CN104978580B - A kind of insulator recognition methods for unmanned plane inspection transmission line of electricity - Google Patents
A kind of insulator recognition methods for unmanned plane inspection transmission line of electricity Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 58
- 239000012212 insulator Substances 0.000 title claims abstract description 57
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- 230000005611 electricity Effects 0.000 title claims abstract description 23
- 238000007689 inspection Methods 0.000 title claims abstract description 20
- 238000012549 training Methods 0.000 claims abstract description 38
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 33
- 238000001514 detection method Methods 0.000 claims abstract description 14
- 238000012545 processing Methods 0.000 claims abstract description 13
- 238000009413 insulation Methods 0.000 claims abstract description 9
- 238000000605 extraction Methods 0.000 claims abstract description 7
- 238000013135 deep learning Methods 0.000 claims abstract description 5
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/19—Recognition using electronic means
- G06V30/192—Recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references
- G06V30/194—References adjustable by an adaptive method, e.g. learning
Abstract
The invention discloses one kind to be efficiently used for unmanned plane inspection electric transmission line isolator recognition methods, including image variants：Extraction for trained subgraph and carries out preliminary treatment from electric transmission line isolator image, forms training dataset；Packing processing, the corresponding label of addition image are carried out to the subgraph for being used for training extracted；Data are trained using convolutional neural networks (CNN) algorithm in deep learning, obtain the detection model for insulator；Detect insulator target area：Transmission line of electricity image is detected, obtains the candidate frame of insulation sub-goal；Non-maxima suppression is carried out to candidate frame, obtains final insulator candidate frame；Fitting a straight line operation is carried out to obtained final insulator candidate frame, obtains central point, the angle and size information of candidate frame, are finally labeled on electric transmission line isolator image.The image that the application obtains inspection screens, and mitigates the burden of artificial examination, has broad application prospects.
Description
Technical field
The present invention relates to Digital Image Processing and mode identification technology in transmission line equipment detection technique field, especially relates to
A kind of and efficient recognition methods for being used for unmanned plane inspection electric transmission line isolator.
Background technology
Insulator is part important in overhead transmission line, for support and fixed busbar with energized conductor, simultaneously
Make have enough distances and insulation between energized conductor or between conductor and the earth.Since overhead transmission line is chronically exposed to nature
In environment, influenced be subject to nature or human factor, there are aging circuit and destroy the problems such as, if not to these problems into
Row, which is inspected periodically and overhauled, may cause serious accident.
Artificial line walking detection efficiency is low, and dangerous high.With the development of unmanned air vehicle technique, pass through unmanned plane skill
Art gathers high-tension line image and these image informations is handled, it is possible to reduce personnel cost and the peace for ensureing construction personnel
Entirely, while work efficiency can be improved.
Since electric power line pole tower position geographical environment is complicated, cause the image background of acquisition also relative complex, this
Identification to succeeding target causes difficulty with positioning, and the transmission line status detection technique based on Aerial Images is still in
Starting stage, refers to document and achievement in research is less.
In the prior art, a kind of method is to utilize colouring information, using max-thresholds method, maximum variance between clusters to colour
Image is split.There are the shortcomings that：This kind of method is influenced than more serious by illumination, and natural environment residing for transmission line of electricity
Complexity, there is the complex background such as trees, river, road so that this kind of method Detection accuracy is not high.
Another kind of method is using the oval information of the laminated structure of insulator, and ellipse is detected with Hough transform.In the presence of
The shortcomings that：The problem of due to shooting angle, there is a situation where to block between piece and piece, cause detection error.
The content of the invention
To solve the shortcomings of the prior art, the invention discloses one kind to be efficiently used for unmanned plane inspection transmission line of electricity
Insulator recognition methods, the technology using deep learning method by learning to the transmission line of electricity image document taken photo by plane,
Then the angle information of insulator is calculated using line fitting method, can be completed to insulator in unmanned plane line walking image
Identification location technology requirement, improve the accuracy and robustness to isolator detecting.
