Intrusion of foreign objects into the Electrified Railway Catenary System can lead to power failures, train service interruptions, and even casualties, making accurate detection essential for safe operation. Due to the scarcity of railway datasets, this study constructs a Railway Catenary Foreign Object Dataset to support model training and evaluation. Existing detection methods often struggle with complex railway environments, diverse object morphologies, and varying scales. To address these challenges, we propose a Railway Catenary Foreign Object Detection Network. It leverages the hierarchical architecture and window-based attention mechanism of Swin Transformer for multi-scale semantic feature extraction and global relational modeling, effectively distinguishing foreground from background. A Multi-branch Fusion Feature Pyramid Network is designed to deeply fuse low- and high-level features across scales, improving detection of objects of different sizes. Additionally, a Regional Receptive Field-Enhanced Edge Module expands the receptive field and enhances edge extraction for elongated foreign objects. Extensive experiments on the constructed dataset demonstrate the effectiveness of the proposed approach, achieving an Average Precision of 60.2%, with 53.8% for small object detection.
Li et al. (Sat,) studied this question.
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