High-voltage line insulator defect detection method based on generative adversarial network

A technology for defect detection and high-voltage lines, applied in biological neural network models, optical test flaws/defects, measurement devices, etc., can solve the problems of small number of defect samples, difficult acquisition, no robustness, etc., to improve feasibility and adaptive effects

Pending Publication Date: 2021-01-05
SHANGHAI UNIVERSITY OF ELECTRIC POWER
View PDF2 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method needs to use a large number of insulator image data with defects. In practice, the number of defect samples is small and difficult to obtain. Moreover, ...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • High-voltage line insulator defect detection method based on generative adversarial network
  • High-voltage line insulator defect detection method based on generative adversarial network
  • High-voltage line insulator defect detection method based on generative adversarial network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0062] A high-voltage line insulator defect detection method based on generative adversarial networks, such as figure 1 shown, including the following steps:

[0063] S1: Establish an image data set, the image data set includes a training set and a test set, the training set includes normal insulator images, and the test set includes normal insulator images and defective insulator images.

[0064] In this embodiment, when establishing an image data set, the model of the present invention only needs to learn the data distribution of positive examples during training, so it is necessary to collect a large number of normal insulator images to make a training set of positive examples. At the same time, in order to evaluate the performance of the model, obtain the detection adaptive threshold, and to improve the accuracy of the abnormal detection of the model, a test link is added to the training step, so it is also necessary to make a test set, which needs to have both positive sa...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a high-voltage line insulator defect detection method based on a generative adversarial network. The method comprises the following steps: establishing an image data set; constructing an improved GANomaly model, wherein the improved GANomaly model sends a feature map output by each convolution layer in the encoder sub-network to the decoder sub-network for feature fusion;constructing a loss function of the improved GANomaly model; training an improved GANomaly model; sending the defective insulator image in the test set into a trained improved GANomaly model to obtainan adaptive threshold; sending a to-be-detected insulator image to the trained improved GANomaly model to judge the image type. Compared with the prior art, the method solves the problems that the number of negative samples in the insulator image is small, the negative samples are difficult to collect, a large amount of energy is needed to label data, and the feasibility and adaptability of the training process are improved.

Description

technical field [0001] The invention relates to the field of insulator defect detection, in particular to a high-voltage line insulator defect detection method based on a generative confrontation network. Background technique [0002] Insulators are important devices for electrical isolation and mechanical fixing of wires in high-voltage power transmission systems. Insulator failure directly threatens the stability and safety of transmission lines. Statistically, accidents caused by insulator defects account for the highest proportion of power system failures. Therefore, intelligent and timely detection of insulator defects is particularly important. In recent years, with the emergence of aerial work platforms such as helicopters and drones, their work is efficient, accurate, and safe, and has become an important tool for electrical equipment inspection. In order to overcome the limitations of manual inspection, it has become urgent to develop automated defect inspection t...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06T7/00G06K9/62G06N3/04G01N21/88G01N21/95
CPCG06T7/0004G01N21/8851G01N21/95G06T2207/20081G06T2207/20084G06T2207/30108G01N2021/8883G06N3/045G06F18/214G06F18/241G06F18/253
Inventor 王道累孙嘉珺朱瑞韩清鹏袁斌霞张天宇李明山李超
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap