全自动平交铁路智能控制系统的研究

doi:10.3969/j.issn.1673-4440.2025.04.005

铁路通信信号工程技术 ›› 2025, Vol. 22 ›› Issue (4): 32-38.DOI: 10.3969/j.issn.1673-4440.2025.04.005

全自动平交铁路智能控制系统的研究

刘雪梅^1，2，李阳^1，2，杨明春^1，2

1．北京全路通信信号研究设计院集团有限公司，北京 100070；
2．列车自主运行智能控制铁路行业工程研究中心，北京 100070

收稿日期:2024-05-15 修回日期:2025-04-05 出版日期:2025-04-25 发布日期:2025-04-25
作者简介:刘雪梅（1979—），女,工程师，硕士，主要研究方向：障碍物检测，邮箱：liuxuemei@crscd.com.cn
基金资助:
四川省重大科技专项（2022ZDZX0043）；北京全路通信信号研究设计院集团有限公司科研项目（2300-K3240001.01）

Research on Fully Automatic Intelligent Control System for Railway Level Crossings

Liu Xuemei^{1, 2}, Li Yang^{1, 2}, Yang Mingchun^{1, 2}

1. CRSC Research & Design Institute Group Co., Ltd., Beijing 100070, China；
2. Engineering Research Center of Railway Industry of Intelligent and Autonomous Train Control, Beijing 100070, China

Received:2024-05-15 Revised:2025-04-05 Online:2025-04-25 Published:2025-04-25

摘要/Abstract

摘要： 介绍全自动平交铁路智能控制系统研究研发的背景和需求，详细阐述全自动平交铁路智能控制系统的组成，整个系统一共包括4部分，即车载设备、道口设备、监控中心和公网云平台，同时详细阐述本系统的关键技术。全自动平交铁路智能控制系统解决了地方铁路、厂矿干线等既有铁路人工成本高、人员短缺、自动化水平低等问题。

关键词: 平交铁路, 全自动, 智能控制, 图像检测, 云平台, 深度学习

Abstract: This paper introduces the background and demand for the research and development of a fully automatic intelligent control system for railway level crossings, and elaborates on the composition of this intelligent control system. The whole system consists of four parts: onboard equipment, level crossing equipment, monitoring center and public network cloud platform. The key technologies of the system are also expounded. This fully automatic intelligent control system solves the problems of high labor costs, shortage of personnel and the low automation level of existing railways, such as local railways and the mainlines of factories and mines.

Key words: evel crossing, fully automatic, intelligent control, image detection, cloud platform, deep learning

中图分类号:

U284.48

刘雪梅, 李阳, 杨明春. 全自动平交铁路智能控制系统的研究[J]. 铁路通信信号工程技术, 2025, 22(4): 32-38.

Liu Xuemei, Li Yang, Yang Mingchun. Research on Fully Automatic Intelligent Control System for Railway Level Crossings[J]. Railway Signalling & Communication Engineering, 2025, 22(4): 32-38.

