Work

Deep learning-based method for damage identification and localization of the maglev track stator surface

Year: 2025

Type: article

Abstract: The stator of the maglev track plays a crucial role in the operation of the maglev system. Currently, the efficiency of maglev track inspection is limited by several factors, including the large span of elevated structures, manual visual inspection, short inspection window times, and limited GPS positioning accuracy. To address these issues, this paper proposes a deep learning-based method for detecting and locating stator surface damage. This study establishes a magnetic levitation track stator surface image dataset, trains different object detection models, and compares their performance. Ultimately, YOLO and ByteTrack object tracking algorithms were chosen as the basic framework and enhanced, to achieve automatic identification of high-speed maglev track stator surface damage images and track and count stator surface localization feature images. By matching the identified damaged images with their corresponding stator segment and beam segment sequence numbers, the location of the damage is pinpointed to the corresponding stator segment, enabling rapid and accurate identification and localization of complex damage to the maglev track stator surface.

Source: High-speed Railway

DOI: 10.1016/j.hspr.2025.09.007

Authors: Shihua Huang, Tiange Wang, Guofeng Zeng

Institution: Tongji University

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funders: Shanghai Science and Technology Development Foundation, National Natural Science Foundation of China