utilizing an image feature point-ORB feature point matching fusion algorithm, so that the accuracy of foreign object target positioning is greatly increased, the operation difficulty and human errors of operators are reduced, and the foreign object target identification and tracking effect close to 100% can be realized.
Description
Technical Field
The invention belongs to the field of power transmission safety, and particularly relates to a method for quickly calibrating a foreign object target of an overhead power transmission line.
Background
The electric power is the foundation of national economy and social development, and an electric power system is taken as an energy support for supporting the national economy development, plays an irreplaceable role in the current social development, is related to the stable development of the whole society, and the safe and stable operation of a power grid is closely related to the national economy development and is also the foundation of the stable living and music industry and the social stability of people. With the high-speed development of power grids in China, the scale of power transmission lines is larger and larger, the number of power transmission line equipment is increased day by day, the length of the power transmission lines under complex terrain conditions and severe environments is increased day by day, the number of the power transmission lines which need to be specially inspected and maintained is increased day by day, inspection and maintenance of the power transmission lines are difficult, and the technical content is high.
In such a power transmission grid, power system faults caused by various reasons are avoided, and in various accidents of the power grid, non-insulating objects such as kites, long cloth strips and plastic bags are hung among power transmission lines to cause short circuit among the lines, so that a switch of a transformer substation is tripped. The foreign matter is eliminated in live working is the item that the safety risk is the highest among the live working, and the main methods of traditional high voltage line of clearing colluding the foreign matter have two kinds: firstly, an electrician is removed from the line after power failure; and secondly, removing the equipotential live line work. Both methods need to invest more manpower and material resources, and have the disadvantages of complex operation procedure, long time, high labor intensity and low safety and reliability. Meanwhile, as the types and winding modes of the foreign matters are various, the foreign matters are treated by the random strain of operation workers in the live working process; some foreign matters tightly wound with the transmission line components are high in danger, and only power failure treatment can be adopted, but power failure treatment directly reduces the reliability of power supply, social and economic losses are caused, and life of people is affected.
Therefore, the efficiency and the quality of the patrol of the power transmission line are improved by innovating and fusing new technologies, foreign matters on the overhead power transmission line are timely removed by a timely and efficient method, and the potential safety hazard of the power transmission line is solved in advance.
Laser is widely used in various industries as a novel foreign matter removing means. Different from common physical shearing, hooking and the like, the laser is removed by means of burning by increasing the temperature of the surface of the foreign matter. The power transmission line is mainly wound with plastic films, kites and other floating foreign matters, the materials are generally combustible materials with lower ignition points such as plastics, nylon, terylene and the like, the ignition points are generally below 300 ℃, and even when the foreign matters are removed by laser, the power transmission line is not substantially damaged by a mode of heating and fusing the foreign matters of the power transmission line.
Disclosure of Invention
The invention aims to provide a method for quickly calibrating a foreign object target of an overhead transmission line, which solves the problem of target identification of foreign objects wound on the overhead transmission line, and increases the flexibility, the maneuverability and the efficiency in the process of removing the foreign objects of the overhead transmission line, thereby achieving better control requirements.
The technical solution for realizing the invention is as follows: a method for quickly calibrating a foreign object target of an overhead transmission line comprises the following steps:
and 5, judging whether the foreign object still exists, if so, returning to the step 1, and if not, ending.
A foreign object target rapid calibration system for an overhead transmission line comprises the following modules:
an image processing module: the system comprises a frame, a target area video image acquisition unit, a target object acquisition unit, a display unit and a display unit, wherein the frame is used for acquiring a target area video image in real time, processing the image, setting a frame picture as an initial frame when the target object is identified, and amplifying the area video image of the target;
foreign object coordinate acquisition module: the system is used for further accurately identifying the amplified video image, resolving image information, and acquiring coordinate information of the foreign object target and coordinate information of the foreign object target at the winding position of the overhead transmission line;
a foreign matter tracking module: the system is used for tracking the foreign object target and identifying position state information of the foreign object target in a video image, wherein the position state information comprises image feature points, a mass center position and a moving track;
a foreign matter treatment module: and cutting the foreign matter by using laser, and judging whether the foreign matter target still exists.
