CN112115986A - Power transmission line scene classification method based on lightweight neural network - Google Patents
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Abstract
The invention discloses a power transmission line scene classification method based on a lightweight neural network, which comprises the following steps of: (1) configuring the operating environment of the power transmission line scene classification system; (2) designing an optimization prediction module containing a convolution layer to build a lightweight neural network; (3) capturing aerial images of the power transmission line by using a camera carried by an unmanned aerial vehicle, constructing a scene classification data set of the power transmission line, and dividing the aerial images of the power transmission line into a training set and a test set; (4) inputting the images of the training set into the lightweight neural network built in the step (2), training the lightweight neural network, and storing the obtained network weight; (5) and (4) inputting the test set image into the network model obtained in the step (4) for testing, and verifying the performance of the network model. The method has strong adaptability to the complex scene of the power transmission line, compared with VGG-16, the FAST-VGG-16 has low memory occupation ratio and high classification precision, invalid information is effectively removed, and the unmanned aerial vehicle inspection efficiency is improved.
Description
Technical Field
The invention belongs to the field of pattern recognition, and particularly relates to a power transmission line scene classification method based on a lightweight neural network.
Background
With the continuous development of modern society, the daily life and industry of human beings have great dependence on electric power. To meet the demand for electric power in modern society, the length of transmission lines is increasing year by year. In order to guarantee the normal operation of transmission line, the circuit is patrolled and examined absolutely. Because the security of artifical patrolling and examining is poor, and is inefficient, so in recent years artifical patrolling and examining is patrolled and examined gradually by unmanned aerial vehicle and is replaced.
However, in the unmanned aerial vehicle inspection process, a camera can capture a large number of images which do not contain key components of the power transmission line. Removing these invalid data can improve the inspection efficiency, so it is necessary to classify the transmission line scene. The traditional classification mainly depends on mechanical learning algorithms such as a support vector machine and a neural network, the working modes of the traditional mechanical learning algorithms such as the support vector machine depend on an image feature extraction algorithm, and the features obtained by the feature extraction algorithm directly influence the classification precision of the traditional machine learning. However, the image features extracted by the feature extraction algorithm have a large difference from the high-level semantics of the image, and the generalization is poor, which results in that the traditional machine learning algorithm has a poor effect in classifying complex scenes. The power transmission line scene comprises various targets such as towers, trees, grasslands and buildings, and the power transmission line scene characteristics change greatly along with the change of external conditions such as illumination, so that the traditional machine learning is difficult to meet the performance requirements of the power transmission line scene classification problem.
With the development of the convolutional neural network, the deep learning obtains remarkable results in various fields such as semantic segmentation, target detection, image classification and the like. Common classification networks include: AlexNet, GoogleNet, VGG, ResNet, and the like. The convolutional neural network does not depend on an artificial image feature extraction algorithm, image features are directly extracted through a convolutional layer and a pooling layer, finally, the image categories are predicted by utilizing a full-connection layer, network weights are modified through a gradient descent method, and the neural network can deal with the classification problem of complex scenes through the working mode. However, the full connection layer for prediction contains a large number of parameters, and the excessive parameters can cause the network operation speed to be reduced and the occupied memory to be increased, so that the full connection layer is not beneficial to being carried on the unmanned aerial vehicle platform.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a power transmission line scene classification method based on a lightweight neural network, which can remove invalid data and improve the inspection efficiency of an unmanned aerial vehicle.
The technical scheme is as follows: the invention discloses a power transmission line scene classification method based on a lightweight neural network, which comprises the following steps of:
(1) configuring the operating environment of the power transmission line scene classification system;
(2) designing an optimization prediction module containing a convolutional layer, and building a lightweight neural network by using the optimization prediction module;
(3) constructing a scene classification data set of the power transmission line, and dividing aerial images of the power transmission line into a training set and a test set;
(4) inputting the images of the training set into the lightweight neural network built in the step (2), training the lightweight neural network, and storing the obtained network weight;
(5) and (4) inputting the test set image into the network model obtained in the step (4) for testing, and verifying the performance of the network model.
In step (2), the optimized prediction module includes two 3 × 3 convolutional layers, a 1 × 1 convolutional layer, two BN layers, and two maximum pooling layers.
In the optimization prediction module, sigmoid is adopted by the last layer of the convolution layer as an activation function, and Leaky ReLU is adopted by the rest of the convolution layers as the activation function.
