A generation-based defect detection system for rail transit infrastructure

The use of unmanned aerial vehicles (UAVs) for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large, inaccessible, and topographically complex areas. However, current UAV-based detection methods face several critical limitations, including constrained deployment frequency, limited availability of annotated defect data, and the lack of mature risk assessment frameworks. To address these challenges, this study introduces a novel approach that integrates diffusion models with large language models (LLMs) to generate high-quality synthetic defect images tailored to railway slope scenarios. Furthermore, an improved transformer-based architecture is proposed, incorporating attention mechanisms and LLM-guided diffusion-generated imagery to enhance defect recognition performance under complex environmental conditions. Experimental evaluations conducted on a dataset of 300 field-collected images from high-risk railway slopes demonstrate that the proposed method significantly outperforms existing baselines in terms of precision, recall, and robustness, indicating strong applicability for real-world railway infrastructure monitoring and disaster prevention.