Compared with the prior art, the invention has the following remarkable advantages:
(1) according to the technical scheme, the accuracy and precision of foreign object target identification are improved by adopting an improved image identification and tracking algorithm;
(2) the technical scheme of the invention uses a trained foreign body target data training set, combines ORB characteristic points and Mean-Shift operator prediction, and can quickly detect and identify the foreign body target to be detected.
The invention is further described with reference to the following figures and detailed description.
Drawings
Fig. 1 is a flow chart of an implementation of the overhead transmission line foreign object target identification method.
Fig. 2 is a mapping diagram of the gray histogram equalization algorithm of the present invention.
FIG. 3 is a diagram illustrating ORB feature point identification comparison in an embodiment of the present invention.
Detailed Description
A method for quickly calibrating a foreign object target of an overhead transmission line comprises the following steps:
step 1-1, acquiring a video image, performing gray level conversion on each acquired frame image, establishing a digital gray level image random mathematical model, mapping each pixel on an original image, and acquiring an image with enhanced gray level, wherein the specific steps are as follows:
step 1-1-1, constructing a digital gray image random mathematical model for the acquired image, and counting gray values of all pixels to obtain a gray distribution histogram, namely the gray level of the original image;
step 1-1-2, according to a segmentation threshold r in an original gray level image XcThe image is divided into two non-intersection subgraphs, and then a histogram equalization algorithm is applied to the two subgraphs respectively to carry out gray distribution normalization probability calculation:
wherein p isLd(dj) And PUd(dj) Respectively representing the gray normalization probabilities of the low-gray subgraph and the high-gray subgraph; wherein d iskL-1, L being the maximum value of the grey level, Pd(dj) For grey level d in the original imagejProbability of gray distribution of (P)d(dk) For grey level d in the original imagekThe gray level distribution probability of (1);
1-1-3, constructing a mapping relation from an original image gray level to a target image gray level, constructing a gray distribution probability density function of continuous variables, dividing a low gray area and a high gray area of an image according to the gray distribution probability density function, respectively obtaining the low gray area and the high gray area of the image, and obtaining a gray image of the high gray area, namely the image with enhanced gray.
Step 1-2, carrying out noise reduction on the image with enhanced gray scale by filtering by using a Gaussian blur method to obtain a noise-reduced image;
step 1-3, performing Gaussian filtering on the noise-reduced image to obtain a smooth image, specifically:
taking a Gaussian function value of a discrete point as a weight, carrying out window template collection on the noise-reduced picture, and carrying out weighted average on each pixel value in a gray matrix in the collected window template so as to eliminate Gaussian noise:
setting the size of the window template to be (2k +1) × (2k +1), then the weight coefficient of each element in the template is:
where (i, j) (i, j ═ 1, 2.., 2k +1) represents the relative coordinates of each pixel point within the window template.
And 1-4, detecting a foreign object target on the obtained smooth image by using a Canny edge detection algorithm, updating the frame image into an initial frame when the foreign object target is detected to be positioned at the center of an image picture, and amplifying a video image of a region where the target is positioned.
