Generative adversarial networks based sample generation of coal and rock images

The intelligentization of coal mine requires the realization of intelligent mining.Thereinto,automatic coal and rock recognition is one of the core technologies for unmanned mining.In recent years,image-based identification of coal and rock has been widely concerned.The difficulty of image acquisition due to the influence of mining environment is one of the main factors restricting coal and rock image recognition.To solve the problem of coal and rock image data shortage and increase the amount of data,this paper proposes the Var-ConSinGAN model based on the Generative Adversarial Networks(GANs) trained on single image and constructs the framework of sample generation and feature migration.Although the images generated by ConSinGAN model are clear,there is still a lack of diversity for coal and rock image generation.The improved training method and image reconstruction method of the model can improve the diversity of coal and rock image generation.The model in this paper adopts a pyramid structure and uses a multi-stage training method.Each stage trains images of different scales,and any number of images can be generated.A new image size transformation method is proposed in the model to generate reconstructed images with different resolution.At the same time,a curve function is used to gradually increase the number of iterations in each stage.Then the results generated from the single image in the first stage are migrated using Conditional Generative Adversarial Networks based on auxiliary classifier.The new image reconstruction method maintains that the density of the reconstructed image at the high resolution stage is higher than that at the low resolution stage.The new training iteration function reduces the number of times the model learns the image structure while increasing the number of times the model learns the image texture details.The new training iteration function can reduce the total number of model iterations,thus reducing the computational resources consumed during model training.The model needs to input a noise image that conforms to the normal distribution.After training,the generator outputs a generated sample that meets the real image distribution.The experiment was carried out on 500 images of coal and rock,each image generated 400 simulation images,and SSIM indicator was used to evaluate the brightness,contrast and structure of the generated images.Among them,the SSIM indicator value of coal and rock images with strong structure is very low,on the contrary,the SSIM index value of coal and rock images with weak structure is relatively high.According to the experimental results,it can be concluded that the Var-ConSinGAN model alleviated the problem that the original GAN could not be trained when the data was insufficient and the coal and rock images generated by ConSinGAN had obvious sense of“border”and insufficient diversity.Compared with ConSinGAN,Var-ConSinGAN improves the performance of the model by 13.8%.The experimental results of feature migration show that different types of coal and rock have learned features that do not exist in each other.The amount of coal and rock image data is increased.