基于深度学习的列车运行环境感知关键算法研究综述

doi:10.3785/j.issn.1008-973X.2025.01.001

浙江大学学报(工学版)

2025, Vol. 59

Issue (1): 1-17 DOI: 10.3785/j.issn.1008-973X.2025.01.001

计算机与控制工程

基于深度学习的列车运行环境感知关键算法研究综述

陈智超1,2,3(

),杨杰1,2,3,4,*(

),李凡1,2,冯志成1,2

1. 江西理工大学电气工程与自动化学院，江西赣州 341000
2. 江西理工大学磁浮轨道交通装备江西省重点实验室，江西赣州 341000
3. 上海电机学院电气学院, 上海 201306
4. 国瑞科创稀土功能材料有限公司，江西赣州 341000

Review on deep learning-based key algorithm for train running environment perception

Zhichao CHEN1,2,3(

),Jie YANG1,2,3,4,*(

),Fan LI1,2,Zhicheng FENG1,2

1. School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China
2. Jiangxi Province Key Laboratory of Maglev Rail Transit Equipment, Jiangxi University of Science and Technology, Ganzhou 341000, China
3. School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China
4. Guorui Scientific Innovation Rare Earth Functional Materials Company Limited, Ganzhou 341000, China

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摘要：

阐述深度学习在感知任务中的理论和相关基础，梳理深度学习在视觉、点云处理方面的模型架构及性能. 系统总结基于图像识别的轨道区域提取、接触网异物检测和低照度图像增强等关键算法，归纳现有算法的难点. 针对列车对3D感知的需求，进一步梳理面向铁路场景的点云分割、单目3D检测和多模态融合检测算法，对常见于文献的数据集进行模型性能的对比分析. 总结列车运行环境感知现阶段存在的问题和未来的发展趋势.

关键词： 列车运行环境感知; 深度学习; 图像处理; 三维感知; 多模态融合

Abstract:

The theoretical and related foundations of deep learning were elaborated in perceptual tasks, and the model architectures and performance of deep learning in vision and point cloud processing were combed. The key image recognition-based algorithms for track region extraction, contact network foreign object detection and low-light image enhancement were summarized, and the difficulties of existing algorithms were listed. For the demand for 3D perception of trains, the point cloud segmentation, monocular 3D detection and multimodal fusion detection algorithms for railroad scenes were clarified, and the model performance of datasets widely used in literature was analyzed. The problems and the trends for train running environment perception were outlined.

Key words: train running environment perception deep learning image processing 3D perception multimodal fusion

收稿日期: 2024-03-08 出版日期: 2025-01-18

CLC:

TP 242.6

基金资助: 国家自然科学基金资助项目（62063009）；国家重点研发计划资助项目（2023YFB4302100）；江西省重大科技研发专项资助项目（20232ACE01011）.

通讯作者: 杨杰 E-mail: chenzhichao_ai@163.com;yangjie@jxust.edu.cn

作者简介: 陈智超（1997—），男，博士生，从事列车智能感知与安全防护研究. orcid.org/0000-0002-7150-4914. E-mail：chenzhichao_ai@163.com

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引用本文:

陈智超,杨杰,李凡,冯志成. 基于深度学习的列车运行环境感知关键算法研究综述[J]. 浙江大学学报(工学版), 2025, 59(1): 1-17.

Zhichao CHEN,Jie YANG,Fan LI,Zhicheng FENG. Review on deep learning-based key algorithm for train running environment perception. Journal of ZheJiang University (Engineering Science), 2025, 59(1): 1-17.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.01.001 或 https://www.zjujournals.com/eng/CN/Y2025/V59/I1/1

图 1 文章内容的逻辑关系示意图

表 1 列车运行环境感知任务

图 2 基于编码器-解码器的语义分割架构示意图

图 3 二阶段和单阶段目标检测的架构示意图

图 4 零参考深度曲线估计算法的整体架构[22]

图 5 点云数据处理中的典型数据表示方法

图 6 典型点云分割网络的结构示意图

图 7 体素和柱体的表现形式

图 8 典型相机-激光雷达融合检测网络结构的示意图

表 2 不同融合方法在NuScenes数据集上的性能对比

图 9 轨道沿线环境状态感知的典型案例

图 10 接触网异物入侵典型案例

表 3 异物检测算法的概况

图 11 基于光照曲线参数估计的渐进式低照度图像增强网络

图 12 低照度增强算法处理前后的检测结果

图 13 使用PointNet++的铁路场景分割效果

图 14 FarNet的网络结构[72]

图 15 OSDaR23数据集的数据采集设备[76]

表 4 OSDaR23数据集中BEVFusion的多模态检测实验结果

表 5 不同检测距离下BEVFusion的平均精度均值

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