step 2-1, performing target edge detection on the amplified target area by using a Canny edge detection algorithm, detecting edges of the target area in four directions including the horizontal direction, the vertical direction and two diagonal lines, calculating the gradient strength and the direction of the edges, performing noise elimination on the edges, converting image information into Gaussian cells by using a Gaussian blur method, and eliminating the influence of noise on identification, wherein the method specifically comprises the following steps:
step 2-1-1, dividing the gradient direction of the image into four directions according to angles, wherein the four directions are respectively edges of 0 degrees, 45 degrees, 90 degrees and 135 degrees, comparing the sizes of other two values of the central value in the corresponding gradient direction in each 3 x 3 neighborhood, if the central value is the maximum value, retaining the central value, otherwise, inhibiting the central value and removing false edge points;
step 2-1-2, setting two gradient size thresholds: high threshold is mthhLow threshold value mthlThe image is divided into two edge maps as follows:
EHif M (x, y) ≧ M is ≧ e (x, y) |thhIf e (x, y) is 1; otherwise e (x, y) ═ 0}
ELIf M (x, y) ≧ M is ≧ e (x, y) |thlIf e (x, y) is 1; otherwise e (x, y) ═ 0}
Wherein M (x, y) represents the edge gradient value at the (x, y) coordinate in the image before segmentation, and e (x, y) represents the edge gradient value at the current (x, y) coordinate after segmentation;
step 2-1-3, map E of high thresholdHMarking all the corresponding positions of the pixels as edge points, and traversing EHAt all points in (1), using 8-way relation at the low threshold map ELAnd determining the final edge point to enhance the edge continuity and finish the target edge detection.
And 2-2, converting the image information of the amplified target area into an image identification matrix, and processing the image identification matrix by using Hough transformation detection to obtain the position coordinates of the foreign object target and the winding point of the overhead transmission line in the image.
step 3-1, taking a foreign matter region in an initial frame after the video image is updated as a reference frame, predicting a foreign matter target region in a current frame by using a Mean-Shift operator, and solving the Shift average value of foreign matter target feature points in the current frame by using a Mean-Shift algorithm:
wherein the content of the first and second substances,
is under the current reference pointCharacteristic point deviation mean, Ck,dRepresenting a normalized constant, k (x) is a contour function, H is a kernel function bandwidth parameter, d is a matrix dimension, H is a bandwidth matrix of d x d dimensions, x is a reference pointiIs d dimension European space RdN is the number of sample points in the d-dimensional space;
3-2, updating the current image, taking the foreign matter characteristic point offset mean value as a new reference point, and repeatedly and circularly updating until the characteristic point offset distance threshold is met;
3-3, predicting a foreign matter region of the current frame by using a Mean-Shift operator by using a feature point offset distance threshold as a matching basis, extracting ORB feature points of the foreign matter region in the current frame and a reference frame, and constructing a foreign matter region feature set through feature point matching;
3-4, extracting and screening new feature points from the feature set of the foreign body region of the current frame, updating the feature set of the foreign body region, eliminating unmatched feature points, and improving the accuracy of foreign body detection by continuously updating the feature points in the feature set of the foreign body region;
and 3-5, converting the spatial coordinates of the foreign object target in the image into angle coordinates in real time, and tracking the foreign object target.
and 5, judging whether the foreign object still exists, if so, returning to the step 1, and if not, ending.
A foreign object target rapid calibration system for an overhead transmission line comprises the following modules:
an image processing module: the system comprises a frame, a target area video image acquisition unit, a target object acquisition unit, a display unit and a display unit, wherein the frame is used for acquiring a target area video image in real time, processing the image, setting a frame picture as an initial frame when the target object is identified, and amplifying the area video image of the target;
foreign object coordinate acquisition module: the system is used for further accurately identifying the amplified video image, resolving image information, and acquiring coordinate information of the foreign object target and coordinate information of the foreign object target at the winding position of the overhead transmission line;
a foreign matter tracking module: the system is used for tracking the foreign object target and identifying position state information of the foreign object target in a video image, wherein the position state information comprises image feature points, a mass center position and a moving track;
a foreign matter treatment module: and cutting the foreign matter by using laser, and judging whether the foreign matter target still exists.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
and 5, judging whether the foreign object still exists, if so, returning to the step 1, and if not, ending.
A computer-storable medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
and 5, judging whether the foreign object still exists, if so, returning to the step 1, and if not, ending.
