An inception transformer-based weighted prototype network for few-shot defect recognition of wheelset bearing

Abstract The low incidence of failures and high randomness in high-speed train wheelset bearings pose significant challenges in identifying bearing defects under few-shot sample conditions. An inception transformer (IFormer)-based weighted prototype network is proposed for few-shot recognition of wheelset bearing defect images. To capture subtle differences in few-shot samples, an IFormer network integrating the strengths of convolutional neural networks (CNNs) and transformers is adopted in the prototype representation space. A multi-path fusion attention mechanism (MPAM)-based weighting prototype block is introduced to assign weights to features of same-class samples, thus enhancing the representation of target class prototypes. By integrating the modified cost function (MCF), the proposed model can more accurately evaluate the similarity between query samples and class prototypes. Extensive experiments on a public steel plates surface defects data set and the self-constructed train wheelset bearing defect (TWBD) data set demonstrate the robustness of the proposed model compared to other state-of-the-art few-shot learning models. Furthermore, the effectiveness of the proposed model has been validated through a series of ablation experiments and visualization analyses. The proposed approach shows potential as a tool to facilitate intelligent recognition of train wheelset bearing images under few-shot sample conditions.