基于视觉的列车轨道缺陷检测综述

陈天炎; 韩泽明; 黄允浒; 石金进; 陈德旺

doi:10.11959/j.issn.2096-6652.202427

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基于视觉的列车轨道缺陷检测综述

学术论文 | 更新时间：2024-11-04

- 基于视觉的列车轨道缺陷检测综述
- Survey on vision-based railway track defect detection
- 智能科学与技术学报 2024年6卷第3期页码：367-380
- 作者机构：
  
  1.福建理工大学交通运输学院，福建福州 350118
  2.福建船政交通职业学院轨道交通学院，福建福州 350007
  3.闽江学院计算机与大数据学院，福建福州 350108
- 作者简介：
  
  [ "陈天炎（1981- ），男，福建船政交通职业学院轨道交通学院教授，福建理工大学交通运输学院硕士生导师，主要研究方向为工业机器人、轨道缺陷检测。" ]
  [ "韩泽明（1998- ），男，福建理工大学交通运输学院硕士生，主要研究方向为机器视觉、轨道缺陷检测。" ]
  [ "黄允浒（1990- ），男，闽江学院计算机与大数据学院讲师，主要研究方向为机器学习、模糊系统及智能交通系统。" ]
  [ "石金进（1984- ），男，福建理工大学交通运输学院讲师、硕士生导师，主要研究方向为工业机器人、机器视觉和缺陷检测。" ]
  [ "陈德旺（1976- ），男，福建理工大学交通运输学院教授，俄罗斯自然科学院外籍院士，福州大学计算机与大数据学院博士生导师，福建省“闽江学者”特聘教授，主要研究方向为人工智能算法、模糊系统和智能交通系统。" ]
- 基金信息：
  
  福建省第三批创新之星人才计划项目(003002);福建省交通运输科技项目(2020030);福建船政交通职业学院科教发展基金项目(20220201)
- DOI：10.11959/j.issn.2096-6652.202427
  中图分类号： TP391.4
- 收稿：2024-03-05，
  
  修回：2024-06-15，
  
  纸质出版：2024-09-15
- 稿件说明：
移动端阅览
陈天炎,韩泽明,黄允浒等.基于视觉的列车轨道缺陷检测综述[J].智能科学与技术学报,2024,06(03):367-380.

CHEN Tianyan,HAN Zeming,HUANG Yunhu,et al.Survey on vision-based railway track defect detection[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):367-380.
陈天炎,韩泽明,黄允浒等.基于视觉的列车轨道缺陷检测综述[J].智能科学与技术学报,2024,06(03):367-380. DOI： 10.11959/j.issn.2096-6652.202427.

CHEN Tianyan,HAN Zeming,HUANG Yunhu,et al.Survey on vision-based railway track defect detection[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):367-380. DOI： 10.11959/j.issn.2096-6652.202427.

摘要

列车轨道缺陷检测对轨道交通安全具有重要意义，但当前人工检测已不能满足复杂繁重的轨道巡检要求。深度学习方法极大地扩展了缺陷检测的手段，为了提高表面缺陷检测的效率与质量，结合轨道缺陷的类型与相关属性，系统分析了当前视觉检测方法的变化趋势。详细阐述了轨面及扣件缺陷视觉检测方法，以及与之相关的深度学习方法的基本原理、技术、方法与应用现状，讨论了轨面及扣件缺陷检测方法的理念、应用和意义。最后，对当前研究的轨道缺陷检测领域发展趋势进行分析和总结，首次提出了全自动轨道检修系统的概念，致力于为未来相关研究提供有益的支撑和借鉴。

Abstract

The detection of train track defects is of great significance to the safety of rail transportation

but current manual inspection can no longer meet the complex and heavy track inspection requirements. Deep learning methods have greatly expanded the means and detection capabilities of defect detection

and in order to improve the efficiency and quality of surface defect detection

the current trends of visual detection methods in conjunction with the types and related attributes of track defects was systematically analyzed. The basic principles

technologies

methods

and current application status of visual detection methods for track and fastening defects

as well as the concepts

applications

and significance of these detection methods were elaborated. Finally

the current trends in the field of track defect detection are analyzed and summarized

and for the first time proposes the concept of a fully automatic track maintenance system

which aims to provide useful support and reference for future related research.

关键词

Keywords

references

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