Insulator detection method, device and equipment based on deep convolutional neural network

An insulator detection and neural network technology, which is applied in the field of insulator detection based on deep convolutional neural network, can solve the problems of low efficiency of artificial naked eye detection, inaccurate extraction of insulator information, and poor detection effect.

Pending Publication Date: 2020-10-30
XIANGTAN UNIV
View PDF0 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These factors make the detection efficiency of manual naked eyes low, the workload is huge, and it is easy to cause a large number of false detections and missed detections due to visual fatigue.
[0004] In related technologies, in the final result of detecting insulators based on convolutional neural network, the target frame is usually h

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
  • Insulator detection method, device and equipment based on deep convolutional neural network
  • Insulator detection method, device and equipment based on deep convolutional neural network
  • Insulator detection method, device and equipment based on deep convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0046] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be described in detail below. Apparently, the described embodiments are only some of the embodiments of this application, not all of them. Based on the embodiments in the present application, all other implementation manners obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present application.

[0047] see figure 1 , figure 1 It is a flowchart of an insulator detection method based on a deep convolutional neural network provided by an embodiment of the present application.

[0048] Such as figure 1 As shown, this embodiment provides an insulator detection method based on a deep convolutional neural network, which may specifically include the following steps:

[0049] Step 11, collect several original images, and preprocess the original images to cons...

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 an insulator detection method, device and equipment based on a deep convolutional neural network. The insulator detection method based on the deep convolutional neural networkcomprises the steps: collecting a plurality of original images, carrying out preprocessing on the original images, and constructing a sample training set; constructing a multi-angle candidate regionnetwork structure based on an RPN network, an inclination NMS algorithm and an angle factor; based on the multi-angle candidate region network structure, training the deep convolutional neural networkby using the sample training set to obtain a training model; and recognizing the to-be-recognized image by using the training model, and outputting an insulator image with a target frame if an insulator exists in the to-be-recognized image. Thus, the extraction precision of the insulator information is effectively improved, the situation that missing detection occurs to insulators with complex backgrounds and small targets during detection is avoided, and the situation that the detection effect is poor due to partial overlapping or shielding in insulator images is also avoided.

Description

technical field [0001] The present application relates to the technical field of insulator detection, in particular to an insulator detection method, device and equipment based on a deep convolutional neural network. Background technique [0002] With the continuous development of the country's economic level and science and technology, power consumption continues to rise. As the infrastructure of power transportation, the safety and stability of transmission lines is the basic guarantee for national power consumption. Insulators are important components of transmission lines, and they are responsible for electrical insulation isolation and mechanical protection and support. Since most insulators are exposed to the natural environment all the year round, they must withstand lightning, wind and snow, high temperature, etc. while bearing the original load. In extreme weather, insulators are prone to aging and damage, which brings great safety hazards to power transportation. ...

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): G06T7/00G06K9/32G06K9/34G06K9/62G06N3/04
CPCG06T7/0002G06V10/25G06V10/267G06N3/045G06F18/253G06F18/214
Inventor 肖业伟陈志豪李志强
Owner XIANGTAN UNIV
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