Flaws classification using ANN for radiographic weld images

Kumar, Jayendra; Anand, R. S.; Srivastava, S. P.

doi:10.1109/spin.2014.6776938

Cited by 25 publications

(18 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, they combined the geometric features and texture features for classification, and compared the performance of classifiers with different feature sets. The results show that the classifier with the combined features perform better than with only geometric features or only texture features [12].…”

Section: Feature Extractionmentioning

confidence: 94%

“…Strang used a wavelet filter to transform the image in order to restrain the noise with a simple threshold operation [8]. The median filter and adaptive Wiener filter were applied successfully for removing the noise from images in many literatures [9][10][11][12][13]. Median filter is a nonlinear, low-pass filter.…”

Section: Noise Removalmentioning

confidence: 99%

“…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]. In the last ten years, ANN has been widely used in the classification of welding defects [6,12,49]. Vilar applied ANN to classify the defect candidates.…”

Section: Classifiermentioning

confidence: 99%

See 2 more Smart Citations

Review on Computer Aided Weld Defect Detection from Radiography Images

Hou

Zhang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Feature Extractionmentioning

confidence: 94%

Section: Noise Removalmentioning

confidence: 99%

See 1 more Smart Citation

Review on Computer Aided Weld Defect Detection from Radiography Images

Hou

Zhang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…An overview of studies using supervised machine learning algorithms for the classification step is presented in Table 9. According to this table, various works are based on ANN (artificial neural networks) [40][41][42] and fuzzy logic systems [43]. Moreover, support vector machines have been utilized [44].…”

Section: Comparative Performance Using the Gdxray Imagesmentioning

confidence: 99%

Deep Learning Technology for Weld Defects Classification Based on Transfer Learning and Activation Features

Ajmi

Zapata

Zaafouri

et al. 2020

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

Weld defects detection using X-ray images is an effective method of nondestructive testing. Conventionally, this work is based on qualified human experts, although it requires their personal intervention for the extraction and classification of heterogeneity. Many approaches have been done using machine learning (ML) and image processing tools to solve those tasks. Although the detection and classification have been enhanced with regard to the problems of low contrast and poor quality, their result is still unsatisfying. Unlike the previous research based on ML, this paper proposes a novel classification method based on deep learning network. In this work, an original approach based on the use of the pretrained network AlexNet architecture aims at the classification of the shortcomings of welds and the increase of the correct recognition in our dataset. Transfer learning is used as methodology with the pretrained AlexNet model. For deep learning applications, a large amount of X-ray images is required, but there are few datasets of pipeline welding defects. For this, we have enhanced our dataset focusing on two types of defects and augmented using data augmentation (random image transformations over data such as translation and reflection). Finally, a fine-tuning technique is applied to classify the welding images and is compared to the deep convolutional activation features (DCFA) and several pretrained DCNN models, namely, VGG-16, VGG-19, ResNet50, ResNet101, and GoogLeNet. The main objective of this work is to explore the capacity of AlexNet and different pretrained architecture with transfer learning for the classification of X-ray images. The accuracy achieved with our model is thoroughly presented. The experimental results obtained on the weld dataset with our proposed model are validated using GDXray database. The results obtained also in the validation test set are compared to the others offered by DCNN models, which show a best performance in less time. This can be seen as evidence of the strength of our proposed classification model.

show abstract

“…The classification of flaws in radiographic weld images using ANN is discussed in [9]. Initially, wiener filter is used in the preprocessing stage to remove noises.…”

Section: Introductionmentioning

confidence: 99%

An Industrial Inspection Approach for Weld Defects Using Machine Learning Algorithm

Selvi¹,

D²

2019

International Journal of Advances in Signal and Image Sciences

View full text Add to dashboard Cite

The weld defects are formed due to the incorrect welding patterns or wrong welding process. The defects in the weld may vary from size, shape and their projected quality. The most common weld defects occur during welding process is slag inclusions, porosity, lack of fusion and incomplete penetration. In this study, an effective method for weld defect classification using machine learning algorithm is presented. The system uses Speeded-up Robust Features (SURF) for feature extraction and one of the machine learning algorithms called Auto-Encoder Classifier (AEC) for classification. Initially, the features that distinguish weld defects and no defects in the weld image are extracted by SURF. Then, AEC is analyzed for weld image classification using different number of neurons in different hidden layers (2 and 3 hidden layers). The performance of the system is evaluated by GD X-ray weld image database. The results show that the weld images are correctly classified with 98% accuracy using SURF and AEC.

show abstract

Flaws classification using ANN for radiographic weld images

Cited by 25 publications

References 15 publications

Review on Computer Aided Weld Defect Detection from Radiography Images

Review on Computer Aided Weld Defect Detection from Radiography Images

Deep Learning Technology for Weld Defects Classification Based on Transfer Learning and Activation Features

An Industrial Inspection Approach for Weld Defects Using Machine Learning Algorithm

Contact Info

Product

Resources

About