To achieve the above object, concrete scheme of the invention is as follows：
A kind of insulator recognition methods for unmanned plane inspection transmission line of electricity, comprises the following steps：
Step 1：Image variants：Extraction is gone forward side by side for the subgraph of training from electric transmission line isolator image
Row preliminary treatment, forms training dataset；
Step 2：Packing processing, the corresponding label of addition image are carried out to the subgraph for being used for training extracted；
Step 3：Using convolutional neural networks (CNN) algorithm in deep learning to the data in step 1 data set into
Row training, obtains the detection model for insulator；
Step 4：Detect insulator target area：Transmission line of electricity image is detected, obtains the candidate of insulation sub-goal
Frame；
Step 5：Non-maxima suppression is carried out to candidate frame, obtains final insulator candidate frame；
Step 6：Fitting a straight line operation is carried out to obtained final insulator candidate frame, obtains central point, candidate frame
Angle and size information, are finally labeled on electric transmission line isolator image.
In the step 1, image variants process is：Insulation sub portion is extracted in electric transmission line isolator image
The area image of part, shaft tower and background, processing is zoomed in and out by the image extracted, and angle is carried out to the insulator extracted
Rotation process.
In the step 3, when carrying out model training with convolutional neural networks algorithm：
The template parameter that uses of initial training is set first, including the number of plies of convolutional neural networks, convolution kernel it is big
It is small, the initial weight of each node, if handled using down-sampling, per the number of layer data input and output, activation primitive, every layer
The learning efficiency that the gradient of convolutional neural networks declines.
In the step 3, specifically the isolator detecting model training process based on convolutional neural networks includes following step
Suddenly：
3-1) forward conduction (Feedforward Pass)：Divide the coloured image of input to RGB triple channels extraction pixel letter
Breath, the input information as convolutional neural networks；
3-2) conduction (BackPropagation Pass) backward：The class label that optimization forward conduction obtains and sample are real
Loss function between the label of border.
The step 3-1) in, the structure of convolutional neural networks, uses six layers of convolutional neural networks structured training template；
First layer is convolutional layer, and third layer is convolutional layer, and layer 5 is convolutional layer, and convolutional layer use sets the volume of pixel size
Product core carries out convolution operation with input information and obtains feature vector；
The second layer is down-sampled layer, and the 4th layer is down-sampled layer, and definition of the down-sampled layer in parameterized template, is dropped
Sampling operation, is maximized as output using in the image block of the pixel size of setting, has by down-sampled process
Effect reduces data processing amount on the basis of useful information is preserved；
Layer 6 is full articulamentum：The feature vector that layer 5 is obtained is integrated, and is formed a long vector, is passed it to
Activation primitive obtains the judgement to input sample classification, selects label of the output valve of maximum as input sample.
The step 3-2) in, it is assumed that for training set form it is { (x(1),y(1)),(x(2),y(2)),...,(x(n),y(n))},x(i)Represent i-th of training data, y(i)Represent data x(i)Corresponding data label, training dataset include n sample.
For single sample (x, y), the result learnt is hw,b(x), its loss function is：
So whole loss function J (W, b) is：
In formula (2), sl, sl+1Represent the number of l layers and l+1 layers neuron node, nlRepresent the number of plies of neutral net,Represent the weight coefficient of connection i-th of node of l layers and l+1 j-th of node of layer in l group weight parameters,For l layers
The biasing of i-th of node, Section 1 J (W, b；x(i),y(i)) it is a mean square deviation item, Section 2 is regularization term, for reducing
The amplitude of weight, prevents over-fitting, γ coefficients in order to control.
Parameter W and b are updated with gradient descent method：
Wherein, the α in formula (3) is learning rate, for the speed for controlling gradient to decline.
In the step 4, the detection of insulator is carried out using convolutional neural networks, method detailed is as follows：
4-1) extract trained convolutional neural networks template, including the network structure that weight, biasing and training use；
Then according to these parameter initialization test programs；
Image 4-2) is loaded into, since the image that unmanned plane collects is larger, image is zoomed in and out to accelerate follow-up fortune
Calculate, in order to accurately position the position of insulator, add multi-scale method, slider bar operation is carried out on multiple scales, obtain
Specific target image block；
4-3) using target image block as input, forward conduction operation is carried out, obtains the generic of object block；
4-4) preserve the other target block message of insulator class, start position and length and width information including object block.
In the step 6, insulator is marked using line fitting method, detailed process is as follows：
6-1) the m of the t classification obtained in obtaining step fivei, i={ 1 ..., t } a candidate frame information, calculates each wait
The center position of frame is selected, and is preserved；
6-2) center position is (P, Q), the center of each classification can be intended with the mode of linear fit
Close, accurately position the position of each insulator, can solve the problems, such as this with unitary linear fit here：
Y=kX+b'(4)
Least square fitting method 6-3) is used, calculates a straight line that can most reflect X and Y relations.
