Basic-Class and Cross-Class Hybrid Feature Learning for Class-Imbalanced Weld Defect Recognition

Abstract

Class-imbalanced weld defect recognition, which realizes defect recognition via learning features of class-imbalanced X-ray images, is an emerging but challenging task. Nevertheless, the existing studies on the class-imbalanced problem mainly focus on large-scale data, and it is difficult to extract high-quality features from the insufficient industrial data, resulting in weak recognition performance. To address the above issue, this paper comprehensively learns features from the perspective of basic-class and cross-class, on this basis, a novel hybrid feature learning model for class-imbalanced weld defect recognition is proposed. First, an image acquisition method completed by photographing, scanning and sampling is designed to collect the class-imbalanced X-ray images. Second, a hybrid feature learning model is proposed to learn the distinctive and effective features from acquired images, so that the class-imbalanced data is mapped to a balanced feature distribution. Third, with the distinguishable features learned by our hybrid feature learning model, an unbiased defect recognition model can be trained to recognize different types of defects. The practical weld data W-PPLN, W-MTL and W-GDXray are adopted in the experiments, and the experimental results show that our method outperforms the state-of-the-art (SOTA) methods on the task of class-imbalanced weld defect recognition.