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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.

In this paper we present a welding defect detection system using radiographic images. Main goal is to craft a dependable system because a human evaluator is not a stable evaluator besides other humanoid constraints. We present a novel technique for the detection and classification of weld defects by means of geometric features. Firstly noise reduction is done as radiographic images contain noise due to several effects. After this we tend to localize defects with maximum interclass variance and minimum intra class variance. Further we move towards extracting features describing the shape of localized objects in segmented images. Using these shape descriptors (geometric features) we classify the defects by Artificial Neural Network.