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<p>Efficient and accurate railway track segmentation is vital for autonomous train operation and early obstacle detection. However, blurred boundaries and complex backgrounds pose challenges for traditional instance segmentation models. In order to address the aforementioned issues, a novel instance segmentation framework, SMDE-YOLO, which builds upon YOLO11n-seg and is designed for railway track instance segmentation in complex environments. Considering the segmentation challenges posed by blurred track boundaries, a Scharr-based edge enhancement strategy is incorporated into the data augmentation phase to enhance edge feature expression. The network incorporates the Dynamic Multi-Branch Feature Pyramid Network (DMBFPN) to enhance multi-scale feature modeling and integrates a Multi-Scale Edge Feature Fusion (MSEFF) module to strengthen edge-guided feature aggregation. In order to balance lightweight design and segmentation accuracy, the Dual Enhanced Efficient Decoupled segmentation head (DEED-Seg) is also introduced to reduce computational load. Experimental results show that SMDE-YOLO delivers superior results on the Railsem7750 dataset. Accurate track localization remains vital for intelligent inspection and safety zoning in complex traffic environments.</p>

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Edge-guided multi-scale instance segmentation for railway track

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月24日

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  • Junting Lin,
  • Weikun Yang,
  • Xiaocheng Du

摘要

Efficient and accurate railway track segmentation is vital for autonomous train operation and early obstacle detection. However, blurred boundaries and complex backgrounds pose challenges for traditional instance segmentation models. In order to address the aforementioned issues, a novel instance segmentation framework, SMDE-YOLO, which builds upon YOLO11n-seg and is designed for railway track instance segmentation in complex environments. Considering the segmentation challenges posed by blurred track boundaries, a Scharr-based edge enhancement strategy is incorporated into the data augmentation phase to enhance edge feature expression. The network incorporates the Dynamic Multi-Branch Feature Pyramid Network (DMBFPN) to enhance multi-scale feature modeling and integrates a Multi-Scale Edge Feature Fusion (MSEFF) module to strengthen edge-guided feature aggregation. In order to balance lightweight design and segmentation accuracy, the Dual Enhanced Efficient Decoupled segmentation head (DEED-Seg) is also introduced to reduce computational load. Experimental results show that SMDE-YOLO delivers superior results on the Railsem7750 dataset. Accurate track localization remains vital for intelligent inspection and safety zoning in complex traffic environments.