The expression of the activation function leak ReLU is as follows:
in the formula, a represents a fixed parameter in the interval (1, + ∞).
And (4) training the lightweight neural network by using a random gradient descent method.
The training of the lightweight neural network by using the stochastic gradient descent method specifically comprises the following steps:
(4.1) activating a neural network function library Tensorflow, constructing a Fast-VGG-16 network, and activating a neural computation acceleration library cuDNN; .
(4.2) inputting the training set picture into a neural network in a tensor form, wherein the positive samples and the negative samples are respectively stored in different files;
(4.3) calculating the gradient descending direction theta' by the following formula:
in the formula, Θ represents the network weight before updating; thetajRepresenting updated network weights; α represents an initial learning rate; i-1 denotes the initial number of iterations, i-imaxThe maximum number of iterations is indicated.
(4.4) inputting the training set picture into a Fast-VGG-16 network by using the SGD training strategy, and training;
(4.5) Loop step S44 until a maximum number of iterations i is reachedmaxAnd ending the loop and saving the network weight.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that: the method has strong adaptability to complex scenes of the power transmission line, compared with VGG-16, FAST-VGG-16 has lower memory occupation ratio and higher classification precision, invalid information can be effectively removed, and the unmanned aerial vehicle inspection efficiency is improved.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is an aerial photography scenario of the power transmission line of the present invention;
FIG. 3 is a network structure diagram of the lightweight neural network Fast-VGG-16 constructed by the invention.
Detailed Description
The invention is described in further detail below with reference to specific embodiments and the attached drawings.
As shown in fig. 1, the power transmission line scene classification method based on the lightweight neural network of the present invention includes the following steps:
(1) configuring the operating environment of the power transmission line scene classification system; the operating environment of the power transmission line scene classification system comprises a software system and a hardware system, wherein the software system comprises a windows10 operating system, a Tensorflow open source software library, a CUDA and a convolutional neural network acceleration library cuDNN; the hardware system adopts NVIDIA Jetson AGX Xavier.
(2) Designing an optimization prediction module containing a convolution layer, and building a lightweight neural network by using the optimization prediction module; the optimized prediction module does not comprise a full connection layer, and consists of two 3 x 3 convolution layers, a 1 x 1 convolution layer, two Batch Normalization (BN) layers and two maximum pooling layers, in the optimized prediction module, except that the activation function adopted by the last convolution layer is sigmoid, the rest layers are all adopted as activation functions, and the module is used for replacing the original prediction to build FAST-VGG-16.
The expression of the activation function Leaky ReLU in the optimization prediction module is as follows:
in the formula, a represents a fixed parameter in the interval (1, + ∞).
The light weight neural network Fast-VGG-16 takes VGG-16 as a reference network, a traditional prediction module is replaced by an optimized prediction module, network parameters and network operation loss are reduced, and the network configuration of the Fast-VGG-16 is shown in Table 1:
TABLE 1 network configuration Table for Fast-VGG-16
(3) Capturing aerial images of the power transmission line by using a camera carried by an unmanned aerial vehicle, establishing a scene classification data set of the power transmission line, and establishing a training set and a testing set according to a ratio of 3: 1.
(4) Inputting the images of the training set into the lightweight neural network built in the step (2), training the lightweight neural network by using a random gradient descent method, and storing the obtained network weight; the method comprises the following specific steps:
(4.1) activating a neural network function library Tensorflow, constructing a lightweight neural network, and activating a neural computation acceleration library cuDNN;
(4.2) inputting the training set images into the lightweight neural network in a tensor form; wherein, the positive sample and the negative sample are respectively stored in different files; as shown in fig. 2, graph (a) shows a positive sample, and graph (b) shows a negative sample;
(4.3) calculating the gradient descending direction theta' by the following formula:
in the formula, Θ represents the network weight before updating; thetajRepresenting updated network weights; α represents an initial learning rate; i-1 denotes the initial number of iterations, i-imaxThe maximum number of iterations is indicated.
(4.4) inputting the training set picture into a Fast-VGG-16 network by using the SGD training strategy, and training;
(4.5) Loop step S44 until a maximum number of iterations i is reachedmaxEnding the cycle and savingNetwork weight.
