摘要
随着我国高铁的开通里程增加、运营强度加大,在运维天窗期有限的条件下利用车载视频对接触网关键设施的服役状态开展常态化智能巡检是亟待解决的重要需求问题。文章以C3系统研制过程为依托,梳理了接触网视频巡检任务的全场景、高透视、微目标、弱异变和少样本的特殊性,指出了专家知识和典型案例对数据模型的强化作用,并以吊弦为例,以吊弦故障样本完备性、缺陷识别准确性和数据筛选高速性为研究对象,提出了一种基于知识模型的接触网缺陷智能视觉辨识方法。该方法基于专家经验和典型案例进行吊弦缺陷数值无偏仿真,基于多尺度视觉注意力进行吊弦微小目标异常识别并基于森林参数进行状态快速筛选。现场实验数据表明,采用文中所提的视觉辨识方法识别接触网故障,至少可提升3%识别精度。
As high speed rails increase in mileage and operational intensity,the use of onboard video to conduct normalized and intelligent inspections of the service status of key catenary facilities under the condition of limited operation and maintenance skylights is an important demand problem that needs to be solved urgently.Based on the development process of the C3 system,this paper sorts out the specificity of catenary video inspection task such as full scene,high perspective,micro target,weak change and few samples,and points out the strengthening effect of expert knowledge and typical cases on data model.Considering the completeness of hanger fault samples,the accuracy of defect recognition and the high speed of data screening as the research objects,an intelligent visual identification method for catenary defects based on knowledge model is proposed.In the proposed method,numerical unbiased simulation of hanger defects is performed based on expert experience and typical cases,abnormality recognition of hanger is achieved based on multi-scale visual attention,and rapid state screening is carried out based on forest parameters.Field experimental data shows that the use of the proposed visual identification method can increase the identification accuracy by at least 3%.
作者
唐鹏
金炜东
张兴斌
张志军
邢铠鹏
霍志浩
TANG Peng;JIN Weidong;ZHANG Xingbin;ZHANG Zhijun;XING Kaipeng;HUO Zhihao(School of Electrical Engineering,Southwest Jiaotong University,Chengdu,Sichuan 611756,China)
出处
《控制与信息技术》
2021年第6期84-90,共7页
CONTROL AND INFORMATION TECHNOLOGY
基金
国家重点研发计划(2016YFB1200401-102F)。
关键词
接触网系统
视觉辨识
吊弦缺陷
小目标检测
C3系统
在线仿真
高速甄别
catenary system
visual perception
hanger anomaly
tiny target detection
C3 system
online simulation
high-speed screening
