An electric power equipment detection algorithm based on a convolution neural network

A technology of convolutional neural network and power equipment, applied in the direction of biological neural network model, neural learning method, neural architecture, etc., can solve the problems of difficult network convergence and low detection accuracy, and achieve excellent detection performance and real-time accurate detection effect

Inactive Publication Date: 2019-03-08
SUN YAT SEN UNIV
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However, if the pre-trained model is not used for fine-tuning, the network is...

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  • An electric power equipment detection algorithm based on a convolution neural network
  • An electric power equipment detection algorithm based on a convolution neural network
  • An electric power equipment detection algorithm based on a convolution neural network

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[0054] The present invention is further described below.

[0055] Implementation process and examples of the present invention are as follows:

[0056] (1) First, 5,000 images were acquired through the visible light camera and the thermal imaging camera, totaling 10,000 images. Taking visible light images as an example, divide 5000 visible light images into 4500 training sets and 500 test sets, and there is no intersection between the training set and the test set. The division of thermal imaging images is the same as that of visible light images. Then use the labelImg tool to label the image to obtain the labels about the power equipment in the image. The label of the electrical equipment contains the coordinates (x1, y1) of the upper left corner of the relevant electrical equipment in the image, the coordinates (x2, y2) of the lower right corner and the type c of the electrical equipment. In particular, there may be multiple different types of electrical equipment in an i...

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Abstract

The invention relates to an electric power equipment detection algorithm based on a convolution neural network, comprising the following steps of (1) a labelImg tool is used for labeling the visible light image and infrared image of the electric power equipment, and obtaining the label about the electric power equipment in the image; (2) the images and device-level tags in the training set are inputted to the detection model of electric power equipment for training, and update the model parameters by using the gradient descent algorithm of driving quantity to back propagate; (3) in the testingphase, the prediction box is reduced by soft non-maximum suppression, and the prediction box whose confidence level is higher than the threshold value is outputted finally; (4) compared with the current power equipment detection methods, the algorithm can converge and obtain excellent detection performance without fine-tuning on the pre-training model, has excellent robustness for small power equipment, can effectively reduce the phenomenon of frame error and frame leakage, and has high detection accuracy and detection speed to achieve real-time processing.

Description

technical field [0001] The invention relates to the field of image target detection, namely a convolutional neural network-based power equipment detection algorithm. Background technique [0002] At present, there are a large number of power equipment in our country. They are all independent individuals and are combined into a huge power system through power lines. The health status of power equipment directly affects the normal operation of the power system and is related to our daily life. For electrical equipment, if we can quickly and accurately locate their locations and identify the corresponding categories, it will be of great help to us in analyzing their internal temperature changes and calculating their external rust and damage. It is beneficial for us to evaluate the health of the power equipment, avoid the impact of repairing the power equipment after it is damaged, and reduce the workload of construction personnel. However, the health analysis of the power sys...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V20/10G06N3/045G06F18/214
Inventor 陈楚城戴宪华
Owner SUN YAT SEN UNIV
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