图/表 8

参考文献

[1] Zhang B Q, Wang S, Liu Y F, et al. Object Detection Difficulty:Suppressing Over-Aggregation for Faster and Better Video Object Detection[C]//Proceedings of the 31st ACM International Conference on Multimedia. Ottawa ON Canada: ACM, 2023: 1768-1778.
[2] Dewangan D K, Sahu S P. Deep Learning-Based Speed Bump Detection Model for Intelligent Vehicle System Using Raspberry Pi[J]. IEEE Sensors Journal, 2021, 21(3): 3570-3578.
[3]李冠，庞玉琳，田坤.基于YOLO和ConvLSTM混合神经网络的暴力视频检测[J].计算机应用与软件，2023，40（11）：233-240.
Li Guan, Pang Yulin, Tian Kun. Violent Video Detection Based on Yolo and Convlstm Hybrtd Neural Network[J]. Computer Applications and Software, 2023, 40(11): 233-240.
[4] Bondesan R, Welling M. The Hintons in Your Neural Network:a Quantum Field Theory View of Deep Learning[EB/OL]. （2021-03-08）[2024-05-15]. Https://arxiv.org/abs/2103.04913v1.
[5] He D Q, Zou Z H, Chen Y J, et al. Rail Transit Obstacle Detection Based on Improved CNN[J/OL]. IEEE Transactions on Instrumentation and Measurement, 2021, 70[2019-09-21]. https://ieeexplore.ieee.org/document/9552593.
[6] Huang X P, Huang F, Hu J H, et al. Automatic Face Detection of Farm Images Based on an Enhanced Lightweight Deep Learning Model[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2024, 38(12): 2456009.
[7] Yahya Saleh A, Ka Chin C, Ameera Rosdi R. Transfer Learning for Lung Nodules Classification with CNN and Random Forest[J/OL]. Pertanika Journal of Science and Technology, 2024, 32(1)[2024-01-15]. https://doi.org/10.47836/pjst.32.1.25.
[8] Xu F Y, Wang Z L, Wang H Q, et al. Dynamic Vehicle Pose Estimation and Tracking Based on Motion Feedback for LiDARs[J/OL]. Applied Intelligence, 2023, 53: 2362-2390[2022-05-07]. https://doi.org/10.1007/s10489-022-03576-3.
[9] Du B G, Guo J W, Guo J, et al. A Rapid Oriented Detection Method of Virtual Components for Augmented Assembly[J/OL]. Expert Systems with Applications,2024[2024]. http://www.keyanzhidian.com/doc/detail?id=2075458442.
[10] Cong R X, Sheng H, Zhao M Y, et al. Multimodal Perception Integrating Point Cloud and Light Field for Ship Autonomous Driving[J]. IEEE Transactions on Intelligent Transportation Systems, 25(9): 12477-12489.
[11] Li J, Yang R G, Zhao Y L, et al. RSMNet:a Robust Stacked Multiscale Feature Fusion Network for Visible RS Images[J/OL]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 5509605[2023-09-14]. https://ieeexplore.ieee.org/document/10251423.
[12] Moghimi M K, Mohanna F. Reliable Object Recognition Using Deep Transfer Learning for Marine Transportation Systems with Underwater Surveillance[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(2): 2515-2524.
[13] Song T J, Jeong J, Kim J H. End-to-End Real-Time Obstacle Detection Network for Safe Self-Driving via Multi-Task Learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 16318-16329.
[14]张清蓉，陈龙灿，刘庆.视频前景区域运动目标姿态识别仿真[J].计算机仿真，2024，41（7）：258-262.
Zhang Qingrong, Chen Longcan, Liu Qing. Simulation of Moving Target Attitude Recognition in Video Foreground Area[J]. Computer Simulation, 2024, 41(7): 258-262.
[15]董玉圻.基于神经网络的铁路通信网络传输延迟控制方法[J].铁路通信信号工程技术，2023，20（1）：33-39.
Dong Yuqi. Transmission Delay Control Method of Railway Communication Networks Based on Neural Network[J]. Railway Signalling & Communication Engineering, 2023, 20(1): 33-39.
[16]路远.中低速磁浮车辆段闯红灯防护方案[J].铁路通信信号工程技术，2024，21（1）：83-86.
Lu Yuan. Red Light Running Protection Scheme of Medium and Low Speed Maglev Depot[J]. Railway Signalling & Communication Engineering, 2024, 21(1): 83-86.
[17]张东涛.重载铁路列车障碍物检测研究[J].铁路通信信号工程技术，2024，21（4）：28-33，39.
Zhang Dongtao. Research on Obstacle Detection for Heavy-Haul Train[J]. Railway Signalling & Communication Engineering, 2024, 21(4): 28-33, 39.
[18]周欢.城轨信号系统智能运维信息采集方案[J].铁路通信信号工程技术，2023，20（2）：73-78.
Zhou Huan. Research on Information Acquisition Scheme of Intelligent Operation and Maintenance for Urban Rail Signaling System[J]. Railway Signalling & Communication Engineering, 2023, 20(2): 73-78.
[19]杨光，罗雨欣，万琛.高铁列车乘务作业视频YOLOv4算法分析应用研究[J].铁道运输与经济，2024，46（4）：101-108，118.
Yang Guang, Luo Yuxin, Wan Chen. Application Research on YOLOv4 Algorithm in Analysis of High Speed Train Crew Operation Videos[J]. Railway Transport and Economy, 2024, 46(4): 101-108, 118.
[20] Chen L, Wu P H, Chitta K, et al. End-to-End Autonomous Driving: Challenges and Frontiers[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12): 10164 - 10183.
[21] Chen X S, Shi S S, Zhu B J, et al. MPPNet:Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection[M]//Computer Vision - ECCV 2022. Cham: Springer Nature Switzerland, 2022: 680-697.
[22] Mekhalfi M L, Nicolò C, Bazi Y, et al. Contrasting YOLOv5,Transformer,and EfficientDet Detectors for Crop Circle Detection in Desert[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 3003205.

全自动平交铁路智能控制系统的研究

Research on Fully Automatic Intelligent Control System for Railway Level Crossings

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