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

Deep neural network (DNN) exhibits state-of-the-art performance in many fields including weld defect classification. However, there is still a large room for improving the classification performance over the generic DNN models. In this paper, a unified deep neural network with multi-level features is proposed for weld defect classification. Firstly, we define 11 weld defect features as inputs of our proposed classification model. Not limited to geometric and intensity features, 4 features based on the intensity contrast between weld defect and its background are proposed in this paper. Secondly, we construct a novel deep learning framework: a unified deep neural network, where multi-level features of each hidden layer are fused by the last hidden layer to predict the type of weld defect comprehensively. In addition, we investigate pre-training and fine-turning strategies to get better generalization performance with small dataset. Comparing with other classification methods like SVM and generic DNN model, our framework takes full advantage of multi-level features extracted from each hidden layer, an outstanding performance is shown where the classification accuracy is improved by 3.18% and 4.33% on the test dataset, to reach 91.36%.

Automated summary

A unified deep neural network with multi-level features is proposed for weld defect classification, achieving an improved accuracy of 3.18% and 4.33% on the test dataset compared to other methods. The study also investigates pre-training and fine-turning strategies for better generalization performance with small datasets.