Research on Object Detection for Foreign Object Intrusion on Railway Tracks under Complex Weather Conditions

WEI Yutong; HOU Tao; NIU Hongxia

doi:10.7652/xjtuxb202610010

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Research on Object Detection for Foreign Object Intrusion on Railway Tracks under Complex Weather Conditions

Special Topic Intelligent Perception and Autonomous Decision-Making | 更新时间：2026-09-29

- Research on Object Detection for Foreign Object Intrusion on Railway Tracks under Complex Weather Conditions
- Journal of Xi'an Jiaotong University Vol. 60, Issue 10, Pages: 113-124(2026)
- 作者机构：
  
  兰州交通大学自动化与电气工程学院，730070，兰州
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202610010
  CLC： TP391.41
- Received：10 March 2026，
  
  Online First：07 May 2026，
  
  Published：10 October 2026
- 稿件说明：
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WEI Yutong, HOU Tao, NIU Hongxia. Research on Object Detection for Foreign Object Intrusion on Railway Tracks under Complex Weather Conditions[J/OL]. Journal of Xi'an Jiaotong University,2026,60 (10):113-124. https://doi.org/10.7652/xjtuxb202610010.
DOI：

WEI Yutong, HOU Tao, NIU Hongxia. Research on Object Detection for Foreign Object Intrusion on Railway Tracks under Complex Weather Conditions[J/OL]. Journal of Xi'an Jiaotong University,2026,60 (10):113-124. https://doi.org/10.7652/xjtuxb202610010. DOI：

摘要

为保障复杂天气下列车运行安全，针对复杂天气下，铁轨异物入侵目标检测算法参数量大、清晰度低、易出现误检漏检及推理速度慢等问题，提出基于改进YOLOv11n算法的复杂天气铁路轨道异物入侵检测算法（RCW-YOLO）。首先，在YOLOv11n算法基础上新增雨雾感知动态融合门控，并设计了雨雾浓度估计器模块；然后，在目标检测头框架DynamicHead中新增雨雾注意力分支，并在卷积偏移量中融入雨雾浓度系数，减少误检率和漏检率；其次，在骨干和颈部网络中使用注意力增强多子带融合小波池化模块替代YOLOv11n现有卷积模块，在保持检测效果的同时减少参数量，提高推理速度；最后，引入新设计的损失函数EW-MPDIoU替代原损失函数CIOU，提高检测精度。实验结果表明：采用所提RCW-YOLO算法的平均检测精度为84.6%，较YOLOv11n算法提升了9.2%，参数量下降了18.15%；与主流目标检测算法YOLOv9t和YOLOv10n相比，采用RCW-YOLO算法的平均检测精度分别提升了11.3%和11.3%，参数量分别下降了21.63%、25.34%；相比最新的YOLOv12和YOLOv13算法，平均检测精度分别提高了7.8%、7.0%，参数量分别下降了22.18%和19.93%。

Abstract

To ensure the safety of train operations under complex weather conditions

and to address the issues of existing object detection algorithms for foreign object intrusion on railway tracks

such as a large number of parameters

poor image clarity

high rates of false and missed detections

and slow inference speed

a foreign object intrusion detection algorithm for railway tracks under complex weather conditions (RCW-YOLO) based on the improved YOLOv11n is proposed. First

based on the YOLOv11n algorithm

a rain-fog perception dynamic fusion gating mechanism is introduced

and a rain-fog density estimator module is designed. Second

a rain-fog attention branch is added to the object detection head framework DynamicHead

and a rain-fog density coefficient is integrated into the convolutional offset to reduce the false and missed detection rates. Third

in the backbone and neck networks

an attention-enhanced multi-subband fusion wavelet pooling module is utilized to replace the existing convolutional modules of YOLOv11n

which reduces the number of parameters and improves inference speed while maintaining detection performance. Finally

a newly designed loss function

EW-MPDIoU

is introduced to replace the original CIoU loss function

thereby improving detection accuracy. Experimental results show that the proposed RCW-YOLO algorithm achieves an average detection accuracy of 84.6%

which is 9.2% higher than that of the YOLOv11n algorithm

while the number of parameters is reduced by 18.15%. Compared with the mainstream object detection algorithms YOLOv9t and YOLOv10n

the average detection accuracy of the RCW-YOLO algorithm is improved by 11.3% and 11.3%

respectively

and the number of parameters is reduced by 21.63% and 25.34%

respectively. Furthermore

compared with the latest YOLOv12and YOLOv13algorithms

the average detection accuracy is improved by 7.8% and 7.0%

respectively

and the number of parameters is reduced by 22.18% and 19.93%

respectively.

关键词

Keywords

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