New procedure for weld defect detection based-Gabor filter

Ajmi, Chiraz; Ferchichi, Sabra El; Laabidi, Kaouther

doi:10.1109/aset.2018.8379826

Cited by 9 publications

(7 citation statements)

References 12 publications

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“…e validation classification accurately reaches as high as 0.65 when the iteration is 1, while it increases to 1 when the iteration is 80. e effect of fine-tuning layers of the network, according to Table 2, is shown in the form of AlexNet6-8 where the network will be fine tuned from layer 6 to layer8 while the previous layers are kept constant with no update. In addition, we stated that the generation of dataset can give a high-quality image by enhancing the quality and eliminating the portion of periodic noise in it [18], which is one of the main supporting successes of the proposed algorithm. Last, it is required to evaluate trained network.…”

Section: Layers' Fine-tuning Experimental Results For Classificationmentioning

confidence: 79%

“…According to the statistics on the welding defects dataset, the defect images variable resolution is ordered between 640 × 480 and 720 × 576 pixels. So, we implemented an algorithm with MATLAB for cropping and rescaling the original image to new images; after that, we applied preprocessing methods of noise removal with Wiener and Gaussian filter and contrast enhancement with stretching as is mentioned in a previous work [18]. An example of generated images is presented in Figure 5.…”

Section: Dataset Processing and Training Methodmentioning

confidence: 99%

“…e mini batch size was taken as 16, the maximum number of epochs was 10, and the learning rate was 0.001 and in general the other parameters were taken to be the same as the proposed structure and the details of each network are replaced as described in subsection 6.1. e ranking performance of each CNN is summarized in Table 5 which illustrates performance comparison for all pretrained models using all chosen performance metrics. e tabular results show that the pretrained AlexNet model reaches the best results followed by VGG- [16][17][18][19] e performance measures presented in Table 5 indicate that the DCNNs models VGG-16, VGG-19, and GoogLeNet proved to be stellar by attaining 95%, 97.8%, and 99.3% accuracy. Moreover, ResNet50 and ResNet101 have accomplished 100% but with more computation time compared to our proposed model.…”

Section: Methodology Of Transfer Learning In Dnns Modelsmentioning

confidence: 99%

“…ere are prominent results for many applications as the visual tasks [20]; also all fields of weld defects detection [15][16][17][18]30] are well investigated. AlexNet [21] is a developed CNN [20] for ImageNet 2012 and the challenge of large-scale visual recognition (ILSVRC-2012).…”

Section: Overview Of Cnn Structure and Detailed Networkmentioning

confidence: 99%

“…A research on CNN's basic principles related to tests series has made it possible to deduce the utility of this technique (transfer learning), to recognize the steps taken to adapt to a network, and to progress its integration in the welding fields. Our goal is the classification of defects in X-ray images; in a previous work [18], we followed preprocessing, extraction of ROI, and segmentation steps. Now, we can get away from these last steps and go directly to the classification with pretrained CNN to classify the defects.…”

mentioning

confidence: 99%

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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

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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.

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Section: Layers' Fine-tuning Experimental Results For Classificationmentioning

confidence: 79%

Section: Dataset Processing and Training Methodmentioning

confidence: 99%