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Welding defect detection method based on feature extraction and extreme searching

LIANG Wei1,2, TAO Liang3, ZHANG Guangxian1, LI Zhenhua1   

  1. 1. School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China;
    2. Jinan Engineering Vocational Technical College, Jinan 250200, Shandong, China;
    3. School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, Shandong, China
  • Received:2014-01-08 Online:2014-06-20 Published:2014-01-08

Abstract: A location detecting algorithm of the welding defect based on the grayscale distribution curve feature was proposed. At first, the welding line X-ray digital image was acquired by the flat panel detector. Secondly, because the problem of large noise and low dynamic range is ubiquitous, the time domain image frame sequence integration method was adopted to reduce the image noise and enhance the defect detail. Then, the optimal integration time was calculated. Thirdly, the grayscale value curve was drawn parallel to the welding line direction. The welding line image was segmented on the basis of its second derivative curve. At last, extreme point was searched in the welding line image. The size and location were decided if obvious maximum value was existed. Experimental results proved the high accuracy.

Key words: non-destructive testing, defect detection, digital X-ray imaging, welding line segmentation, extreme searching

[1] LI Chunlei, ZHANG Zhaoxiang, LIU Zhoufeng, LIAO Liang, ZHAO Quanjun. A novel fabric defect detection algorithm based on textural differential visual saliency model [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(4): 1-8.
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