CN109598349A - Overhead transmission line fault detection data sample batch processing training method based on classification stochastical sampling - Google Patents
Overhead transmission line fault detection data sample batch processing training method based on classification stochastical sampling Download PDFInfo
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Abstract
The overhead transmission line fault detection data sample batch processing training method based on classification stochastical sampling that the invention discloses a kind of, steps are as follows: S1, unmanned plane acquire data, determine the corresponding classification of each data sample, category stores data, then counts the quantity of the included sample of every one kind；S2, deep learning algorithm model batch processing quantity N is determined according to the quantity that every a kind of sample includesbatchNumerical value；S3, it is to guarantee that each classification sample size is identical in batching data, randomly selects N from every one kindbatch/ n data sample；S4, the data being drawn into are combined into one group of batching data, are trained after upsetting sequence for model batch processing；S5, to the training of this batch of data after, repeat the above steps S3 and S4 carry out the training of batch processing next time.The present invention ensure that identical for classification sample size each in trained every batch of data in data plane, effectively improve the generalization ability of training pattern.
Description
Technical field
The present invention relates to machine learning techniques fields, and in particular to a kind of overhead transmission line based on classification stochastical sampling
Fault detection data sample batch processing training method.
Background technique
Overhead transmission line is largely directly characterized as electric system important component, operation conditions
The operation conditions of entire power grid.For the reliability for ensuring operation of power networks, patrol officer need to periodically examine overhead transmission line
It looks into.Currently, there are mainly two types of detection modes: traditional detection mode and intelligent measurement mode.Traditional detection mode relies primarily on people
Work carries out, firstly, traditional detection mode generally requires to expend a large amount of man power and material；Secondly, high voltage transmission line scale it is big,
It has a very wide distribution, the environment of surrounding has many potential danger, is unfavorable for artificial detection, so that traditional method for inspecting is applicable in model
It encloses and greatly reduces.For this problem, unmanned plane inspection technology is widely used in succession, they carry photograph or camera shooting
Equipment is flown along power transmission line corridor, and shooting at close range route obtains Aerial Images.Its shortcoming needs artificial searching picture
The defects of position, and mark out come.What traditional image labeling was manually performed, the understanding and mark of image are aligned
Really, but under big data environment, artificial mark workload is huge, and for the missing of some widgets, detection leakage phenomenon is very
Common phenomenon.
In recent years, with the rise of deep learning, scholar proposes the defect automatic marking technology based on deep learning.
Automatic marking technology because its excellent performance, meet modern intelligent and automation requirement the advantages that due to be gradually taken seriously.
The important branch that deep learning learns as robot is the important channel for making computer have intelligence, main research meter
The learning behavior of the mankind is simulated, realized to calculation machine how, identifies existing knowledge, obtains new knowledge, constantly improves performance and improve certainly
Body.Therefore, deep learning is combined with modern inspection technology, have in terms of electric power defects detection very high convenience with it is excellent
More property.
In recent years, with the further investigation of deep learning, the target detection based on deep learning achieves astonishing achievements.
However, electric power defects detection and target detection are there are while similitude, the two also has otherness to a certain degree.Wherein the most
Significantly surely belong to positive and negative data sample imbalance, imbalance here in electric power defects detection and refers to that the quantity of defect classification is remote
Less than the quantity of normal category sample.Traditional algorithm of target detection often assumes the sample number that each classification of training sample includes
Amount be it is same or similar, i.e., number of samples of all categories be it is balanced, but the defects of field of power system detection be not to be inconsistent
This hypothesis is closed, the electric system most of the time is in safe and stable operating status, the sample of defect or fault category
It only occupies the minority, is largely normal.In general, this unbalanced training sample will lead to training pattern on test set
Stress the more normal category of number of samples, and the defect classification that " despising " number of samples is fewer, however, in reality
The correct meaning for distinguishing minority class defect sample is much higher than whole classification accuracy, this segmental defect sample there is a serious shortage of to not
Balanced sort increases difficulty.
