Research on Visible Light Equipment Image Data Enhancement Technology Based on Generative Model

Visible light remote sensing imaging technology is an important tool for detecting and analyzing equipment. Massive image data is crucial for training and verifying automatic equipment recognition models. Although there are already corresponding image libraries, there are still problems such as insufficient image types and unsatisfactory quality. In order to solve these problems, this paper compares the use of several different GAN variants, including CGAN, WGAN-CP, WGAN-GP, and DDPM in the diffusion model to enhance the visible light remote sensing equipment image library. The effect is to expand the image data set by leveraging the advantages of each model, thereby supporting the implementation of more accurate and robust equipment identification in remote sensing applications.