Examples
With reference to fig. 1, a method for quickly calibrating a foreign object target of an overhead transmission line includes the following steps:
step 1-1, acquiring a video image, performing gray level conversion on each acquired frame image, establishing a digital gray level image random mathematical model, mapping each pixel on an original image, and acquiring an image with enhanced gray level, wherein the specific steps are as follows:
step 1-1-1, constructing a digital gray image random mathematical model for the acquired image, and counting gray values of all pixels to obtain a gray distribution histogram, namely the gray level of the original image;
step 1-1-2, according to a segmentation threshold r in an original gray level image XcThe image is divided into two non-intersection subgraphs, and then a histogram equalization algorithm is applied to the two subgraphs respectively to carry out gray distribution normalization probability calculation:
wherein p isLd(dj) And PUd(dj) Respectively representing the gray normalization probabilities of the low-gray subgraph and the high-gray subgraph; wherein d iskL-1, L being the maximum value of the grey level, Pd(dj) For grey level d in the original imagejProbability of gray distribution of (P)d(dk) For grey level d in the original imagekThe gray level distribution probability of (1);
1-1-3, constructing a mapping relation from an original image gray level to a target image gray level, constructing a gray distribution probability density function of continuous variables, dividing a low gray area and a high gray area of an image according to the gray distribution probability density function, respectively obtaining the low gray area and the high gray area of the image, and obtaining a gray image of the high gray area, namely the image with enhanced gray.
Step 1-2, carrying out noise reduction on the image with enhanced gray scale by filtering by using a Gaussian blur method to obtain a noise-reduced image;
step 1-3, performing Gaussian filtering on the noise-reduced image to obtain a smooth image, specifically:
taking a Gaussian function value of a discrete point as a weight, carrying out window template collection on the noise-reduced picture, and carrying out weighted average on each pixel value in a gray matrix in the collected window template so as to eliminate Gaussian noise:
setting the size of the window template to be (2k +1) × (2k +1), then the weight coefficient of each element in the template is:
where (i, j) (i, j ═ 1, 2.., 2k +1) represents the relative coordinates of each pixel point within the window template.
Assuming that the window template is 3 × 3 and the value of σ is 1.5, gaussian filtering is performed on the image of the embodiment, the calculated weight matrix is as shown in fig. 3, the result obtained by multiplying each point by the corresponding weight value is the gaussian filtering output value of the central point, and the above process is repeated for all the pixel points in the image, so that the image after gaussian filtering is obtained
And 1-4, detecting a foreign object target on the obtained smooth image by using a Canny edge detection algorithm, updating the frame image into an initial frame when the foreign object target is detected to be positioned at the center of an image picture, and amplifying a video image of a region where the target is positioned.
step 2-1, performing target edge detection on the amplified target area by using a Canny edge detection algorithm, detecting edges of the target area in four directions including the horizontal direction, the vertical direction and two diagonal lines, calculating the gradient strength and the direction of the edges, performing noise elimination on the edges, converting image information into Gaussian cells by using a Gaussian blur method, and eliminating the influence of noise on identification, wherein the method specifically comprises the following steps:
step 2-1-1, dividing the gradient direction of the image into four directions according to angles, wherein the four directions are respectively edges of 0 degrees, 45 degrees, 90 degrees and 135 degrees, comparing the sizes of other two values of the central value in the corresponding gradient direction in each 3 x 3 neighborhood, if the central value is the maximum value, retaining the central value, otherwise, inhibiting the central value and removing false edge points;
step 2-1-2, setting two gradient size thresholds: high threshold is mthhLow threshold value mthlThe image is divided into two edge maps as follows:
EHif M (x, y) ≧ M is ≧ e (x, y) |thhIf e (x, y) is 1; otherwise e (x, y) ═ 0}
ELIf M (x, y) ≧ M is ≧ e (x, y) |thlIf e (x, y) is 1; otherwise e (x, y) ═ 0}
Wherein M (x, y) represents the edge gradient value at the (x, y) coordinate in the image before segmentation, and e (x, y) represents the edge gradient value at the current (x, y) coordinate after segmentation;
step 2-1-3, map E of high thresholdHMarking all the corresponding positions of the pixels as edge points, and traversing EHAt all points in (1), using 8-way relation at the low threshold map ELAnd determining the final edge point to enhance the edge continuity and finish the target edge detection.
