Abstract: To solve the problems of low efficiency and safety hazards in traditional manual inspection of rail transit lines, this paper proposed a rail fastener defect detection method based on Transformer and local feature fusion. The paper constructed a defect detection model for rail fasteners, integrated Transformer with local feature modules, integrated local information, and extracted defect features of rail fasteners, at the same time, used data augmentation methods to expand the dataset of rail fastener defect samples and verify the detection effect of the constructed model. The experimental results show that compared to traditional methods, the proposed method has improved accuracy and average accuracy in identifying two types of defects, namely missed and damaged track fasteners. It also shows good detection performance in different track experimental environments.