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A foreign object detection dataset and network for electrified railway catenary systems.

Fengqi Li1, Jinhao Cao1, Haolin Yang1

  • 1School of Railway Intelligent Engineering, Dalian Jiaotong University, Dalian, 116028, China.

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Summary

Accurate detection of foreign objects in railway catenary systems is crucial for safety. This study introduces a novel detection network that significantly improves the identification of these objects, even small ones, in complex environments.

Keywords:
DatasetDilated convolutionFeature fusionForeign object detectionRailway catenarySwin transformer

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Area of Science:

  • Computer Vision
  • Railway Engineering
  • Artificial Intelligence

Background:

  • Foreign object intrusion in electrified railway catenary systems poses significant safety risks, including power failures and operational disruptions.
  • Existing foreign object detection methods face challenges in complex railway environments, diverse object shapes, and varying object scales.
  • The scarcity of dedicated railway datasets hinders the development and evaluation of robust detection models.

Purpose of the Study:

  • To develop and validate an effective deep learning model for detecting foreign objects in electrified railway catenary systems.
  • To address the limitations of current methods in handling complex environments and diverse object characteristics.
  • To contribute a new dataset for training and evaluating railway foreign object detection models.

Main Methods:

  • Construction of a novel Railway Catenary Foreign Object Dataset.
  • Proposal of a Railway Catenary Foreign Object Detection Network utilizing Swin Transformer for multi-scale feature extraction and global relation modeling.
  • Implementation of a Multi-branch Fusion Feature Pyramid Network for enhanced feature fusion across scales.
  • Integration of a Regional Receptive Field-Enhanced Edge Module to improve detection of elongated objects.

Main Results:

  • The proposed network achieved an Average Precision (AP) of 60.2% on the constructed dataset.
  • Small object detection performance was notably improved, reaching an AP of 53.8%.
  • The model demonstrated effectiveness in distinguishing foreground objects from the background in complex railway scenes.

Conclusions:

  • The developed Railway Catenary Foreign Object Detection Network effectively addresses the challenges of detecting foreign objects in complex railway environments.
  • The proposed methods, including the Swin Transformer and feature fusion techniques, significantly enhance detection accuracy, particularly for small and elongated objects.
  • The creation of the Railway Catenary Foreign Object Dataset provides a valuable resource for future research in this critical area of railway safety.