Improving the accuracy of computer-aided radiographic weld inspection by feature selection

Abstract This paper presents new results of our continuous effort to develop a computer-aided radiographic weld inspection system. The focus of this study is on improving accuracy by feature selection. To this end, we propose two versions of ant colony optimization (ACO)-based algorithms for feature selection and show their effectiveness to improve the accuracy in detecting weld flaws and the accuracy in classifying weld flaw types. The performances of ACO-based methods are compared with that of no feature selection and that of sequential forward floating selection, which is a known good feature selection method. Four different classifiers, including nearest mean, k -nearest neighbor, fuzzy k -nearest neighbor, and center-based nearest neighbor, are employed to carry out the tasks of weld flaw identification and weld flaw type classification.

[1]  Jian-Hui Jiang,et al.  Modified Ant Colony Optimization Algorithm for Variable Selection in QSAR Modeling: QSAR Studies of Cyclooxygenase Inhibitors , 2005, J. Chem. Inf. Model..

[2]  Zhong Yan,et al.  Ant Colony Optimization for Feature Selection in Face Recognition , 2004, ICBA.

[3]  Amr M. Youssef,et al.  Artificial Life Feature Selection Techniques for Prostrate Cancer Diagnosis Using TRUS Images , 2005, ICIAR.

[4]  Petra Perner,et al.  Improving the accuracy of decision tree induction by feature preselection , 2001, Appl. Artif. Intell..

[5]  K Murakami.  Image processing for non-destructive testing , 1990 .

[6]  Romeu Ricardo da Silva,et al.  Pattern recognition of weld defects detected by radiographic test , 2004 .

[7]  T. Warren Liao,et al.  Classification of welding flaw types with fuzzy expert systems , 2003, Expert Syst. Appl..

[8]  P. Rose,et al.  Automatic recognition of weld defects in x-ray inspection , 1992 .

[9]  Lúcia Valéria Ramos de Arruda,et al.  An object detection and recognition system for weld bead extraction from digital radiographs , 2006, Comput. Vis. Image Underst..

[10]  Xin Wang,et al.  Radiographic Image Segmentation for Weld Inspection Using a Robust Algorithm , 2005 .

[11]  T. W. Liao,et al.  Detection of welding flaws from radiographic images with fuzzy clustering methods , 1999, Fuzzy Sets Syst..

[12]  Yueming Li,et al.  Extraction of welds from radiographic images using fuzzy classifiers , 2000, Inf. Sci..

[13]  A Gayer,et al.  Automatic recognition of welding defects in real-time radiography , 1990 .

[14]  Ling Wang,et al.  A Modified Discrete Binary Ant Colony Optimization and Its Application in Chemical Process Fault Diagnosis , 2006, SEAL.

[15]  Gang Wang,et al.  Automatic identification of different types of welding defects in radiographic images , 2002 .

[16]  Hong Hu,et al.  Ant Colony Optimization Combining with Mutual Information for Feature Selection in Support Vector Machines , 2005, Australian Conference on Artificial Intelligence.

[17]  Yasuo Suga,et al.  Application of artificial neural network to discrimination of defect type in automatic radiographic testing of welds , 1999 .

[18]  S. Matsui,et al.  DEVELOPMENT OF AN AUTOMATIC WELD DEFECT IDENTIFICATION SYSTEM FOR RADIOGRAPHIC TESTING , 1993 .

[19]  Zheng-Zhi Wang,et al.  Center-based nearest neighbor classifier , 2007, Pattern Recognit..

[20]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[21]  T. Warren Liao,et al.  Fuzzy reasoning based automatic inspection of radiographic welds: weld recognition , 2004, J. Intell. Manuf..

[22]  T. Warren Liao,et al.  Automated Extraction of Welds from Digitized Radiographic Images Based on MLP Neural Networks , 1997, Appl. Artif. Intell..

[23]  James M. Keller,et al.  A fuzzy K-nearest neighbor algorithm , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[24]  Yueming Li,et al.  An automated radiographic NDT system for weld inspection: Part II—Flaw detection , 1998 .

[25]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[26]  Shingo Nagashima,et al.  A method for defect segmentation in digital radiographs of pipeline girth welds , 1996 .

[27]  Sreeram Ramakrishnan,et al.  A hybrid approach for feature subset selection using neural networks and ant colony optimization , 2007, Expert Syst. Appl..

[28]  Yue Min Zhu,et al.  Uncertainty modelling using Dempster-Shafer theory for improving detection of weld defects , 2003, Pattern Recognit. Lett..

[29]  Rafael Bello,et al.  Two Step Ant Colony System to Solve the Feature Selection Problem , 2006, CIARP.

[30]  Domingo Mery,et al.  Segmentation of welding discontinuities using a robust algorithm , 2004 .

[31]  T. Warren Liao,et al.  An automated radiographic NDT system for weld inspection: Part I — Weld extraction , 1996 .