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A Deep Learning-Based Insulator Fault Location Recognition Method

A fault location and identification method technology, applied in neural learning methods, image analysis, image enhancement, etc., can solve problems such as inability to recognize insulator fault diagnosis, inability to combine insulator background information well, etc., to achieve effective category judgment and location The effect of regression, recognition rate increase, and position positioning regression accuracy

Active Publication Date: 2022-03-25
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0003] At present, in the research of insulator fault identification method, in the research of insulator identification based on ordinary convolutional neural network, the whole image of the insulator is used as the input of the network, which cannot combine the background information of the insulator well with the entire image as the input of the network , cannot accurately identify insulators and perform fault diagnosis in complex environments

Method used

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  • A Deep Learning-Based Insulator Fault Location Recognition Method
  • A Deep Learning-Based Insulator Fault Location Recognition Method
  • A Deep Learning-Based Insulator Fault Location Recognition Method

Examples

Experimental program
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Embodiment

[0052] like figure 1 As shown, a deep learning-based insulator fault location and identification method includes the following steps:

[0053] Step 1. Collect insulator images and preprocess them to construct an insulator image dataset;

[0054] 1a) Use a drone equipped with a camera to patrol the high-voltage line to take pictures of insulators, crop the insulators that have been photographed, normalize the pictures to 300*300, and perform amplification processing on the cropped pictures. The methods include rotating, cropping, panning, mirroring, sharpening, and denoising the image, expanding the number of pictures to 10,000, creating a folder insulator for storing data, and creating Annotations and ImageSets under the insulator folder. , Images, label folder, put the image in the Images folder;

[0055] 1b) Use Bbox-label-tools to mark the position and category of the insulator on the image amplified in step 1a), and modify the category label into three categories, namely...

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Abstract

The invention discloses an insulator fault location and recognition method based on deep learning, which mainly solves the problem of the naked eye recognition rate of insulator faults during power operation. The method mainly includes the following steps: collecting insulator images and performing preprocessing, and constructing insulators Image data set; construct a deep convolutional neural network model, which is used to extract the characteristics of insulators, and perform category judgment and target positioning on insulators; use the insulator image data set to train the deep convolutional neural network model; finally use the training to complete The deep convolutional neural network model is used to locate and identify faults in the image of the insulator to be tested. The deep learning-based insulator fault location and recognition method of the present invention has high accuracy and fast speed for insulator fault location, and realizes real-time detection of insulator fault location.

Description

technical field [0001] The invention belongs to the fields of image target detection and recognition and computer vision, and mainly relates to a real-time detection method for fault location and recognition of insulators of small power components on high-voltage transportation lines, in particular: a deep learning-based insulator fault location and recognition method. Background technique [0002] As a common power component in power transportation, insulators play an irreplaceable role in the safe operation of the power grid. Insulators are exposed all year round. The accumulation of time and bad weather will damage the performance of insulators and affect the transportation of electricity. It is very important to detect and replace faulty insulators in time. At present, the manual analysis and processing of insulators is a huge workload and low efficiency, and there are deviations caused by the experience and personal qualities of the staff. Therefore, a reliable method f...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/73G06N3/04G06N3/08
CPCG06N3/084G06T7/0004G06T7/73G06T2207/10004G06T2207/30164G06T2207/20084G06T2207/20081G06N3/045
Inventor 田立斌阮海清
Owner SOUTH CHINA UNIV OF TECH
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