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

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 maglev 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.