基于图像识别技术的轨道交通缺陷检测研究
    点此下载全文
引用本文:马 茜.基于图像识别技术的轨道交通缺陷检测研究[J].计算技术与自动化,2022,(1):117-122
摘要点击次数: 167
全文下载次数: 0
作者单位
马 茜 (西咸新区轨道交通发展有限公司陕西 西安 710000) 
中文摘要:传统人工巡检轨道交通缺陷存在效率低、误差大等缺点,运用综合轨道检查车(CTIV)为检测平台,建立了一种车载式轨道交通图像识别智能巡检系统。利用CTIV采集连续轨道边界框图像建立数据集,在C++中使用应用程序开发框架(QT)设计了可视化数据标注的定制软件工具。通过全卷积网络(FCN)建立了多任务学习扩展网络架构,结合多个检测器可以提高轨枕和轨道弹力紧固件的缺陷检测性能。实验结果表明,提出方法在检测轨枕和紧固件缺陷时具有较高的精度。
中文关键词:轨道交通  图像识别  全卷积网络  轨枕  紧固件
 
Research on Rail Transit Defect Detection Based on Image Recognition Technology
Abstract:The traditional manual inspection of rail transit defects has the disadvantages of low efficiency and large error. A vehicle mounted rail transit image recognition intelligent inspection system is established by using the comprehensive rail transit inspection vehicle (CTIV) as the detection platform. CTIV is used to collect the image of continuous track boundary frame to establish data set, and QT is used in C++ to design a customized software tool for visual data annotation. The multi task learning extended network architecture is established by full convolution network (FCN), and the defect detection performance of sleeper and track elastic fastener can be improved by combining multiple detectors. The experimental results show that this method has high accuracy in the detection of sleeper and fastener defects.
keywords:rail transit  image recognition  full convolution network  sleeper  fastener
查看全文   查看/发表评论   下载pdf阅读器