And 2-2, converting the image information of the amplified target area into an image identification matrix, and processing the image identification matrix by using Hough transformation detection to obtain the position coordinates of the foreign object target and the winding point of the overhead transmission line in the image.
step 3-1, taking a foreign matter region in an initial frame after the video image is updated as a reference frame, predicting a foreign matter target region in a current frame by using a Mean-Shift operator, and solving the Shift average value of foreign matter target feature points in the current frame by using a Mean-Shift algorithm:
wherein the content of the first and second substances,
shifting the mean value for the feature points under the current reference point, Ck,dRepresenting a normalized constant, k (x) is a contour function, H is a kernel function bandwidth parameter, d is a matrix dimension, H is a bandwidth matrix of d x d dimensions, x is a reference pointiIs d dimension European space RdN is the number of sample points in the d-dimensional space;
3-2, updating the current image, taking the foreign matter characteristic point offset mean value as a new reference point, and repeatedly and circularly updating until the characteristic point offset distance threshold is met;
3-3, predicting a foreign matter region of the current frame by using a Mean-Shift operator by using a feature point offset distance threshold as a matching basis, extracting ORB feature points of the foreign matter region in the current frame and a reference frame, and constructing a foreign matter region feature set through feature point matching;
3-4, extracting and screening new feature points from the feature set of the foreign body region of the current frame, updating the feature set of the foreign body region, eliminating unmatched feature points, and improving the accuracy of foreign body detection by continuously updating the feature points in the feature set of the foreign body region;
and 3-5, converting the spatial coordinates of the foreign object target in the image into angle coordinates in real time, and tracking the foreign object target.
and 5, judging whether the foreign object still exists, if so, returning to the step 1, and if not, ending.
TABLE 1 ORB feature points match number comparison
The matching accuracy is calculated through the test data in the table 1, which shows that the accuracy of image identification processing on the obtained video sequence is high, foreign object target tracking is realized by using a Mean-Shift operator and an ORB characteristic point matching principle, and the matching rate of the foreign object target tracking is higher than that of the tracking in the prior art.
As shown in fig. 4, a schematic diagram of comparing ORB feature point identification with ordinary identification is shown, and compared with ordinary identification, ORB feature point identification greatly eliminates identification errors caused by background noise. The common identification identifies the target by detecting the position and the edge of the target, is easily affected by different backgrounds, and is easy to cause error identification. And (3) ORB feature point detection, namely firstly eliminating the noise influence of the background, improving the gray value of the identification target, framing the edge of the object through edge detection, carrying out ORB feature point matching in a high gray value target range, continuously extracting and screening new feature points in a foreign object target area feature set, updating the foreign object area feature set and improving the foreign object detection accuracy.
The foreign object target point position is determined in real time through ORB feature point matching, the accuracy of the total matching point number is not influenced by errors of a few matching points, the influence on real-time confirmation of the foreign object target position is very little, and when the foreign object target area position is judged in an iteration mode, the foreign object target recognition algorithm can be optimized by the number of the correct points, so that the target recognition effect close to 100% is achieved.
Claims (10)
1. A method for quickly calibrating a foreign object target of an overhead transmission line is characterized by comprising the following steps:
step 1, acquiring a video image of a target area in real time, processing the image, identifying current video image information frame by frame, setting a frame picture as an initial frame when a target object is identified, and amplifying the video image of the area where the target is located;
step 2, further accurately identifying the amplified video image, resolving image information, and acquiring coordinate information of the foreign object target and coordinate information of the foreign object target at the winding position of the overhead transmission line;
step 3, detecting picture image information of an initial frame, carrying out automatic tracking processing on the foreign object target, and identifying position state information of the foreign object target in the video image, wherein the position state information comprises image characteristic points, a mass center position and a moving track;
step 4, tracking the foreign bodies, and cutting by laser;
and 5, judging whether the foreign object still exists, if so, returning to the step 1, and if not, ending.
