DocumentCode :
2075772
Title :
Research on Image Recognition Method of In-Service Pipeline Corrosion Fault
Author :
Yuan Peixin ; Tan Jun
Author_Institution :
Northeastern Univ., Shenyang, China
fYear :
2009
fDate :
17-19 Oct. 2009
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, for the practical demand of in-service pipeline detection, a new way called mathematical morphology wavelet de-noising innovative method has been developed, based on separate defect points. This method needs to extract the edge of defective parts by wavelet transform modulus maximum method, select some key characteristic parameters in favor of defects identification like fine length, moment invariant, gray energy and so on, and recognize patterns by means of single-output BP neural network. This method has been successfully applied to differentiate the weld joints and corrosion defects of pipelines, and quantitatively recognize the corrosion defects.
Keywords :
edge detection; mathematical morphology; neural nets; defect points; defects identification; image recognition method; in-service pipeline corrosion fault; mathematical morphology wavelet de-noising innovative method; patterns recognition; single-output BP neural network; wavelet transform modulus maximum method; Character recognition; Corrosion; Image edge detection; Image recognition; Morphology; Neural networks; Noise reduction; Pattern recognition; Pipelines; Wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4244-4129-7
Electronic_ISBN :
978-1-4244-4131-0
Type :
conf
DOI :
10.1109/CISP.2009.5301140
Filename :
5301140
Link To Document :
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