The step 6-3) in, defining loss function is：
Wherein, (pj,qj) be belong to i classes j-th of rectangle frame central point.
By seeking local derviation to b' and k, to obtain the optimal solution of b' and k：
The last solution for obtaining k and b' is
Wherein, (pj,qj) be belong to i classes j-th of rectangle frame central point, miRepresent the number of the rectangle frame of the i-th class.
Beneficial effects of the present invention：
The application uses convolutional layer, down-sampled layer and full linking layer：, will after three-layer coil product and two layers of down-sampled operation
All characteristic blocks carry out full attended operation, have thus obtained describing the final feature of image block.
Effective identification technology of electric transmission line isolator, which can be realized effectively, determines the insulation sub-goal in inspection image
Position, basis is provided for subsequent defective diagnosis.Meanwhile the image that this technology can also obtain inspection screens, mitigate artificial
The burden of examination, has broad application prospects.
Brief description of the drawings
Fig. 1 CNN structure charts；
Fig. 2 convolution and down-sampled process；
Fig. 3 unmanned plane transmission line of electricity images；
Transmission line of electricity image after Fig. 4 candidate frames mark；
The final isolator detecting result images of Fig. 5；
Fig. 6 trains flow chart；
Fig. 7 target detections and positioning flow figure.
Embodiment：
The present invention is described in detail below in conjunction with the accompanying drawings：
The training flow chart of experimentation is as shown in fig. 6, extraction data set, data set packing, start training process, instructing
Be trained hierarchical structure and initiation parameter before practicing, during training, train forward, reverse feedback, judge maximum iteration/
Required precision, if it is, output template, otherwise, continues to train.
For target identification with positioning flow figure as shown in fig. 7, input picture, initializes detection model, sliding window extracts subgraph,
Judge whether it is insulator, if it is not, sliding window extraction subgraph process is returned to, if so, storage candidate frame, is fitted candidate
Frame, in artwork mark position, output.
One kind is efficiently used for unmanned plane inspection electric transmission line isolator recognition methods, and specific steps include：
1) training set：Subgraph of the extraction for training from transmission line of electricity image, forms training dataset.
2) packing processing is carried out to the insulation subgraph extracted, adds the label of correspondence image；
3) utilize convolutional neural networks (CNN) algorithm in deep learning to be trained data, obtain being directed to insulator
Detection model；
4) insulator target area is detected：Transmission line of electricity image is detected, obtains the candidate frame of insulation sub-goal；
5) non-maxima suppression is carried out to candidate frame, obtains final insulator candidate frame.
6) fitting a straight line operation is carried out to obtained candidate frame, obtains central point, the angle and size information of frame, finally exist
It is labeled in artwork.
The image acquisition process of the step 1) is：The administrative division map of insulator part, shaft tower and background is extracted in artwork
Picture, in order to meet trained requirement, zooms in and out processing, size zooms to 64*64 pixels by the image extracted.In reality
In the situation of border, due to shooting angle reason obtains shaft tower image may be there are inclined situation, the insulation for this to extracting
Son carries out the rotation process of low-angle, to increase the diversity of data set, improves the robustness of training pattern.
In the step 3) step, the specific method that model training is carried out with CNN algorithms is as follows：
(1) template parameter for setting initial training to use is first had to, including the number of plies of convolutional neural networks, convolution
The size of core, the initial weight of each node, if handled using down-sampling, per the number of layer data input and output, activate letter
Number etc., also has the setting of the learning efficiency of the gradient decline of corresponding templates.
(2) network structure, here we use six layers of convolutional neural networks structured training template.CNN in form such as Fig. 1
Shown in structure chart.
Specifically the isolator detecting model training process based on CNN mainly has following two step：
A. forward conduction：RGB triple channels are divided to extract Pixel Information, the input as convolutional network the coloured image of input
Information, first layer are convolutional layer (Convolution), and convolution operation is carried out with the convolution kernel and input information of 5*5 pixel sizes.
