铁路通信信号工程技术2026，Vol.23Issue(6)：24-31,38,9.DOI:10.3969/j.issn.1673-4440.2026.06.004
一种用于轨道交通桥梁的无监督异常检测方法研究
Research on Unsupervised Anomaly Detection Method for Rail Transit Bridges
摘要
Abstract
As bridges constitute critical infrastructure for elevated sections of urban rail transit systems,their operational safety has direct impacts on the reliability of the transportation systems and the life and property safety of the passengers.Unmanned Aerial Vehicles(UAVs)have increasingly become an important means for bridge inspections because of their flexibility and cost-effectiveness.In order to address multiple challenges such as difficulty in the modeling of diverse bridge defects like corrosions and cracks in images,and scarcity of anomaly samples,this paper proposes a lightweight Unsupervised Anomaly Detection Network(UADNet).This approach integrates a teacher-student network with a skip-connection autoencoder,leveraging the complementarity of local and global features to achieve the precise identification of multi-scale defects.The introduction of lightweight convolution effectively reduces the complexity of the proposed model.The experimental results demonstrate that the proposed UADNet achieves superior detection accuracy compared to existing methods on both steel bridge node plate and concrete bridge pier beam datasets.关键词
无人机巡检/轨道交通桥梁/无监督异常检测/教师-学生网络/自编码器Key words
UAV inspection/rail transit bridges/unsupervised anomaly detection/teacher-student networks/autoencoder分类
航空航天引用本文复制引用
桂文标,张宁,赵一夫,叶习兵..一种用于轨道交通桥梁的无监督异常检测方法研究[J].铁路通信信号工程技术,2026,23(6):24-31,38,9.基金项目
台州畅行轨道交通运营管理有限公司科研项目(CXGD-CG-24024) （CXGD-CG-24024）
