DocumentCode
3065394
Title
Image Processing Technology for Pipe Weld Visual Inspection
Author
Liao, Gaohua ; Xi, Junmei
Author_Institution
Nanchang Inst. of Technol., Nanchang, China
Volume
1
fYear
2009
fDate
10-11 July 2009
Firstpage
173
Lastpage
176
Abstract
To the pipeline welding defect detection, robot tracking weld reliably, the welds original image acquired by visual sensor need pretreatment to eliminate the impact of noise. A pipeline welding machine vision detection method proposed in this paper. First of all, the use of neighborhood mean filter for smoothing, the largest variance threshold method selecting adaptive threshold to segmentation image. Image after smooth will be the binarization processing. Then remove the small area noise with labeling method, get a clear image of the weld. The level of projection method to the recognition of weld image and determine the location of weld. The experiments show that the pretreatment method solute the prevailing situation of uneven illumination, and also reducing the size of the processing image, reducing the amount of data, saving time, meeting the needs of the pipeline weld tracking real-time detection, laying a solid foundation for follow-up quality inspection.
Keywords
image segmentation; inspection; mechanical engineering computing; pipelines; reliability; welding; adaptive image thresholding; binarization processing; image processing; image segmentation; pipe weld visual inspection; pipeline welding defect detection; pipeline welding machine vision detection; robot tracking weld reliably; visual sensor; Adaptive filters; Image processing; Image segmentation; Image sensors; Inspection; Machine vision; Pipelines; Robot sensing systems; Smoothing methods; Welding; Image processing; Object recognition; computer vision; robot;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Engineering, 2009. ICIE '09. WASE International Conference on
Conference_Location
Taiyuan, Shanxi
Print_ISBN
978-0-7695-3679-8
Type
conf
DOI
10.1109/ICIE.2009.262
Filename
5210863
Link To Document