Paper Info
Title
Research on power equipment recognition method based on image processing.
Abstract
Electric energy is an indispensable energy in life, and the power network is the basis to ensure its normal circulation, in which the operation status of power equipment is one of the key factors to determine the safe and stable operation of the power network. In the information age, the traditional manual periodic inspection and the existing method of relying on manual monitoring equipment operation status can no longer meet the needs of safe operation of equipment; relying on computer technology and image recognition technology to achieve automatic identification of power equipment has become a research hot spot. In order to realize automatic identification of power equipment, this paper presents a method of recognition of power equipment based on image processing. Firstly, the power equipment image is preprocessed by various denoising and sharpening algorithms to remove the noise and distortion of the image and improve the image quality; secondly, the SIFT algorithm is used to extract image features, and PCA algorithm is used to reduce the dimension; finally, the support vector machine is used to classify and recognize the image. The simulation results show that the proposed denoising and sharpening algorithms can process images well and improve the quality of images. The support vector machine is used to classify the image features processed by SIFT algorithm and PCA algorithm, and the automatic recognition of power equipment is realized. And the method of power identification based on image processing proposed in this paper has good recognition accuracy.
Year
DOI
Venue
2019
10.1186/s13640-019-0452-5
EURASIP Journal on Image and Video Processing
Keywords
Field
DocType
Power equipment, Image processing, SIFT algorithm, PCA algorithm, Support vector machine
Sharpening,Computer vision,Scale-invariant feature transform,Pattern recognition,Computer science,Feature (computer vision),Support vector machine,Image processing,Image quality,Artificial intelligence,Distortion,Computer technology
Journal
Volume
Issue
ISSN
2019
1
1687-5281
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Haiyan Wang13916.48
Fanwei Meng2112.86