Foreign Object Detection Method for Railway Catenary Based on a Scarce Image Generation Model and Lightweight Perception Architecture

Foreign object detection (FOD) in railway catenary systems is crucial for ensuring operational safety and preventing catastrophic failures. However, current detection frameworks encounter two significant challenges. First, the infrequency of fault events leads to severe data scarcity, hampering the training and validation of robust detection models. Second, although lightweight networks (e.g., MobileNet, YOLO) achieve compactness by compressing channel factors, they struggle to balance local feature extraction with global dependency modeling. To address these challenges, we propose a solution that includes the following: 1) RailFOD23, a publicly available dataset created using generative AI to mitigate data scarcity; and 2) EPRepSADet, a compact detection framework that utilizes a re-parameterizable bottleneck (Re-bottleneck) and lightweight self-attention (LSA) module for efficient FOD. The Re-bottleneck consolidates multi-branch structures into a single-path representation, whereas LSA facilitates element-wise attention modeling to effectively reduce computational complexity. In addition, the efficient detection head further minimizes model complexity through hierarchical semantic modeling. Extensive experiments demonstrate that EPRepSADet achieves a mean Average Precision (mAP) of 92.5% on the RailFOD23 test set, requiring only 1.7G FLOPs, thus outperforming several state-of-the-art baseline models.