Therefore, in order to further enhance model generalization ability, solve network training when unmanned plane inspection take photo by plane in figure not
Balance sample handles problem, makes and being correspondingly improved in original deep learning algorithm model batch processing training, is just able to satisfy existing
For intelligent patrol detection requirement.It is necessary in view of the above-mentioned problems, proposing a kind of overhead transmission line defect based on classification stochastical sampling
Detection data sample batch processing training method.
Summary of the invention
It is an object of the invention to solve the problems, such as in the electric power defects detection based on deep learning class imbalance this, mention
A kind of overhead transmission line fault detection data sample batch processing training method based on classification stochastical sampling out, is effectively relieved electricity
The weight number of samples on the upside of test set of training pattern caused by defect categorical data is on the low side in power defects detection is more normal
Classification.
The purpose of the present invention can be reached by adopting the following technical scheme that:
A kind of overhead transmission line fault detection data sample batch processing training method based on classification stochastical sampling, including
Following steps:
S1, determine that collected overhead transmission line fault detection data sample includes several classifications, category is marked
Note, then counts the quantity of the included sample of every one kind, is denoted as N respectively1、N2…Nn, n is the quantity of classification type；
S2, suitable algorithm model batchparameters N is chosen according to the quantity for counting every a kind of sample in step S1batch
Numerical value；
S3, it is to guarantee that each classification sample size is identical in batching data, randomly selects N from every one kindbatch/ n
Data sample；
S4, the data sample randomly selected in step S3 is combined, is trained after upsetting sequence as batch processing
Data sample；
S5, to the training of this batch of data after, repeat the above steps S3 and step S4 carry out the training of batch processing next time.
Detailed process is as follows by step S1 as a preferred technical solution:
The data that unmanned plane collection in worksite arrives are arranged, satisfactory picture sample is picked out, subsequent category stores number
According to sample, the quantity of every included sample of one kind is counted, is denoted as N respectively1、N2…Nn, finally corresponding mark is added for every picture
Label；
Wherein, the classification type of the overhead transmission line fault detection data sample includes normally, missing, loosens,
The quantity n of classification type is equal to 3 at this time.
Detailed process is as follows by step S2 as a preferred technical solution:
Suitable algorithm model batchparameters (N is chosen according to the quantity for counting every a kind of sample in step S1batch)
Numerical value, it should be noted that the N of selectionbatchMake Nbatch/ n is integer, to facilitate subsequent processing；
Detailed process is as follows by step S3 as a preferred technical solution:
N is randomly selected from every a kind of data samplebatch/ n data sample, it is ensured that be used for each batch processing of deep learning
Data in each classification sample size for including it is identical；
Detailed process is as follows by step S4 as a preferred technical solution:
The step S3 data sample randomly selected is combined, the number trained after sequence as batch processing is upset
According to sample；
Detailed process is as follows by step S5 as a preferred technical solution:
The data after sequence will be upset in S4 and be used for batch processing training deep learning defects detection model, until epicycle training
Terminate, repeats the above steps, carry out the training of next round.
The present invention has the following advantages and effects with respect to the prior art:
1, the method for the present invention ensure that in data plane for classification sample size phase each in trained every batch of data
Together, the generalization ability of training pattern is effectively increased；
2, the method for the present invention uses stochastical sampling, it is ensured that the chance that each data is drawn into is identical, and method is easy to real
It is existing, it can canbe used on line in memory.
