Network-Based Rail Running Band Anomaly Recognition via Recurrent Attention Graphs

Anomaly detection for rail running bands, the pattern of wheel–rail contact area, is crucial to analyze composite rail irregularities. This paper presents an all-weather vision-based solution for running-band inspection. However, two major algorithmic challenges restrict inspection effectiveness: 1) the identification of high-quality features for diversified and imbalanced data subject to noise and outliers, and 2) inferring implicit anomaly co-occurrence patterns. We regard overall running-band anomaly detection as a multi-label classification problem and develop a novel deep multi-anomaly recognition network via recurrent attention graphs (RAGRN). The proposed RAGRN consists of two fundamental components, each directly addressing the two major challenges of this paper: 1) Class-specific features are extracted for fine-grained discrimination via split-channel and gradient-guided class-specific attention mechanisms; 2) We develop a multi-anomaly classifier, which effectively captures long-distance correlation features via a recurrent attention graph with visual and statistical guidance for graph propagation, containing prior statistical and image-specific information. The experiments and statistical analyses demonstrate that RAGRN outperforms all related state-of-the-art frameworks and has the potential to be applied to practical inspection.