LAUSR

The high-speed maglev train is a new type of transportation. The long stator track plays a critical role in the levitation guidance and traction system. Therefore, its condition directly affects the operation of maglev trains. It is extremely important to detect the abnormal condition of high-speed maglev tracks to ensure the stable, safe, and reliable operation of the train. In this article, an onboard image detection system is designed for high-speed maglev tracks, which can accurately obtain the image of long stator tracks under the harsh conditions of limited installation space, insufficient illumination, and rapid operation of vehicles. High-speed maglev trains are not yet in widespread use. In China, there is currently only one demonstration operating line located in Shanghai, and the length of the track test line is limited. Therefore, the number of track images that can be acquired is extremely limited. In view of the lack of defective samples of high-speed maglev tracks, this article proposes a data enhancement method based on sample generation and image fusion to augment the dataset of defective samples. To improve the quality of generated high-speed maglev track defect images, a joint attention layer (JEA) combining squeeze-and-exception (SE) block and spatial attention module (SAM) is designed and introduced into the generative adversarial network (GAN). This work provides a data basis for the study of track defect detection of high-speed maglev trains. In addition, this article detects the defects of high-speed maglev tracks via deep learning-based target detection algorithms, which can automatically detect, accurately classify and locate the defects of stator surface and cables, filling the gap in the field of high-speed maglev track defect detection.