Insulator image recognition method and system based on salient features

A technology of image recognition and insulators, which is applied in the field of image processing, can solve problems such as ineffective recognition of the appearance of insulators, achieve the effects of reducing a large amount of redundant data and information, improving recognition accuracy, and reducing interference

Pending Publication Date: 2020-11-03
CHENGDU TECHCAL UNIV
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is that in the existing insulator identification methods, there is a problem that both manual field detection and UAV aerial photography detection will be affected by various factors when detecting the collected images, resulting in invalid insulator appearance recognition. The purpose is to provide a A salient feature-based insulator image recognition method and system to solve the above problems

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 image recognition method and system based on salient features
  • Insulator image recognition method and system based on salient features
  • Insulator image recognition method and system based on salient features

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0068] Such as figure 1 As shown, an insulator image recognition method based on salient features, including:

[0069] S1: Perform a feature extraction operation on the collected insulator image to obtain the salient features of the insulator image;

[0070] S2: Using BP neural network to train the salient features to obtain an insulator image recognition model;

[0071] S3: Transmitting the newly collected insulator image to the insulator image recognition model to obtain a recognition result.

[0072] In this embodiment, an effective classifier for insulators is obtained through neural network learning. At the same time, the addition of salient feature processing to extract the appearance of the insulator image by using the contrast of the histogram effectively reduces a large amount of redundant data and information in the original image, and improves the follow-up process. Using the training effect of neural network learning modeling, the fitting degree is greatly improv...

Embodiment 2

[0117] On the basis of Example 1, the collected insulator images are subjected to insulator feature saliency extraction, sample training set modeling, sample test set verification, and insulator image recognition;

[0118] And in the step of extracting the salience of insulator features, the original insulator image is input into MATLAB, and the existing script file and function file are used to process the HC method to obtain the recognition result.

[0119] After the processing of the above steps, a large amount of redundant data and information in the original insulator image in this embodiment are effectively reduced.

[0120] Such as image 3 As shown, the S2 includes the following steps:

[0121] S21: Determine the network structure of the insulator image recognition model according to the salient features;

[0122] S22: Input the insulator training sample parameter matrix as P, the target parameter matrix as T, the matrix T is the insulator test sample, the number of ...

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 discloses an insulator image recognition method and system based on saliency features, and the method comprises the steps: S1, carrying out the feature extraction of a collected insulator image, and obtaining the saliency features of the insulator image; s2, training the salient features by adopting a BP neural network to obtain an insulator image recognition model; and S3, transmitting the newly acquired insulator image to the insulator image recognition model to obtain a recognition result. According to the method, an effective classifier for the insulator is obtained through neural network learning, meanwhile, significant feature processing of the insulator image appearance is extracted through histogram contrast, a large amount of redundant data and information in an original image are effectively reduced, the training effect of subsequent modeling through neural network learning is improved, the fitting degree is greatly improved, and the identification precision ofthe insulator is correspondingly improved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an insulator image recognition method and system based on salient features. Background technique [0002] At present, insulator identification methods are mainly traditional manual on-site inspection and UAV aerial photography inspection. Manual inspection technology has high recognition accuracy, but information collection is difficult and inefficient. UAVs are used for inspection of transmission lines, although information collection is convenient. , but it is still necessary to manually inspect the collected images. Although this technology is more efficient than the traditional manual field inspection technology, it will also be affected by related factors, such as the influence of light when the UAV is shooting insulators and artificial long-term inspection. The detection of aerial images is prone to missed detection. [0003] Machine vision technology has the advantages of ...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06N3/04G06N3/08
CPCG06N3/084G06V10/50G06V10/56G06V10/462G06N3/045
Inventor 吴婕萍于文萍赵文昊郑骊陈宇峰钟英冯丹彤
Owner CHENGDU TECHCAL 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

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