Automatic classification of weld defects in radiographic images

Shen, Qingming; Gao, Jianmin; Li, Cheng

doi:10.1784/insi.2010.52.3.134

Cited by 22 publications

(10 citation statements)

References 15 publications

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“…Three new geometric features were defined to be added the features for classification [37]. Shen defined four new features: roughness of defect edge, roughness of defect region, skewness and kurtosis, which are closely related to the defect types but cannot be detected by human eyes [38]. The expert vision system proposed by Shafeek was based on the features estimating the shape, orientation and location of the defect [39].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Shen used direct multiclass SVM (DMSVM) to classify the defects through the features defined by themselves. DMSVM yields a direct method for training multiclass predictors instead of constructing the classifier according to the samples to be classified [38]. Silva implemented a study of nonlinear classifier using ANN and proved that the quality of the extracted features is more important than the quantity of the features [36].…”

Section: Classifiermentioning

confidence: 99%

See 1 more Smart Citation

Review on Computer Aided Weld Defect Detection from Radiography Images

Hou

Zhang

et al. 2020

Applied Sciences

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The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of recent studies. The studies of automatic defect inspection are synthetically concluded from three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of traditional defect classification method based on the feature extraction, selection and classifier are summarized. Then the applications of novel models based on learning(especially deep learning) were introduced. Finally, the achievement of automation methods were discussed and the challenges of current technology are presented for future research for both weld quality management and computer science researchers.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Classifiermentioning

confidence: 99%

Review on Computer Aided Weld Defect Detection from Radiography Images

Hou

Zhang

et al. 2020

Applied Sciences

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“…Wang and Guo 2014), potential defects were detected, then support vector machine (SVM) was used to distinguish real defects from the potential ones. SVM was also used to classify weld defects, direct multiclass support vector machine (DMSVM) with higher accuracy and faster computation speed was proposed to classify defects (Shen et al 2010). When there were a large amount of features, the SVM classifier for automatic weld defect classification was combined with PCA by which the number of features were reduced (Mu et al 2013).…”

Section: Figure 2 Workflow Of An Automated Rt Systemmentioning

confidence: 99%

Weld defect classification in radiographic images using unified deep neural network with multi-level features

Yang

Jiang

2020

J Intell Manuf

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“…However, RT images usually have high noise, low contrast and definition, weak edge intensity, and so on, resulting in problems related to the subsequent image processing and automatic inspection system. Moreover, it is difficult to segment defects accurately from low-quality RT images, and they contain a considerable amount of useful information that cannot be retrieved [1][2][3].…”

Section: Introductionmentioning

confidence: 99%