- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | ZHENG Xinyu, HUANG Shihua, WANG Tiange, ZHANG Lingfeng, LIN Guobin. Detection Method for Stator Surface of Maglev Tracks Based on Vision-Language Fusion[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20250643
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To solve the problems of insufficient data volume and unbalanced category distribution in the detection of the stator surface of maglev tracks, a vision-language fusion detection method based on a large language model-guided diffusion model was proposed. First, given the current situation of scarcity and unbalanced distribution of five types of samples (spalling, cracking, scratching, stator gap, and beam gap), a prompt word generation strategy guided by a large language model was designed to generate diversified semantic descriptions of defects through chain-of-thought reasoning. Secondly, the generated prompt words and spatial mask constraints were input into a latent diffusion model to generate high-quality samples with similar distributions, effectively expanding the data of scarce categories. Finally, a detection network fusing vision and language modalities was constructed, where bidirectional cross-attention fusion of images and texts was achieved through a feature enhancer, a language-guided query selection mechanism was adopted to dynamically generate detection queries, and a cross-modal decoder was designed to complete multi-stage feature interaction. The research results indicate that under the condition of data augmentation assisted by the large language model, the quality of generated samples increases by approximately 15% compared with traditional generation methods, and the average detection accuracy of the detection network on the five categories increases by approximately 7% compared with baseline methods.
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