DocumentCode :
2611268
Title :
Automatic weld defect detection in real-time X-ray images based on support vector machine
Author :
Shao, Jiaxin ; Shi, Han ; Du, Dong ; Wang, Li ; Cao, Huayong
Author_Institution :
Dept. of Mech. Eng., Tsinghua Univ., Beijing, China
Volume :
4
fYear :
2011
fDate :
15-17 Oct. 2011
Firstpage :
1842
Lastpage :
1846
Abstract :
Automatic weld defect detection based on real-time X-ray image plays a vital role in improving the automation level of radiographic inspection in industry. Most of the existing real-time automatic inspection technologies only use defect segmentation algorithms, which leads to the difficulty of reducing both the undetected rate and false alarm rate. In this paper, an effective method based on Support Vector Machine (SVM) is proposed to detect weld defect in real-time X-ray images. Firstly, all potential defects are segmented by background subtraction algorithm. Then three features including defect area, average grayscale difference to its surrounding district and grayscale standard deviation are extracted. Lastly, the extracted features are used as input to SVM classifier to distinguish non-defects from defects. Results show that the proposed automatic defect detection method can reduce the undetected rate and false alarm rate effectively in real-time X-ray images of weld.
Keywords :
X-ray imaging; feature extraction; image classification; image segmentation; inspection; production engineering computing; radiography; support vector machines; welds; SVM classifier; automatic weld defect detection; average grayscale difference; background subtraction algorithm; defect area; defect segmentation algorithms; feature extraction; grayscale standard deviation; radiographic inspection; real-time X-ray images; support vector machine; Feature extraction; Image segmentation; Real time systems; Support vector machines; Training; Welding; X-ray imaging; Background subtraction; Defect detection; Real-time X-ray image; Support Vector Machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing (CISP), 2011 4th International Congress on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9304-3
Type :
conf
DOI :
10.1109/CISP.2011.6100637
Filename :
6100637
Link To Document :
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