Abstract
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.
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Data availability
The constructed Railway Catenary Foreign Object Dataset is available on GitHub at https://github.com/dashgfuidng/CPSSFOdataset.git
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Acknowledgements
Thanks for the hard work of the editors and the constructive suggestions of the anonymous reviewers.
Funding
This research was supported in part by the National Natural Science Foundation of China (No.62276042), by the Artificial Intelligence Scientific and Technological Innovation Program of Liaoning Province (No.2023JH26/10100008), and by the Science and Technology Plan Joint Program of Liaoning Province (No.2025-MSLH-138, No.2025 BSLH-100, No.2025-BSLH-101).
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Li contributed to the conceptualization, methodology, project administration, and writing—original draft. Cao was responsible for investigation, methodology, software, visualization, and writing—original draft. Yang contributed to data curation, software, and visualization. Diao performed formal analysis, investigation, and validation. Wang contributed to formal analysis, resources, and validation. Yu was responsible for data curation, resources, and writing—review & editing. Yan contributed to data curation and writing—review & editing. Xu provided funding acquisition, supervision, and writing—review & editing. All authors reviewed the manuscript.
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Li, F., Cao, J., Yang, H. et al. A foreign object detection dataset and network for electrified railway catenary systems. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39129-8
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DOI: https://doi.org/10.1038/s41598-026-39129-8