It can strengthen original signal by convolution operation, and reduce the influence of noise.The second layer is down-sampled layer, according to parameterized template
In definition, carry out it is down-sampled operation (SubSampling/Pooling), taken most using in the image block of 2*2 here
Big value is as output.Data processing amount effectively can be reduced on the basis of useful information is preserved by down-sampled process；The
Three-layer coil lamination, the 4th layer of down-sampled layer, layer 5 convolutional layer carry out similar operation；Layer 6 is full articulamentum：By the 5th
The feature vector that layer obtains is integrated, and forms a long vector, is passed it to activation primitive and is obtained and input sample classification is sentenced
It is disconnected.As shown in Figure 2.
Convolutional layer, down-sampled layer and full linking layer are further described below below：
Input color image, carries out convolution algorithm, then plus one partially with a trainable wave filter fx and image
Bx is held, this has just extracted a characteristics of image, obtains the Cx of convolutional layer；In order to reduce data volume, pixel in four neighborhood parts
Change is maximized as output, then by the nonlinear transformation of the complete paired data of ReLu activation primitives, nonlinear transformation can be with
Reduce the linear relationship between feature and feature, the expressive faculty of Enhanced feature.Moreover, the processing of down-sampled layer in this way
Afterwards, the data volume obtained is only a quarter of convolutional layer.
After three-layer coil product and two layers of down-sampled operation, all characteristic blocks are subjected to full attended operation, thus
Arrive and the final feature of image block has been described.For the classification of prognostic chart picture, by obtained Feature Descriptor into line activating letter
Number calculation process, selects label of the output valve of maximum as image block.
B. to conduction after：The class label obtained by Feedforward Pass is with sample physical tags come counting loss
Function.Assuming that it is { (x that we, which are used for training set form,(1),y(1)),(x(2),y(2)),...,(x(n),y(n)), training dataset bag
Containing n sample, for single sample (x, y), the result learnt is hw,b(x), its loss function is：
So whole loss function is：
Formula (2) slRepresent the number of neuron in l layers,Represent to connect l layers in l group weight parameters i-th
The weight coefficient of node and l+1 j-th of node of layer.Section 1 J (W, b；x(i),y(i)) it is a mean square deviation item, Section 2 is
Regularization term, for reducing the amplitude of weight, prevents over-fitting, γ coefficients in order to control.Updated with each iteration of gradient descent method
Parameter W and b are updated：
α in formula (3) is learning rate, for the speed for controlling gradient to decline.
In following pseudocode, Δ W(l)It is one and matrix W(l)The identical matrix of dimension, Δ b(l)It is one and b(l)
The identical vector of dimension.In the following, provide an iteration process realized in gradient descent method：
1. couple all layers of l, make Δ W(l):=0, Δ b(l):=0；
2. couple i=1 to n：
A. calculated using back-propagation algorithm
B. calculate
C. calculate
3. update weight parameter：
In this way, what can be repeated reaches the value for reducing loss function J (W, b) using gradient descent method to iterate to calculate,
And then solve whole neutral net.
In the step 4), the detection of insulator is carried out using CNN, method detailed is as follows：
(1) trained CNN templates, including the network structure that weight, biasing and training use are extracted；Then according to this
A little parameter initialization test program frames；
(2) be loaded into image (as shown in Figure 3), the image collected due to unmanned plane is larger (5184*3456), to image into
Row is scaled to accelerate follow-up computing.In order to accurately position the position of insulator, multi-scale method is added, on multiple scales
Slider bar operation is carried out, obtains specific target image block.
(3) using target image block as input, CNN Feedforward Pass operations is carried out, obtain the affiliated of object block
Classification.(4) the other target block message of insulator class, start position and length and width information including object block are preserved, Fig. 4 is warp
Cross the image for marking insulator；
In the step 6), insulator is marked using approximating method, detailed process is as follows：
(1) m for the t classification that obtaining step (5) obtainsi, i={ 1 ..., t } a candidate frame information, calculates each frame
Central point position
Put, and preserve.
(2) center position is (P, Q), the center of each classification can be intended with the mode of linear fit
Close, accurately position the position of each insulator.Here this can be solved the problems, such as with unitary linear fit：
Y=kX+b'(4)
(3) least square fitting method is used, calculates a straight line that can most reflect X and Y relations：
Defining loss function is：
By seeking local derviation to b' and k, to obtain the optimal solution of b' and k：
The last solution for obtaining k and b' is
The angle information of insulator is thus determined, marks the specific position of insulator in artwork according to angle information
Put.If Fig. 5 is that final insulator marks image.