(5) And (4) inputting the test set image into the Fast-VGG-16 network model obtained in the step (4) for testing, and verifying the performance of the Fast-VGG-16 network model. As shown in FIG. 3, in order to verify the performance of the network, the test set pictures containing the positive and negative samples are input into the Fast-VGG-16 and VGG-16 models which are trained, so as to obtain the classification accuracy of the two networks.
TABLE 2 Fast-VGG-16 vs VGG-16 network Performance
Claims (6)
1. A power transmission line scene classification method based on a lightweight neural network is characterized by comprising the following steps:
(1) configuring the operating environment of the power transmission line scene classification system;
(2) designing an optimization prediction module containing a convolutional layer, and building a lightweight neural network by using the optimization prediction module;
(3) capturing aerial images of the power transmission line by using a camera carried by an unmanned aerial vehicle, constructing a scene classification data set of the power transmission line, and dividing the aerial images of the power transmission line into a training set and a test set;
(4) inputting the images of the training set into the lightweight neural network built in the step (2), training the lightweight neural network, and storing the obtained network weight;
(5) and (4) inputting the test set image into the network model obtained in the step (4) for testing, and verifying the performance of the network model.
2. The power transmission line scene classification method based on the lightweight neural network as claimed in claim 1, wherein: in step (2), the optimized prediction module includes two 3 × 3 convolutional layers, a 1 × 1 convolutional layer, two BN layers, and two maximum pooling layers.
3. The power transmission line scene classification method based on the lightweight neural network as claimed in claim 2, wherein: in the optimization prediction module, sigmoid is adopted by the last layer of the convolution layer as an activation function, and Leaky ReLU is adopted by the rest of the convolution layers as the activation function.
5. The power transmission line scene classification method based on the lightweight neural network as claimed in claim 1, wherein: and (4) training the lightweight neural network by using a random gradient descent method.
6. The power transmission line scene classification method based on the lightweight neural network as claimed in claim 5, wherein the training of the lightweight neural network by using the stochastic gradient descent method specifically comprises the following steps:
(4.1) activating a neural network function library Tensorflow, constructing a lightweight neural network, and activating a neural computation acceleration library cuDNN;
(4.2) inputting the training set images into the lightweight neural network in a tensor form; wherein, the positive sample and the negative sample are respectively stored in different files;
(4.3) calculating the gradient descending direction theta' by the following formula:
in the formula, Θ represents the network weight before updating; thetajRepresenting updated network weights; alpha meterInitializing an initial learning rate; i-1 denotes the initial number of iterations, i-imaxThe maximum number of iterations is indicated.
(4.4) inputting the training set picture into a Fast-VGG-16 network by using the SGD training strategy, and training;
(4.5) Loop step S44 until a maximum number of iterations i is reachedmaxAnd ending the loop and saving the network weight.
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CN113486865A (en) * | 2021-09-03 | 2021-10-08 | 国网江西省电力有限公司电力科学研究院 | Power transmission line suspended foreign object target detection method based on deep learning |
CN113569672A (en) * | 2021-07-16 | 2021-10-29 | 国网电力科学研究院有限公司 | Lightweight target detection and fault identification method, device and system |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107274451A (en) * | 2017-05-17 | 2017-10-20 | 北京工业大学 | Isolator detecting method and device based on shared convolutional neural networks |
CN110349146A (en) * | 2019-07-11 | 2019-10-18 | 中原工学院 | The building method of fabric defect identifying system based on lightweight convolutional neural networks |
CN110827251A (en) * | 2019-10-30 | 2020-02-21 | 江苏方天电力技术有限公司 | Power transmission line locking pin defect detection method based on aerial image |
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107274451A (en) * | 2017-05-17 | 2017-10-20 | 北京工业大学 | Isolator detecting method and device based on shared convolutional neural networks |
CN110349146A (en) * | 2019-07-11 | 2019-10-18 | 中原工学院 | The building method of fabric defect identifying system based on lightweight convolutional neural networks |
CN110827251A (en) * | 2019-10-30 | 2020-02-21 | 江苏方天电力技术有限公司 | Power transmission line locking pin defect detection method based on aerial image |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113569672A (en) * | 2021-07-16 | 2021-10-29 | 国网电力科学研究院有限公司 | Lightweight target detection and fault identification method, device and system |
CN113486865A (en) * | 2021-09-03 | 2021-10-08 | 国网江西省电力有限公司电力科学研究院 | Power transmission line suspended foreign object target detection method based on deep learning |
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