Feature Fusion for Weld Defect Classification with Small Dataset
Wenhui Hou, Lulu Rao, Andong Zhu, Dashan Zhang
Abstract
Detecting defects from weld radiography images is an important topic in the field of nondestructive testing. Many intelligent detection systems are constructed based on computer. Feature extraction is critical for constructing such system to recognize and classify the weld defects. Deep neural networks have powerful ability to learn representative features that are more sensitive to classification. However, a large number of samples are usually required. In this paper, a stacked autoencoder network is used to pretrain a deep neural network with a small dataset. We can learn the hierarchical feature from the network. In addition, two kinds of traditional manual features are extracted from the same set. These features are combined into new fusion feature vectors for classifying different weld defects. Two evaluation methods are used to test the classification performance of these features through several experiments. The results show that deep feature based on stacked autoencoder network performs better than the other features. The classification performance of fusion features is better than single feature.