Although above-mentioned be described the embodiment of the present invention with reference to attached drawing, model not is protected to the present invention
The limitation enclosed, those skilled in the art should understand that, on the basis of technical scheme, those skilled in the art are not
Need to make the creative labor the various modifications that can be made or deformation still within protection scope of the present invention.
Claims (10)
1. a kind of insulator recognition methods for unmanned plane inspection transmission line of electricity, it is characterized in that, comprise the following steps：
Step 1：Image variants：Extraction for trained subgraph and carries out just from electric transmission line isolator image
Step processing, forms training dataset；
Step 2：Packing processing is carried out to the subgraph for being used for training extracted, adds the label of correspondence image；
Step 3：The data in step 1 data set are trained using the convolutional neural networks algorithm in deep learning, are obtained
To the detection model for insulator；
Step 4：Detect insulator target area：Transmission line of electricity image is detected, obtains the candidate frame of insulation sub-goal；
Step 5：Non-maxima suppression is carried out to candidate frame, obtains final insulator candidate frame；
Step 6：Fitting a straight line operation is carried out to obtained final insulator candidate frame, obtains central point, the angle of candidate frame
And size information, finally it is labeled on electric transmission line isolator image.
2. a kind of insulator recognition methods for unmanned plane inspection transmission line of electricity as claimed in claim 1, it is characterized in that, institute
State in step 1, image variants process is：Insulator part, shaft tower and the back of the body are extracted in electric transmission line isolator image
The area image of scape, processing is zoomed in and out by the image extracted, and the rotation process of angle is carried out to the insulator extracted.
3. a kind of insulator recognition methods for unmanned plane inspection transmission line of electricity as claimed in claim 1, it is characterized in that, institute
State in step 3, when carrying out model training with convolutional neural networks algorithm：
The template parameter for setting initial training to use first, including the number of plies of convolutional neural networks, the size of convolution kernel,
The initial weight of each node, if handled using down-sampling, per the number of layer data input and output, activation primitive, every layer of volume
The learning efficiency that the gradient of product neutral net declines.
4. a kind of insulator recognition methods for unmanned plane inspection transmission line of electricity as described in claim 1 or 3, its feature
It is that in the step 3, specifically the isolator detecting model training process based on convolutional neural networks comprises the following steps：
3-1) forward conduction：RGB triple channels are divided to extract Pixel Information the coloured image of input, as the defeated of convolutional neural networks
Enter information；
3-2) backward conduction：The class label obtained by forward conduction is with sample physical tags come counting loss function.
5. a kind of insulator recognition methods for unmanned plane inspection transmission line of electricity as claimed in claim 4, it is characterized in that, institute
State step 3-1) in, the structure of convolutional neural networks, using six layers of convolutional neural networks structured training template, first layer is volume
Lamination, third layer are convolutional layers, and layer 5 is convolutional layer, and convolutional layer is carried out with the convolution kernel of setting pixel size with input information
Convolution operation obtains feature vector；
The second layer is down-sampled layer, and the 4th layer is down-sampled layer, and definition of the down-sampled layer in parameterized template, carries out down-sampled
Operation, is maximized as output using in the image block of the pixel size of setting, effective by down-sampled process
Data processing amount is reduced on the basis of useful information is preserved；
Layer 6 is full articulamentum：The feature vector that layer 5 is obtained is integrated, and is formed a long vector, is passed it to activation
Function obtains the judgement to input sample classification, selects label of the output valve of maximum as image block.
