CN107784634A - A kind of power transmission line shaft tower Bird's Nest recognition methods based on template matches - Google Patents
A kind of power transmission line shaft tower Bird's Nest recognition methods based on template matches Download PDFInfo
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- 238000005516 engineering process Methods 0.000 abstract description 6
- 230000005611 electricity Effects 0.000 abstract description 4
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- 230000007547 defect Effects 0.000 abstract description 3
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Abstract
The defects of research for being mostly based on unmanned plane polling transmission line technology is all the suspension of stranded and foreign matter, the insulator missing on wire is detected.And the research for the horizontal Bird's Nest identification technology of power transmission line shaft tower is few.And picture background is complex, it is difficult to which more satisfied Detection results are made us in acquisition.Shaft tower is the important component in transmission line of electricity, once failure hidden danger, will directly threaten high-voltage fence safe.The present invention relates to a kind of power transmission line shaft tower Bird's Nest recognition methods based on template matches, the figure that will take photo by plane first is converted to HSI spaces from GRB spaces, in order to reduce operand, improves recognition speed, by carrying out dimension-reduction treatment to picture, then pre-processed respectively on H and channel S.After pretreatment, ready template is loaded into, image to be identified is carried out into the information area with template image using template matching method is superimposed, and carries out matched pixel statistics to the image after superposition, so as to obtain matching factor, it is best match to take matching factor maximum.
Description
Technical field
The present invention relates to template matches, and in particular to a kind of power transmission line shaft tower Bird's Nest recognition methods based on template matches.
Background technology
Economic development not only makes urban and rural power grids load rapid growth, also power supply reliability and power supply quality is proposed higher
Requirement.The power circuit corridor in China, it is often necessary to pass through various complicated geographical environments, frequently by lake and reservoir with
And high and steep mountains etc., this coverage of transmission line of electricity is big, distributed areas are wide, transmission range is long, geographical conditions are complicated and changeable and
The features such as notable is influenceed by amblent air temperature, great challenge is brought to the day-to-day operation of circuit, maintenance and maintenance.
The tour of transmission line of electricity typically uses manual patrol mode, though this method is simple, it is less efficient, the cycle compared with
It is long, and need to be equipped with a large amount of optical devices and quality is high, veteran track walker, the requirement to manpower, financial resources is higher.
With the development and application of polling transmission line technology of the China based on unmanned plane, for how under the natural background of complexity,
Using image processing techniques, line facility (such as wire, insulator) is automatically and accurately extracted from aviation image, it is accurate to know
Its defect is not detected, turns into a key technical problem.
At present, the research for being mostly based on unmanned plane polling transmission line technology is hanged on the stranded and foreign matter of wire
The defects of extension, insulator missing, is detected.And the research for the horizontal Bird's Nest identification technology of power transmission line shaft tower is few.Meanwhile
Because picture background is complex, it is difficult to which more satisfied Detection results are made us in acquisition.Shaft tower is the important portion in transmission line of electricity
Part, once failure hidden danger, will directly threaten high-voltage fence safe, or even cause loss difficult to the appraisal.
Herein for this deficiency, the identification of the shaft tower Bird's Nest in primary study Aerial Images.
The content of the invention
The invention discloses a kind of power transmission line shaft tower Bird's Nest recognition methods based on Aerial Images, main contents include taking photo by plane
Image preprocessing, Bird's Nest, display matching result are detected using template matching method.
The present invention overall design philosophy be：Because picture background of taking photo by plane is more complicated, and shooting angle is not fixed, herein
The figure that will take photo by plane first is converted to HSI spaces from GRB spaces.In order to reduce operand, recognition speed is improved, by entering to picture
Row dimension-reduction treatment, then pre-processed respectively on H and channel S.After pretreatment, ready template is loaded into, utilizes mould
Image to be identified is carried out the information area with template image and is superimposed by plate matching method, and matched pixel system is carried out to the image after superposition
Meter, so as to obtain matching factor, it is best match to take matching factor maximum.Fig. 1 is this method overview flow chart.
