Multi-Scale Rail Surface Anomaly Detection Based on Weighted Multivariate Gaussian Distribution

Abstract

Rail surface anomaly detection, referring to the process of identifying and localizing abnormal patterns in rail surface images, faces the limitation of robustness because of the large diversity of scale, quantity, and morphology of surface anomalies. To address this challenge, we propose a multi-scale rail surface anomaly detection method (MRS-AD) based on a distribution model, which cooperates neighborhood information to precisely locate rail surface anomalies. Specifically, MRS-AD integrates multi-scale structures to enhance the perception of different scale information of anomalies. Furthermore, the neighborhood information is utilized to capture the correlations between adjacent regions, and thereby a weighted multivariate Gaussian distribution model is estimated to improve the recognition capability of anomalous morphologies. To validate the effectiveness of MRS-AD, we collected and built a Rail Surface Anomaly Detection dataset (RSAD), considering the scale and quantity of rail surface anomalies. Extensive experiments on RSAD, RSDD and NEU-RSDD-2 demonstrate the superiority of MRS-AD. The code and dataset are publicly available at https://github.com/lyy70/MRS-AD