6. a kind of insulator recognition methods for unmanned plane inspection transmission line of electricity as claimed in claim 4, it is characterized in that, institute
State step 3-2) in, it is assumed that for training set form it is { (x(1),y(1)),(x(2),y(2)),...,(x(n),y(n))},x(i)Represent
I-th of training data, y(i)Represent data x(i)Corresponding data label, training dataset include n sample；
For single sample (x, y), the result learnt is hw,b(x), its loss function is：
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</mrow>
</msub>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>W</mi>
<mrow>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mrow>
<mo>(</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>=</mo>
<mo>&lsqb;</mo>
<mfrac>
<mn>1</mn>
<mi>n</mi>
</mfrac>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<mrow>
<mo>(</mo>
<mfrac>
<mn>1</mn>
<mn>2</mn>
</mfrac>
<mo>|</mo>
<mo>|</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>w</mi>
<mo>,</mo>
<mi>b</mi>
</mrow>
</msub>
<mo>(</mo>
<msup>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>)</mo>
<mo>-</mo>
<msup>
<mi>y</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>|</mo>
<msup>
<mo>|</mo>
<mn>2</mn>
</msup>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>+</mo>
<mo>+</mo>
<mfrac>
<mi>&gamma;</mi>
<mn>2</mn>
</mfrac>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>l</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<msub>
<mi>n</mi>
<mi>l</mi>
</msub>
<mo>-</mo>
<mn>1</mn>
</mrow>
</munderover>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>s</mi>
<mi>l</mi>
</msub>
</munderover>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>s</mi>
<mrow>
<mi>l</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>W</mi>
<mrow>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mrow>
<mo>(</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
Formula (2) slRepresent the number of neuron in l layers,Represent in l group weight parameters connect i-th of node of l layers with
The weight coefficient of l+1 j-th of node of layer,For the biasing of l i-th of node of layer, Section 1 J (W, b；x(i),y(i)) it is one
A mean square deviation item, Section 2 are regularization terms, for reducing the amplitude of weight, prevent over-fitting, γ coefficients in order to control.
7. a kind of insulator recognition methods for unmanned plane inspection transmission line of electricity as claimed in claim 6, it is characterized in that, use
The each iteration renewal of gradient descent method is updated parameter W and b：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>W</mi>
<mrow>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mrow>
<mo>(</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>W</mi>
<mrow>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mrow>
<mo>(</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>-</mo>
<mi>&alpha;</mi>
<mfrac>
<mo>&part;</mo>
<mrow>
<mo>&part;</mo>
<msubsup>
<mi>W</mi>
<mrow>
<mi>j</mi>
<mi>i</mi>
</mrow>
<mrow>
<mo>(</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
</msubsup>
</mrow>
</mfrac>
<mi>J</mi>
<mrow>
<mo>(</mo>
<mi>W</mi>
<mo>,</mo>
<mi>b</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>b</mi>
<mi>i</mi>
<mrow>
<mo>(</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>b</mi>
<mi>i</mi>
<mrow>
<mo>(</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>-</mo>
<mi>&alpha;</mi>
<mfrac>
<mo>&part;</mo>
<mrow>
<mo>&part;</mo>
<msubsup>
<mi>b</mi>
<mi>i</mi>
<mrow>
<mo>(</mo>
<mi>l</mi>
<mo>)</mo>
</mrow>
</msubsup>
</mrow>
</mfrac>
<mi>J</mi>
<mrow>
<mo>(</mo>
<mi>W</mi>
<mo>,</mo>
<mi>b</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, the α in formula (3) is learning rate, for the speed for controlling gradient to decline.
8. a kind of insulator recognition methods for unmanned plane inspection transmission line of electricity as claimed in claim 1, it is characterized in that, institute
State in step 4, the detection of insulator is carried out using convolutional neural networks, method detailed is as follows：
4-1) extract trained convolutional neural networks template, including the network structure that weight, biasing and training use；Then
According to these parameter initialization test programs；
Image 4-2) is loaded into, since the graphical rule that unmanned plane collects is larger, image is zoomed in and out to accelerate follow-up fortune
Calculate, in order to accurately position the position of insulator, add multi-scale method, slider bar operation is carried out on multiple scales, obtain
Specific target image block；
4-3) using target image block as input, forward conduction operation is carried out, obtains the generic of object block；
4-4) preserve the other target block message of insulator class, start position and length and width information including object block.
9. a kind of insulator recognition methods for unmanned plane inspection transmission line of electricity as claimed in claim 1, it is characterized in that, institute
State in step 6, insulator is marked using line fitting method, detailed process is as follows：
6-1) the m of the t classification obtained in obtaining step fivei, i={ 1 ..., t } a candidate frame information, calculates each candidate frame
Center position, and preserve, miRepresent the number of the rectangle frame of the i-th class；
6-2) center position is (P, Q), the center of each classification is fitted with the mode of linear fit, accurately
The position of each insulator is positioned, this is solved the problems, such as with unitary linear fit：
Y=kX+b'(4)
Least square fitting method 6-3) is used, calculates a straight line that can most reflect X and Y relations.
