Generation of transmission line defect samples based on stable diffusion

Real-world defect samples of transmission lines suffer from generalization issues between positive and negative samples, as well as a lack of publicly available and high-quality samples for training. To address this, a method for generating transmission line defect samples based on stable diffusion is proposed. By applying the stable diffusion model, a large number of high-quality and realistic transmission line defect images are generated. These images are then input into Cycle- GAN, which outputs transmission line images containing various defects such as cracks, breaks, and foreign objects. Experiments show that this method can rapidly generate high-quality transmission line defect samples, improving the classification accuracy of defect samples. The generated transmission line defect images can serve as training samples to enhance the accuracy and generalization ability of transmission line defect detection models.