Research on Target Detection of Foreign Objects Intruding onto Railway Tracks under Complex Weather Conditions

WEI Yutong; HOU Tao; NIU Hongxia

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Research on Target Detection of Foreign Objects Intruding onto Railway Tracks under Complex Weather Conditions

更新时间：2026-04-30

- Research on Target Detection of Foreign Objects Intruding onto Railway Tracks under Complex Weather Conditions
- JOURNAL OF XI’AN JIAOTONG UNIVERSITY (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：
  CLC： TP391.41;U298
- Received：13 March 2026，
  
  Revised：2026-04-30，
  
  Accepted：30 April 2026，
- 稿件说明：
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WEI Yutong, HOU Tao, NIU Hongxia. Research on Target Detection of Foreign Objects Intruding onto Railway Tracks under Complex Weather Conditions[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026.
DOI：

WEI Yutong, HOU Tao, NIU Hongxia. Research on Target Detection of Foreign Objects Intruding onto Railway Tracks under Complex Weather Conditions[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026. 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清晰度低，容易出现误检漏检及参数量大与推理速度慢等问题，提出基于改进YOLOv11n的复杂天气铁路轨道异物入侵检测算法RCW-YOLO。首先，新建雨雾感知动态融合门控，设计雨雾浓度估计器模块，提高检测精度。然后在检测头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，RCW-YOLO的处理结果的平均精度为84.6%，较YOLOv11n提升了9.2%，参数量下降了18.15%。与主流目标检测算法相比，RCW-YOLO的处理结果的平均精度较YOLOv9t、YOLOv10n分别提升了11.3%、11.3%，参数量较YOLOv9t、YOLOv10n分别下降了21.63%、25.34%，相比最新的YOLOv12、YOLOv13，平均精度分别提高了7.8%、7.0%，参数量分别下降了22.18%、19.93%。

Abstract

To ensure the safety of train operation under complex weather conditions

considering the low clarity of target detection for foreign objects invading the railway tracks under such conditions

and the problems of frequent false detections

missed detections

large parameter quantity and slow inference speed

a complex weather railway track foreign object intrusion detection algorithm RCW-YOLO(Railway Complex Weather-YOLO) based on improved YOLOv11n is proposed. Firstly

a rain and fog perception dynamic fusion gating is established

and a rain and fog concentration estimator module is designed to improve the detection accuracy. Then

a rain and fog attention branch is added to the detection head DynamicHead

and the rain and fog concentration coefficient is integrated into the convolution offset to reduce the false detection rate and missed detection rate. Secondly

the attention-enhanced multi-subband fusion wavelet pooling module is used instead of the existing convolution module in the backbone and neck networks to replace the YOLOv11n

maintaining the detection effect while reducing the parameter quantity and improving the inference speed. Finally

the newly designed loss function Edge-Weighted Minimum Point Distance Intersection over Union (EW-MPDIoU) is introduced to replace the original loss function CIOU

improving the detection accuracy. Experimental results show that the mAP@0.5 of the processing results of RCW-YOLO is 84.6%

which is 9.2% higher than that of YOLOv11n

and the parameter quantity is 18.15% lower than that of YOLOv11n. Compared with mainstream object detection algorithms

the mAP@0.5 of the processing results of RCW-YOLO is 11.3% higher than that of YOLOv9t and 11.3% higher than that of YOLOv10n

and the parameter quantity is 21.63% lower than that of YOLOv9t and 25.34% lower than that of YOLOv10n. Compared with the latest YOLOv12 and YOLOv13

the mAP@0.5 is 7.8% higher and the parameter quantity is 22.18% lower.

关键词

Keywords

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