基于视觉的列车轨道缺陷检测综述OACSTPCD
Survey on vision-based railway track defect detection
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.
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 summa-rized,and for the first time proposes the concept of a fully automatic track maintenance system,which aims to provide use-ful support and reference for future related research.
陈天炎;韩泽明;黄允浒;石金进;陈德旺
福建理工大学交通运输学院,福建 福州 350118||福建船政交通职业学院轨道交通学院,福建 福州 350007福建理工大学交通运输学院,福建 福州 350118闽江学院计算机与大数据学院,福建 福州 350108
计算机与自动化
轨道交通安全轨道缺陷图像识别深度学习
rail transportation safetyrail detectimage recognitiondeep learning
《智能科学与技术学报》 2024 (003)
367-380 / 14
福建省第三批创新之星人才计划项目(No.003002);福建省交通运输科技项目(No.2020030);福建船政交通职业学院科教发展基金项目(No.20220201) The Third Batch of Innovation Star Talent Program of Fujian Province(No.003002),Fujian Provincial Transpor-tation Science and Technology Project(No.2020030),Science and Education Development Fund of Fujian Shipbuilding and Trans-portation Vocational College(No.20220201)
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