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.

中文翻译：

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基于转移学习和激活特征的焊接缺陷分类深度学习技术

使用X射线图像检测焊接缺陷是一种无损检测的有效方法。按照惯例，这项工作是基于合格的人类专家进行的，尽管需要他们的个人干预才能进行异质性的提取和分类。已经使用机器学习（ML）和图像处理工具完成了许多方法来解决这些任务。尽管在对比度低和质量差的问题上，检测和分类已得到增强，但其结果仍不令人满意。与以往基于ML的研究不同，本文提出了一种基于深度学习网络的新颖分类方法。在这项工作中 一种基于使用预训练网络AlexNet架构的原始方法，旨在对焊缝缺陷进行分类，并在我们的数据集中增加正确的识别度。迁移学习与预先训练的AlexNet模型一起用作方法。对于深度学习应用程序，需要大量的X射线图像，但是管道焊接缺陷的数据集很少。为此，我们增强了针对两种缺陷的数据集，并使用数据增强（对数据进行随机图像转换（例如平移和反射）进行增强）。最后，应用微调技术对焊接图像进行分类，并将其与深度卷积激活特征（DCFA）和一些预训练的DCNN模型（即VGG-16，VGG-19，ResNet50，ResNet101和GoogLeNet）进行比较。这项工作的主要目的是通过转移学习来探索AlexNet和不同的预训练体系结构对X射线图像分类的能力。我们的模型所实现的准确性已得到全面介绍。使用我们提出的模型在焊缝数据集上获得的实验结果已使用GDXray数据库进行了验证。还将在验证测试集中获得的结果与DCNN模型提供的其他结果进行比较，该模型在较短的时间内显示出最佳性能。这可以看作是我们提出的分类模型的优势的证据。使用我们提出的模型在焊缝数据集上获得的实验结果已使用GDXray数据库进行了验证。还将在验证测试集中获得的结果与DCNN模型提供的其他结果进行比较，该模型在较短的时间内显示出最佳性能。这可以看作是我们提出的分类模型的优势的证据。使用GDXray数据库验证了使用我们提出的模型在焊接数据集上获得的实验结果。还将在验证测试集中获得的结果与DCNN模型提供的其他结果进行比较，该模型在较短的时间内显示出最佳性能。这可以看作是我们提出的分类模型的优势的证据。