CFP last date
20 December 2024
Reseach Article

Article:Automatic Defect Detection and Counting In Radiographic Weldment Images

by Prof.Mythili Thiruganam, Dr.S.Margret Anouncia, Sachin Kantipudi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 2
Year of Publication: 2010
Authors: Prof.Mythili Thiruganam, Dr.S.Margret Anouncia, Sachin Kantipudi
10.5120/1457-1971

Prof.Mythili Thiruganam, Dr.S.Margret Anouncia, Sachin Kantipudi . Article:Automatic Defect Detection and Counting In Radiographic Weldment Images. International Journal of Computer Applications. 10, 2 ( November 2010), 1-5. DOI=10.5120/1457-1971

@article{ 10.5120/1457-1971,
author = { Prof.Mythili Thiruganam, Dr.S.Margret Anouncia, Sachin Kantipudi },
title = { Article:Automatic Defect Detection and Counting In Radiographic Weldment Images },
journal = { International Journal of Computer Applications },
issue_date = { November 2010 },
volume = { 10 },
number = { 2 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume10/number2/1457-1971/ },
doi = { 10.5120/1457-1971 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:58:42.476312+05:30
%A Prof.Mythili Thiruganam
%A Dr.S.Margret Anouncia
%A Sachin Kantipudi
%T Article:Automatic Defect Detection and Counting In Radiographic Weldment Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 10
%N 2
%P 1-5
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Digital Image Analysis is one of the most challenging and important tasks in many scientific and engineering applications. Extracting the Region of Interest (ROI) from the image and recognition in image processing are very important steps. When these tasks are manually performed, it is tedious and difficult involving human experts. This paper focuses on automatic defect detection and counting in radiographic weldment images thus considering defects in weldment images as object of interest. To detect defects in radiographic weldment images, thresholding and segmentation algorithm is used and a new procedure is introduced for counting number of defects in the input images. The results obtained from the proposed work are impressive with respect to the computational time and defect detection rate. The performance of the proposed algorithm is found better than the existing defect detection algorithms.

References
  1. Abdelhak Mahmoudi & Fakhita Regragui. Welding Defect Detection by Segmentation of Radiographic Images in 2009 World Congress on Computer Science and Information Engineering.
  2. F. R. A Mahmoudi and al. A novel method for welding defects detection in radiographic images. In 4th International Symposium on Image/Video Communications over fixed and mobile networks (ISIVC), June 2008.
  3. F.l.C. Shitong Wang and F.Xiong. A novel image thresholding method based on parzen window estimate. Pattern Recognition, 41:117–129, March 2008.
  4. G. M. Atiqur Rahaman, Md. Mobarak Hossain, Automatic Defect Detection and Classification Technique from Image: A Special Case Using Ceramic Tiles , (IJCSIS) International Journal of Computer Science and Information Security, Vol. 1, No. 1, May 2009 [0906.3770]
  5. J. Sauvola and M. Pietikainen. Adaptive document image binarisation.PatternRecognition, 33(2):225–236,2000.
  6. M. M. R. T Y Lim and M. A. Khalid. Automatic classif- cation of weld defects using simulated data and an MLP neural network. Insight, 49(3):154–159, March2007.
  7. N. Nacereddine, M. Zelmat, S. S. Belaïfa and M. Tridi ,”Weld defect detection in industrial radiography based digital image processing” , World Academy of Science, Engineering and Technology 2 2005.
  8. N. Ostu. A threshold selection method from gray level Histograms. IEEE Trans. on Systems, Man and Cybern, SMC 9:62–66,1979
  9. P.L.YanWang,YiSunandH.Wang. Detection of line weld defects based on multiple thresholds and support vector machine. NDT &E International, 41:517-524, May 2008.
  10. S. Mansouri Alghalandis, “Welding Defect Pattern Recognition in Radiographic Images Of Gas Pipelines Using Adaptive Feature Extraction Method And Neural Network Classifier “, 23rd World Gas Conference, Amsterdam 2006.
  11. S.Margret Anouncia and J.Godwin Joseph. Approaches for Automated Object Recognition and Extraction from Images – a Study In: Journal of Computing and Information Technology - CIT 17, 2009, 4, 359–370 doi:10.2498/cit.1001363.
Index Terms

Computer Science
Information Sciences

Keywords

Defect Weldment Region of Interest (ROI)