Transmission line inspection models improved by synthetic data from diffusion models

The training of deep learning models is inseparable from sufficient data as the driving force. However, in the power transmission model training task, it is very difficult and expensive to obtain sufficient training samples, so data augmentation has become a common means of increasing training samples. In recent years, various deep generative models have demonstrated strong sample generation capabilities in different modalities, especially generative adversarial networks (GANs) have made important progress in synthesizing compelling image and audio samples. However, the traditional GAN model faces difficulties in the large amount of data and computing resources required for training in the field of transmission lines, and there may be problems such as lack of diversity and detail in generated images, and unstable training. In order to solve these problems, this paper proposes to use a diffusion model-based method to generate transmission line images, expand the data set and improve model performance. The final transmission image generated by the model is complete and clear, and the overall model performance is improved by 1.5%.