2. The method for rapidly calibrating the foreign object target of the overhead transmission line according to claim 1, wherein the step 1 of acquiring the video image and processing the image specifically comprises the following steps:
step 1-1, acquiring a video image, performing gray level conversion on each acquired frame of image, establishing a digital gray level image random mathematical model, and mapping each pixel on an original image to obtain an image with enhanced gray level;
step 1-2, carrying out noise reduction on the image with enhanced gray scale by filtering by using a Gaussian blur method to obtain a noise-reduced image;
step 1-3, performing Gaussian filtering on the noise-reduced image to obtain a smooth image;
and 1-4, detecting a foreign object target on the obtained smooth image by using a Canny edge detection algorithm, updating the frame image into an initial frame when the foreign object target is detected to be positioned at the center of an image picture, and amplifying a video image of a region where the target is positioned.
3. The method for rapidly calibrating the foreign object target of the overhead transmission line according to claim 2, wherein the step 1-1 is to perform gray level conversion on each frame of the acquired image to acquire an image with enhanced gray level, and specifically comprises the following steps:
step 1-1-1, constructing a digital gray image random mathematical model for the acquired image, and counting gray values of all pixels to obtain a gray distribution histogram, namely the gray level of the original image;
step 1-1-2, according to a segmentation threshold r in an original gray level image XcThe image is divided into two non-intersection subgraphs, and then a histogram equalization algorithm is applied to the two subgraphs respectively to carry out gray distribution normalization probability calculation:
wherein p isLd(dj) And PUd(dj) Respectively representing the gray normalization probabilities of the low-gray subgraph and the high-gray subgraph; wherein d iskL-1, L being the maximum value of the grey level, Pd(dj) For grey level d in the original imagejProbability of gray distribution of (P)d(dk) For grey level d in the original imagekThe gray level distribution probability of (1);
1-1-3, constructing a mapping relation from an original image gray level to a target image gray level, constructing a gray distribution probability density function of continuous variables, dividing a low gray area and a high gray area of an image according to the gray distribution probability density function, respectively obtaining the low gray area and the high gray area of the image, and obtaining a gray image of the high gray area, namely the image with enhanced gray.
4. The method for rapidly calibrating the foreign object target of the overhead transmission line according to claim 2, wherein the obtaining of the smooth image in the step 1-3 specifically comprises:
taking a Gaussian function value of a discrete point as a weight, carrying out window template collection on the noise-reduced picture, and carrying out weighted average on each pixel value in a gray matrix in the collected window template so as to eliminate Gaussian noise:
setting the size of the window template to be (2k +1) × (2k +1), then the weight coefficient of each element in the template is:
where (i, j) (i, j ═ 1, 2.., 2k +1) represents the relative coordinates of each pixel point within the window template.
5. The method for rapidly calibrating the foreign object target of the overhead transmission line according to claim 1, wherein the image information calculated in the step 2 specifically comprises:
step 2-1, performing target edge detection on the amplified target area by using a Canny edge detection algorithm, detecting edges in four directions including the horizontal direction, the vertical direction and two diagonal lines of the target area, calculating the gradient strength and the direction of the edges, performing noise elimination on the edges, converting image information into Gaussian cells by using a Gaussian blur method, and eliminating the influence of noise on identification;
and 2-2, converting the image information of the amplified target area into an image identification matrix, and processing the image identification matrix by using Hough transformation detection to obtain the position coordinates of the foreign object target and the winding point of the overhead transmission line in the image.