Brief description of the drawings
Fig. 1 main program flow charts；
Fig. 2-1 plus salt-pepper noise figure after adaptive-filtering with contrasting sectional drawing；
Fig. 2-2 HIS single channel images；
Fig. 2-3 HIS single channel image Threshold segmentations；
Common factor effect is taken after Fig. 2-4 H and channel S Threshold segmentation；
Fig. 2-5 Sobel operators；
Fig. 2-6 Sobel rim detection design sketch；
Fig. 3-1 template matches processes；
Fig. 3-2 Bird's Nest templates；
Fig. 3-3 Bird's Nest recognition results.
Embodiment
Image preprocessing
Due in the absence of perfect state, unavoidably always introducing various noises in image process is obtained, not only hindering
Sense organ, can more hinder the understanding and analysis of subsequent figure source information, and error is caused to result, therefore, to image procossing it
Can gained target image progress denoising as previous.Filtering is the concept in signal transacting, it is therefore an objective to by certain wave in signal
The frequency of section filters out, and is processing method very classical in denoising.
Adaptive median filter
Medium filtering effect depends on the size of filter window, makes very much edge blurry greatly, and too small then denoising effect is bad.
Because noise spot and marginal point are equally the more violent pixels of grey scale change, when common medium filtering changes noise spot gray scale,
Also the gray value of edge pixel will be changed to a certain degree.And noise spot pixel value is nearly all the extreme value in neighborhood, but edge leads to
Chang Buhui is then to limit medium filtering using this feature.
Specifically improved method can be：Progressive scanning picture, when handling each pixel, judge whether the pixel is filter
The maximum or minimum of the lower neighborhood territory pixel of ripple window covering.If it is, using the normal median filter process picture
Element；If it is not, then disregard.This method can effectively remove burst noise point, especially salt-pepper noise,
And have little influence on edge.Comparison diagram is as shown in Fig. 2-1.
In the present invention, because its adjacent spots has very strong correlation, edge feature ensures not to be blurred again, so in
Value filtering method is most suitable.
Spatial alternation and processing
HSI color spaces describe color with tone (Hue), color saturation (Saturation) and brightness (Intensity)
It is color.The classification and depth degree of color are represented with tone and saturation degree, the relative shading value of color is indicated with brightness.HSI colors
Space separates the colourity of image and brightness, is provided a great convenience for Color Image Processing, for particular color, only needs
H and S components are directed to, are analyzed and processed in plane, and ignore the I component on right side.Aerial Images are transformed into HSI spaces,
And separate triple channel, H and S component single channel images are obtained as shown in Fig. 2-2.
Because image resolution ratio is higher, the time of consuming is calculated with regard to long.Therefore, after channel separation, to image
Size is reset, and is reduced to original 0.5 times, can so reduce amount of calculation, accelerates code operational efficiency.Then respectively to H
Enter row threshold division with channel S, the foreground image of acquisition is as Figure 2-3.
From Fig. 2-3, H passage noises are relatively fewer, also essentially eliminate light intensity in single image and prospect is carried
The influence taken, and substantial amounts of noise color lump in channel S be present.Pass through a large amount of tests to different colours shaft tower in different background
Understand, H component single channel Threshold segmentations, shaft tower in most cases can more fully be extracted；Though S components can carry shaft tower
Take, flase drop but easily occur.It can be seen that a certain component of HSI color spaces is used alone, it is difficult to obtain accurate prospect
Image.Two images shown in Fig. 2-3 are sought common ground, can so utilize H and S component informations simultaneously, while ignore I component,
The influence of illumination is avoided, as in Figure 2-4.
More than analysis understand, image is transformed into HSI spaces from rgb color space first, then H and channel S are entered respectively
Row threshold division, finally segmentation result is sought common ground, this method can be reduced meadow, the first-class background removal of concrete floor dry
Disturb the factor of identification.
Sobel rim detections
Sobel operators are a kind of edge detection methods based on gradient.The expression formula of Sobel operators is：
G (i, j)=| f (i-l, j+1)+2f (i, j+l)+f (i+1, j+1)-f (i-1, j-l) -2f (i, j-l)-f (i+1,
j-l)|
+|f(i-l,j-1)+2f(i-l,j)+f(i-l,j+l)-f(i+1,j-l)-2f(i+1,j)-f(i+l,j+
1)|
Two convolutional calculation templates of Sobel edge detection operators as shown in Figure 2-5, each point in image with this two
Individual template makees convolution, and first template responds maximum to common vertical edge, and second template responds most to horizontal edge
Greatly.Output valve of the maximum of two convolution as the point, operation result are a breadths edge magnitude images.Sobel operators are to ash
Degree gradual change and the more image procossing of noise obtain preferably.