Detailed description of the invention
Fig. 1 is typical sample exemplary diagram, wherein Fig. 1 (a) is that the closed pin of sample is normal, schematic diagram of missing；Fig. 1 (b)
It is the schematic diagram that the closed pin of sample loosens；
Fig. 2 is exemplary sample block diagram；
Fig. 3 is example training flow chart.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
Embodiment
As shown in Figure 1, power connection fitting closed pin have normally with defect two states, defect can be subdivided into loosening with
Fall off two states, and exemplary sample block diagram shown in Fig. 2 is summarized simply as follows stochastical sampling, recombination, trains these three ranks
Section, algorithm flow chart shown in Fig. 3 illustrate the model training flow chart of closed pin defects detection, comprising the following steps:
S1, normal, missing will be divided by the unmanned plane figure of screening, loosen 3 classes, category store picture not it
Label is added, then counting each classification includes that sample size is as follows: normal class N1=780, lack class N2=530, loosen class
N3=270；
S2, according to the data volume of all categories in step S1 for including, enable Nbatch=30, algorithm other parameters default is constant；
S3, input data sample and respective labels, from randomly selecting 10 data sample integration in each classification
Come, upsets the batch processing training after sequence for model；
S4, after training to the batch data, judge whether to reach specified training batch, if not having, repeat step S3
Until reaching specified training batch；
After S5, specified trained batch to be achieved, epicycle training terminates, and repeats step S3, S4 until reaching specified exercise wheel
Number.
The above embodiment is a preferred embodiment of the present invention, but embodiments of the present invention are not by above-described embodiment
Limitation, other any changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principles of the present invention,
It should be equivalent substitute mode, be included within the scope of the present invention.
Claims (4)
1. a kind of overhead transmission line fault detection data sample batch processing training method based on classification stochastical sampling, feature
Be, the training method the following steps are included:
S1, the classification type that collected overhead transmission line fault detection data sample includes is determined, category is marked,
The quantity for then counting each included sample of classification type respectively, is denoted as N1、N2…Nn, n is the quantity of classification type；
S2, batchparameters N is chosen according to the quantity of each included sample of classification typebatchNumerical value；
S3, N is randomly selected from the sample of each classification typebatch/ n data sample；
S4, the data sample randomly selected in step S3 is combined, is upset after sequence as batch processing training
Data sample；
S5, to the training of this batch of data after, repeat the above steps S3 and step S4 carry out the training of batch processing next time.
2. the overhead transmission line fault detection data sample batch processing according to claim 1 based on classification stochastical sampling
Training method, which is characterized in that the classification type of the overhead transmission line fault detection data sample includes normal, scarce
It loses, loosen.
3. the overhead transmission line fault detection data sample batch processing according to claim 1 based on classification stochastical sampling
Training method, which is characterized in that the batchparameters NbatchNumerical value selection to make Nbatch/ n is integer.
4. the overhead transmission line fault detection data sample batch processing according to claim 1 based on classification stochastical sampling
Training method, which is characterized in that completed in the step S1 by adding corresponding label to picture every in data sample
Classification type is marked.
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Cited By (3)
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| CN110324207A (en) * | 2019-07-10 | 2019-10-11 | 深圳市智物联网络有限公司 | A kind of detection method and device of data collection station |
| CN112529033A (en) * | 2020-09-22 | 2021-03-19 | 陕西土豆数据科技有限公司 | Method for solving data imbalance of remote sensing image multi-classification scene segmentation algorithm |
| CN114091665A (en) * | 2020-07-30 | 2022-02-25 | 北京四维图新科技股份有限公司 | Method for training deep neural network and network thereof |
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| CN107578071A (en) * | 2017-10-13 | 2018-01-12 | 北京工业大学 | The unbalanced method of solution data based on Epoch |
| CN108197697A (en) * | 2017-12-29 | 2018-06-22 | 汕头大学 | A kind of dynamic method for resampling of trained deep neural network |
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| CN106548196A (en) * | 2016-10-20 | 2017-03-29 | 中国科学院深圳先进技术研究院 | A kind of random forest sampling approach and device for non-equilibrium data |
| CN107578071A (en) * | 2017-10-13 | 2018-01-12 | 北京工业大学 | The unbalanced method of solution data based on Epoch |
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| CN110324207A (en) * | 2019-07-10 | 2019-10-11 | 深圳市智物联网络有限公司 | A kind of detection method and device of data collection station |
| CN114091665A (en) * | 2020-07-30 | 2022-02-25 | 北京四维图新科技股份有限公司 | Method for training deep neural network and network thereof |
| CN112529033A (en) * | 2020-09-22 | 2021-03-19 | 陕西土豆数据科技有限公司 | Method for solving data imbalance of remote sensing image multi-classification scene segmentation algorithm |
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