计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (3): 214-224.doi: 10.11896/jsjkx.250400009

• 计算机图形学 & 多媒体 • 上一篇    下一篇

计算机视觉在轨道交通中的应用

赵斌贝, 朱力, 赵红礼, 李雨彤   

  1. 北京交通大学自动化与智能学院 北京 100044
  • 收稿日期:2025-04-01 修回日期:2025-07-17 发布日期:2026-03-12
  • 通讯作者: 朱力(lizhu@bjtu.edu.cn)
  • 作者简介:(22211320@bjtu.edu.cn)
  • 基金资助:
    国家重点研发计划(2024YFB3108600)

Computer Vision Applications in Rail Transit Systems

ZHAO Binbei, ZHU Li, ZHAO Hongli, LI Yutong   

  1. School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China
  • Received:2025-04-01 Revised:2025-07-17 Online:2026-03-12
  • About author:ZHAO Binbei,born in 2004,undergra-duate.Her main research interests include computer vision and traffic control.
    ZHU Li,born in 1984,Ph.D,professor,Ph.D supervisor.His main research interests include artificial intelligence,intelligent control and optimization of transportation,and perception and big data of transportation systems.
  • Supported by:
    National Key Research and Development Program of China(2024YFB3108600).

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摘要： 轨道交通系统作为交通网络的骨干,因具有高效、便捷的特点,在现代社会中扮演着至关重要的角色。随着技术的持续进步,计算机视觉技术已成为推动轨道交通系统向更高效和更可靠发展的关键因素。对此,深入探讨了计算机视觉技术在轨道交通领域的研究现状,评估了该技术对提升运输效率和安全性的重要贡献,并分析了在实际应用中遇到的挑战以及可能的改进方向。从车站安全检测、轨道安全检测和车体状态检测这3个应用方向,分析了计算机视觉技术的应用内容以及当前研究的发展方向。最后,对未来发展趋势进行了展望,预测了计算机视觉技术将如何进一步推动轨道交通系统的自动化、智能化,以及在保障数据安全的前提下,为轨道交通领域带来更多创新和突破。

关键词: 计算机视觉, 轨道交通, 特征提取, 目标检测

Abstract: As the backbone of transportation networks,rail transit systems play a pivotal role in modern society due to their high efficiency and operational reliability.With continuous technological advancements,computer vision technologies have emerged as a critical driver for enhancing rail transit systems toward greater efficiency and dependability.This paper comprehensively examines the current research landscape of computer vision applications in rail transit,evaluates their significant contributions to improving transportation efficiency and safety,and analyzes both the challenges encountered in practical implementations and potential improvement strategies.Through systematic analysis of three primary application domains－station security surveillance,track condition monitoring,and rolling stock status assessment－the study elucidates the implementation frameworks of computer vision technologies while identifying current research trajectories.Finally,the paper provides a forward-looking perspective on development trends,predicting how computer vision will further propel automation and intelligentization in rail transit systems.It also anticipates innovative breakthroughs in this field while ensuring data security compliance,ultimately fostering safer and more sustainable urban transportation ecosystems.

Key words: Computer vision, Rail transit, Feature extraction, Object detection

中图分类号: 

  • TP181
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