Sobel operators are smoothed to image first, have certain noise inhibiting ability, then remake differential fortune
Calculate, good edge effect can be produced, but also will detect that some pseudo-edges so that edge is thicker.It is real-time in view of image
Processing requirement calculating speed is fast, and the feature of shaft tower Bird's Nest is obvious, and required precision is not high, therefore using based on first derivative
Sobel operators carry out rim detection.Testing result is as shown in figures 2-6.
Bird's Nest identification based on template matches
Matching is to be combined the expression inputted with oneself has in advance, or the mistake of corresponding pattern is found according to known mode
Journey, that is, establish it is unknown with it is known between contact to identify the process of unknown object.The collection of known mode is collectively referred to as template
Storehouse, the set of unknown pattern turn into test sample storehouse.As shown in figure 3-1, the class belonging to pattern to be sorted is found in ATL
Other process is to match.
Template imports
The present invention is identified for invoice number, and the digital template of the invoice number font of standard is imported into database
In, image input language, i.e. sentence are carried using Matlab, i.e.,：
Temp=imread (' C:\Users\Administrator\Desktop\muban.bmp')；
Bird's Nest template is as shown in figure 3-2.
Bird's Nest identification process
First by font masters input database to be identified, then picture pic to be identified of taking photo by plane is inputted in program,
By spatial alternation, Threshold segmentation, rim detection, image background is removed, and is multiplied with template, counts each template
Coefficient correlation after matching, coefficient correlation is bigger to represent that matching degree is higher.The maximum point of coefficient correlation is chosen to tie as identification
Fruit, finally recognition result is exported.Recognition result is as shown in Fig. 3-3.
The specific embodiment of the present invention is described above.It is to be appreciated that the invention is not limited in above-mentioned
Particular implementation, those skilled in the art can make various deformations or amendments within the scope of the claims, this not shadow
Ring the substantive content of the present invention.
Claims (5)
- A kind of 1. power transmission line shaft tower Bird's Nest recognition methods based on template matches, it is characterised in that：The figure that will take photo by plane first is empty from GRB Between be converted to HSI spaces；Secondly, by carrying out dimension-reduction treatment to picture, then pre-processed respectively on H and channel S；By After pretreatment, ready template is loaded into, image to be identified is carried out into the information area with template image using template matching method folds Add, matched pixel statistics is carried out to the image after superposition, so as to obtain matching factor, it is optimal to take matching factor maximum Match somebody with somebody.
- 2. power transmission line shaft tower Bird's Nest recognition methods according to claim 1, it is characterised in that：Described being filtered into is adaptive Medium filtering, progressive scanning picture, when handling each pixel, judge whether the pixel is the lower neighborhood picture of filter window covering The maximum or minimum of element；If it is, using the normal median filter process pixel；If it is not, then not locate Reason.
- 3. power transmission line shaft tower Bird's Nest recognition methods according to claim 1, it is characterised in that：Described spatial alternation is： HSI color spaces separate the colourity of image and brightness, are provided a great convenience for Color Image Processing, for specific face Color, it is only necessary to for H and S components, analyzed and processed in plane, and ignore the I component on right side.
- 4. power transmission line shaft tower Bird's Nest recognition methods according to claim 1, it is characterised in that：Sobel rim detections are specific For：Sobel operators are smoothed to image first, are had certain noise inhibiting ability, are then remake and differentiate, can To produce good edge effect.
- 5. power transmission line shaft tower Bird's Nest recognition methods according to claim 1, it is characterised in that：Described template matches are specific For：By in font masters input database to be identified, then picture to be identified of taking photo by plane is inputted in program, by a series of pre- places Reason operation removes image background, and is multiplied with template, counts the coefficient correlation after each template matches, coefficient correlation is got over It is big to represent that matching degree is higher.The maximum point of coefficient correlation is chosen as recognition result.
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Application publication date: 20180309 |