An algorithm for generating flame image data sets based on GAN

In order to solve the problem of insufficient flame image data, this paper designs a flame image data sets generation algorithm based on generative adversarial network and studies the influence of different generator depth and training times on the flame generation effect. The effect of generative flame under different conditions was quantitatively evaluated by two indexes of Inception score and PSNR. The simulation results show that when the generator is 9 layers and training is 80 times, the Inception score is 2.06 and PSNR is 13.18. The effect of generative flame image is better at this time. Therefore, this generative adversarial network model can better realize the purpose of generating the flame image data sets.