Technical Papers Parallel Session-IV: Localization and classification of welding defects using genetic algorithm based optimal feature set

Abstract/Description

Radiography is being widely used as a nondestructive testing technique to investigate the safety and reliability of welded process. In this paper an automated weld defect recognition framework is presented, which employs image processing and pattern recognition methods on weld radiographs. Initially, a pre-processing step is performed on radiographs that suppresses undesired distortions and enhances image features important for further processing. After image pre-processing, a set of features including the geometric and texture features are extracted from each object in a segmented image. Genetic algorithm is applied for selecting the optimal feature sets. The optimal and reduced feature vector is then given as input to SVM and ANN for classification. The last step of the recognition system includes the evaluation of the detected/classified defects in the weld on the basis of acceptance criterion. Experimental results show that an overall improvement in performance and accuracy is achieved using GA based optimal features with SVM as classifier.

Location

C-9, AMAN CED

Session Theme

Technical Papers Parallel Session-IV (Algorithms)

Session Type

Parallel Technical Session

Session Chair

Dr. Sajjad Haider Zaidi

Start Date

13-12-2015 2:50 PM

End Date

13-12-2015 3:10 PM

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Dec 13th, 2:50 PM Dec 13th, 3:10 PM

Technical Papers Parallel Session-IV: Localization and classification of welding defects using genetic algorithm based optimal feature set

C-9, AMAN CED

Radiography is being widely used as a nondestructive testing technique to investigate the safety and reliability of welded process. In this paper an automated weld defect recognition framework is presented, which employs image processing and pattern recognition methods on weld radiographs. Initially, a pre-processing step is performed on radiographs that suppresses undesired distortions and enhances image features important for further processing. After image pre-processing, a set of features including the geometric and texture features are extracted from each object in a segmented image. Genetic algorithm is applied for selecting the optimal feature sets. The optimal and reduced feature vector is then given as input to SVM and ANN for classification. The last step of the recognition system includes the evaluation of the detected/classified defects in the weld on the basis of acceptance criterion. Experimental results show that an overall improvement in performance and accuracy is achieved using GA based optimal features with SVM as classifier.