Unsupervised Anomaly Detection System for High-Speed Railway Noise Barrier Using UAV Imagery
Jing Cui1, Yong Qin2,*, Yixuan Geng3, Miao Guo4,*, Xue Yang4, Wanyin Shi5
Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.081306
- 24 August 2026
(This article belongs to the Special Issue: Low-altitude Intelligence Transportation Systems: Perception, Decision-Making, Planning, and Optimization)
Abstract Noise barriers (NBs) play a significant role in reducing railway noise and preventing foreign-object intrusion. However, surface damage, corrosion, rust, missing components, and local deformation may gradually reduce their structural reliability and threaten railway operation safety. Because NB anomalies are diverse and defect samples are limited, it remains difficult to build a general detector using conventional supervised learning. To address this problem, this study proposes an unsupervised anomaly detection system for railway NBs using UAV imagery. First, a color-prior-based NB localization algorithm is developed in the HSV color space to extract NB regions without cumbersome More >