Automated detection of welding defects in pipelines from radiographic images DWDI

Boaretto, Neury; Centeno, Tania Mezzadri

doi:10.1016/j.ndteint.2016.11.003

Cited by 143 publications

(64 citation statements)

References 20 publications

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“…The geometric features and texture features have been the most commonly used for weld defect classification in recent decades [6,34]. The geometric features usually describe the shape, size, location, and intensity information of welding defects, while texture features can provide very useful visual cues commonly used in pattern recognition of image.…”

Section: Feature Extractionmentioning

confidence: 99%

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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%

“…Boaretto carried out the identification of the defects successfully. However, they failed on the attempt of classifying the defects due to the unbalanced data generated by the few samples of each defect type [34]. Liao studied the imbalanced data problem in the classification of different types of weld flaws.…”

Section: Challengesmentioning

confidence: 99%

Review on Computer Aided Weld Defect Detection from Radiography Images

Hou

Zhang

et al. 2020

Applied Sciences

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“…Yin et al [7] proposed a new method to automatically extract four geometric features from Lissajous figures and use machine learning-based classifiers to identify defects. Boaretto et al [8] obtained defect features by the exposure technique of double wall double image (DWDI) and used multi-layer perception (MLP) to continuously classify defect or no-defect. Zapata et al [9] selected the shape and direction of weld defects as classification features, and proposed an adaptive network based fuzzy inference system to detect welding defects.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Classification of Weld Surface Defects

Zhu

Liu

2019

Applied Sciences

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In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.

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“…Therefore, it is very important to continuously control these elements and to detect damage as early as possible. To detect this type of damage, usually nondestructive methods (a visual method [1], a penetration method [2], a magnetic particles method [3], a radiographic method [4], an ultrasonic method [5] and a vibration method [6][7][8][9]) are used. If cracks appear, then the system parameters such as the rigidity, the vibration frequency or damping are subjected to change.…”

Section: Introductionmentioning

confidence: 99%

Application of the vibration method for damage identification of a beam with a variable cross-sectional area

2018

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Abstract. The subject of the paper is an application of the non-destructive vibration method for identifying the location of two cracks occurring in a beam. The vibration method is based on knowledge of a certain number of vibration frequencies of an undamaged element and the knowledge of the same number of vibration frequencies of an element with a defect. The analyzed beam, with a variable cross-sectional area, has been described according to the Bernoulli-Euler theory. To determine the values of free vibration frequencies the analytical solution, with the help of the Green's function method, has been used.

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Automated detection of welding defects in pipelines from radiographic images DWDI

Cited by 143 publications

References 20 publications

Review on Computer Aided Weld Defect Detection from Radiography Images

Review on Computer Aided Weld Defect Detection from Radiography Images

Deep Learning-Based Classification of Weld Surface Defects

Application of the vibration method for damage identification of a beam with a variable cross-sectional area

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