6. The method for rapidly calibrating the foreign object target of the overhead transmission line according to claim 5, wherein the target edge detection in the step 2-1 specifically comprises:
step 2-1-1, dividing the gradient direction of the image into four directions according to angles, wherein the four directions are respectively edges of 0 degrees, 45 degrees, 90 degrees and 135 degrees, comparing the sizes of other two values of the central value in the corresponding gradient direction in each 3 x 3 neighborhood, if the central value is the maximum value, retaining the central value, otherwise, inhibiting the central value and removing false edge points;
step 2-1-2, setting two gradient size thresholds: high threshold is mthhLow threshold value mthlThe image is divided into two edge maps as follows:
EHif M (x, y) ≧ M is ≧ e (x, y) |thhIf e (x, y) is 1; otherwise e (x, y) ═ 0}
ELIf M (x, y) ≧ M is ≧ e (x, y) |thlIf e (x, y) is 1; otherwise e (x, y) ═ 0}
Wherein M (x, y) represents the edge gradient value at the (x, y) coordinate in the image before segmentation, and e (x, y) represents the edge gradient value at the current (x, y) coordinate after segmentation;
step 2-1-3, map E of high thresholdHMarking all the corresponding positions of the pixels as edge points, and traversing EHAt all points in (1), using 8-way relation at the low threshold map ELAnd determining the final edge point to enhance the edge continuity and finish the target edge detection.
7. The method for rapidly calibrating the foreign object target of the overhead transmission line according to claim 1, wherein the identifying the position state information of the foreign object target in the video image in the step 3 specifically comprises:
step 3-1, taking a foreign matter region in an initial frame after the video image is updated as a reference frame, predicting a foreign matter target region in a current frame by using a Mean-Shift operator, and solving the Shift average value of foreign matter target feature points in the current frame by using a Mean-Shift algorithm:
wherein the content of the first and second substances,
shifting the mean value for the feature points under the current reference point, Ck,dRepresenting a normalized constant, k (x) being a profile function, H being a kernel bandwidth parameter, d being a matrix dimension, H being a bandwidth matrix in d x d dimensions, x being a reference point,xiis d dimension European space RdN is the number of sample points in the d-dimensional space;
3-2, updating the current image, taking the foreign matter characteristic point offset mean value as a new reference point, and repeatedly and circularly updating until the characteristic point offset distance threshold is met;
3-3, predicting a foreign matter region of the current frame by using a Mean-Shift operator by using a feature point offset distance threshold as a matching basis, extracting ORB feature points of the foreign matter region in the current frame and a reference frame, and constructing a foreign matter region feature set through feature point matching;
3-4, extracting and screening new feature points from the feature set of the foreign body region of the current frame, updating the feature set of the foreign body region, eliminating unmatched feature points, and improving the accuracy of foreign body detection by continuously updating the feature points in the feature set of the foreign body region;
and 3-5, converting the spatial coordinates of the foreign object target in the image into angle coordinates in real time, and tracking the foreign object target.
8. The utility model provides a quick calibration system of overhead transmission line foreign matter target which characterized in that includes following module:
an image processing module: the system comprises a frame, a target area video image acquisition unit, a target object acquisition unit, a display unit and a display unit, wherein the frame is used for acquiring a target area video image in real time, processing the image, setting a frame picture as an initial frame when the target object is identified, and amplifying the area video image of the target;
foreign object coordinate acquisition module: the system is used for further accurately identifying the amplified video image, resolving image information, and acquiring coordinate information of the foreign object target and coordinate information of the foreign object target at the winding position of the overhead transmission line;
a foreign matter tracking module: the system is used for tracking the foreign object target and identifying position state information of the foreign object target in a video image, wherein the position state information comprises image feature points, a mass center position and a moving track;
a foreign matter treatment module: and cutting the foreign matter by using laser, and judging whether the foreign matter target still exists.
9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-7 are implemented by the processor when executing the computer program.
10. A computer-storable medium having a computer program stored thereon, wherein the computer program is adapted to carry out the steps of the method according to any one of claims 1-7 when executed by a processor.
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| CN114862888A (en) * | 2022-04-24 | 2022-08-05 | 国网湖北省电力有限公司荆州供电公司 | Target identification method and remote laser cleaning method and system |
| CN115102089A (en) * | 2022-06-24 | 2022-09-23 | 国网江苏省电力有限公司南京供电分公司 | Method for calibrating aerial movement area of operating personnel without power outage |
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| CN114862888A (en) * | 2022-04-24 | 2022-08-05 | 国网湖北省电力有限公司荆州供电公司 | Target identification method and remote laser cleaning method and system |
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