10. a kind of insulator recognition methods for unmanned plane inspection transmission line of electricity as claimed in claim 9, it is characterized in that,
The step 6-3) in, defining loss function is：
<mrow>
<mi>L</mi>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>q</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<mo>(</mo>
<mrow>
<msup>
<mi>b</mi>
<mo>&prime;</mo>
</msup>
<mo>+</mo>
<msub>
<mi>kp</mi>
<mi>j</mi>
</msub>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
By seeking local derviation to b' and k, to obtain the optimal solution of b' and k：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mrow>
<mo>&part;</mo>
<mi>L</mi>
</mrow>
<mrow>
<mo>&part;</mo>
<msup>
<mi>b</mi>
<mo>&prime;</mo>
</msup>
</mrow>
</mfrac>
<mo>=</mo>
<mn>2</mn>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>q</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<mo>(</mo>
<mrow>
<msup>
<mi>b</mi>
<mo>&prime;</mo>
</msup>
<mo>+</mo>
<msub>
<mi>kp</mi>
<mi>j</mi>
</msub>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mfrac>
<mrow>
<mo>&part;</mo>
<mi>L</mi>
</mrow>
<mrow>
<mo>&part;</mo>
<mi>k</mi>
</mrow>
</mfrac>
<mo>=</mo>
<mo>-</mo>
<mn>2</mn>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>q</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<mo>(</mo>
<mrow>
<msup>
<mi>b</mi>
<mo>&prime;</mo>
</msup>
<mo>+</mo>
<msub>
<mi>kp</mi>
<mi>j</mi>
</msub>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<msub>
<mi>p</mi>
<mi>j</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
The last solution for obtaining k and b' is
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
<mo>*</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
</munderover>
<msub>
<mi>p</mi>
<mi>j</mi>
</msub>
<msub>
<mi>q</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
</munderover>
<msub>
<mi>p</mi>
<mi>j</mi>
</msub>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
</munderover>
<msub>
<mi>q</mi>
<mi>j</mi>
</msub>
</mrow>
<mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
<mo>*</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
</munderover>
<msubsup>
<mi>p</mi>
<mi>j</mi>
<mn>2</mn>
</msubsup>
<mo>-</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
</munderover>
<msubsup>
<mi>q</mi>
<mi>j</mi>
<mn>2</mn>
</msubsup>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msup>
<mi>b</mi>
<mo>&prime;</mo>
</msup>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
<mo>*</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
</munderover>
<msubsup>
<mi>p</mi>
<mi>j</mi>
<mn>2</mn>
</msubsup>
<msub>
<mi>q</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
</munderover>
<msub>
<mi>p</mi>
<mi>j</mi>
</msub>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
</munderover>
<msub>
<mi>q</mi>
<mi>j</mi>
</msub>
</mrow>
<mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
<mo>*</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
</munderover>
<msubsup>
<mi>p</mi>
<mi>j</mi>
<mn>2</mn>
</msubsup>
<mo>-</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
</munderover>
<msubsup>
<mi>q</mi>
<mi>j</mi>
<mn>2</mn>
</msubsup>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
Wherein, (pj,qj) be belong to i classes j-th of rectangle frame central point, miRepresent the number of the rectangle frame of the i-th class.
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Address after: Wang Yue Central Road Ji'nan City, Shandong province 250002 City No. 2000 Co-patentee after: National Network Intelligent Technology Co., Ltd. Patentee after: Electric Power Research Institute of State Grid Shandong Electric Power Company Co-patentee after: State Grid Corporation of China Address before: Wang Yue Central Road Ji'nan City, Shandong province 250002 City No. 2000 Co-patentee before: Shandong Luneng Intelligent Technology Co., Ltd. Patentee before: Electric Power Research Institute of State Grid Shandong Electric Power Company Co-patentee before: State Grid Corporation of China |
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Effective date of registration: 20201030 Address after: 250101 Electric Power Intelligent Robot Production Project 101 in Jinan City, Shandong Province, South of Feiyue Avenue and East of No. 26 Road (ICT Industrial Park) Patentee after: National Network Intelligent Technology Co.,Ltd. Address before: Wang Yue Central Road Ji'nan City, Shandong province 250002 City No. 2000 Patentee before: ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER Co. Patentee before: National Network Intelligent Technology Co.,Ltd. Patentee before: STATE GRID CORPORATION OF CHINA |