Research on Abnormal Image Generation Method for High-Speed Train Fault Detection

In order to solve the problem of scarcity of negative samples in the study of visual anomaly detection algorithms for high-speed trains, this paper proposes a controllable abnormal image sample generation framework based on the fusion of diffusion model and Lora weight fine-tuning, and systematically verifies its effectiveness. First, the controllable image generation and image conversion model based on the diffusion model is introduced, and the principle of its image generation and the steps of generating abnormal image samples of functional components of high-speed trains are described. Then, the core principle of Lora's weight fine-tuning method and the implementation steps of LoRA fine-tuning are introduced. Finally, the experimental results of generating abnormal image samples of high-speed train components by combining the diffusion model local redrawing with the LoRA weight finetuning method are introduced, which proves the effectiveness of this method.