多尺度特征融合的铁轨异物入侵检测研究

王楠; 侯涛; 牛宏侠

doi:10.7652/xjtuxb202409014

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多尺度特征融合的铁轨异物入侵检测研究

—————————————————————— | 更新时间：2025-07-28

- 多尺度特征融合的铁轨异物入侵检测研究
- Research on Railway Track Foreign Object Intrusion Detection Based on Multi-Scale Feature Fusion
- 西安交通大学学报 2024年58卷第9期页码：139-153
- 作者机构：
  
  兰州交通大学自动化与电气工程学院,兰州,730070
- 作者简介：
  
  : 2023-12-12。作者简介: 王楠(1997—), 女, 硕士生
  侯涛(通信作者), 男, 教授, 硕士生导师。基金项目: 甘肃省重点研发计划-工业类资助项目(23YFGA0049)
- 基金信息：
  
  : 2023-12-12。作者简介: 王楠(1997—), 女, 硕士生;侯涛(通信作者), 男, 教授, 硕士生导师。基金项目: 甘肃省重点研发计划-工业类资助项目(23YFGA0049);甘肃省自然科学基金资助项目(21JR7RA321;22JR5RA358)
- DOI：10.7652/xjtuxb202409014
  中图分类号： U298;TP391.41
- 纸质出版：2024
- 稿件说明：
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王楠, 侯涛, 牛宏侠. 多尺度特征融合的铁轨异物入侵检测研究[J]. 西安交通大学学报, 2024,58(9):139-153.

WANG Nan, HOU Tao, NIU Hongxia. Research on Railway Track Foreign Object Intrusion Detection Based on Multi-Scale Feature Fusion[J]. 2024, 58(9): 139-153.
王楠, 侯涛, 牛宏侠. 多尺度特征融合的铁轨异物入侵检测研究[J]. 西安交通大学学报, 2024,58(9):139-153. DOI： 10.7652/xjtuxb202409014.

WANG Nan, HOU Tao, NIU Hongxia. Research on Railway Track Foreign Object Intrusion Detection Based on Multi-Scale Feature Fusion[J]. 2024, 58(9): 139-153. DOI： 10.7652/xjtuxb202409014.

摘要

针对铁路轨道异物检测中不同尺度异物目标的检测易受复杂环境的影响

导致出现检测精度低及检测速度慢等问题

提出一种多尺度特征融合的铁轨异物检测(RMF-YOLO)算法。首先

设计并引入改进的卷积注意力模块(ICBAM)

结合YOLOv7特征提取网络

以增强复杂场景下的特征提取能力。其次

在所有高效层聚合网络模块中采用GhostConv替代普通卷积层

以降低计算复杂度

提高特征输出效率; 设计一种改进的加权双向特征金字塔网络N-BiFPN结构

加强多尺度特征融合能力

平衡不同层级特征信息

提高多尺度检测能力。最后

为进一步提升检测精度

采用WIoU损失函数结合动态非单调聚集机制

有效应对低质量锚框产生的梯度

提高模型对不同尺度异物检测的整体性能。实验结果表明:在自制的铁轨异物数据集上

RMF-YOLO算法减少了原网络模型的参数量

有效提升了模型的检测精度与检测速度

改善了漏检与误检问题

平均精度提升了5.5%

检测速度提升了5.88%

计算量减少了12.25%

能满足铁轨入侵异物检测中对检测精度和实时性的需求。

Abstract

To address the issues of low detection accuracy and slow detection speed caused by the influence of complex environments on detecting foreign objects of different scales on railroad tracks

this paper proposes a multi-scale feature fusion RMF-YOLO(railway multi-scale fusion YOLO)method for railroad track foreign object detection. Firstly

ICBAM

an improved convolutional attention module

is designed and introduced in combination with the YOLOv7 feature extraction network to enhance feature extraction capability in complex scenarios. Subsequently

GhostConv is adopted in all efficient layer aggregation network modules instead of regular convolutional layers to reduce computational complexity and enhance feature output efficiency.An improved weighted bi-directional feature pyramid network(N-BiFPN)structure is introduced to enhance multi-scale feature fusion

balance feature information across different levels

and improve multi-scale detection capabilities. Lastly

to further enhance detection accuracy

the WIoU loss function combined with the dynamic non-monotonic aggregation mechanism is used to effectively deal with the gradient generated by the low-quality anchor frames and to improve the overall performance of the model for the detection of foreign objects at different scales. The experimental results indicate that on the self-made railway track foreign object dataset

the RMF-YOLO algorithm reduces the parameter count of the original network model

effectively enhancing the model's detection accuracy and detection speed of the model and addressing issues related to missed and false detections. On average

the precision increased by 5.5%

the frames per second(FPS)improved by 5.88%

and the computational load decreased by 12.25%. These enhancements meet the requirements for both detection accuracy and real-time performance in railway track foreign object detection.

关键词

Keywords

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