TY  - EJOU
AU  - Cui, Jing 
AU  - Qin, Yong 
AU  - Geng, Yixuan 
AU  - Guo, Miao 
AU  - Yang, Xue 
AU  - Shi, Wanyin 

TI  - Unsupervised Anomaly Detection System for High-Speed Railway Noise Barrier Using UAV Imagery
T2  - Structural Durability \& Health Monitoring

PY  - 
VL  - 
IS  - 
SN  - 1930-2991

AB  - 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 pixel-level labeling or localization-network training. Second, a teacher-student-autoencoder framework, denoted UADNet, is designed for NB anomaly detection. In this framework, a GhostConv-based student network learns normal local feature regression, while a skip connection autoencoder (SCAE) models normal global feature consistency. Anomalies are highlighted when both branches fail to reproduce the expected normal responses. Experiments on public anomaly-detection benchmarks and a customized UAV railway NB dataset demonstrate that the proposed system achieves high detection accuracy, accurate localization, and competitive inference speed in complex rail environments.
KW  - Anomaly detection; railway noise barrier; unmanned aerial vehicle (UAV); unsupervised learning

DO  - 10.32604/sdhm.2026.081306