Image-Based Detection and Real-World Positioning: A Complete System for Anomaly Object Detection in High-Speed Railway Preventive Maintenance

Anomaly object detection is a critical task for ensuring the safety of high-speed railway system (HSRS). With the advancement of imaging technologies and hardware such as visual sensors, large quantities of high resolution inspection images are available in HSRS. Against this backdrop, visionbased measurement (VBM) has emerged as the mainstream technology for detecting anomalous objects. In HSRS, VBM is divided into four stages — (i) Data pre-processing, (ii) Imageanalysis, (iii) Measurand identification, and (iiii) Manual maintenance. Mainstream VBM methods center on the second stage, treating image analysis as the most crucial element. However, recent approaches have been dedicated to locating anomaly objects in images, addressing only ”where anomaly objects lie in images”. This gap greatly exacerbates the inherent lags between anomaly detection and manual preventive maintenance in HSRS. In this paper, we propose a Image-Based Detection and Real-World Positioning (ID-RP) method, which integrates anomaly localization in both images and the real world into a unified system. As the unique identifier of positional information in HSRS, pillar number plate (PNP) allows to uniquely locate defects in terms of specific section, line orientation, and precise positions within high-speed rail operational routes. Therefore, our ID-RP framework unifies PNP detection and anomaly detection into a complete system. Moreover, we develop a multi-geometric feature extraction (MGFE) network, which bridges the feature extraction discrepancy caused by the divergent appearance and morphology of PNP and anomaly objects, enabling joint learning of the two tasks. Our method further constructs a multi-task and multi-modal learning (M2L) pipeline to simultaneously improve localization precision and classification accuracy of PNP and anomaly objects. We compare our proposed ID-RP with many state-of-the-art common and anomaly object detectors. Extensive experimental comparisons demonstrate that our complete system not only achieves more accurate performance on anomaly detection, but also precisely calibrates the real-world position of anomaly objects, providing a more practical and reliable solution for anomaly detection and preventive maintenance in HSRS.