GAN-based data augmentation of prohibited item X-ray images in security inspection

Convolutional neural networks(CNNs)based methods for automatic discriminant of prohibited items in X-ray images attract attention increasingly.However,it is difficult to train a reliable CNN model using the available X-ray security image databases,since they are not enough in sample quantity and diversity.Recently,generative adversarial network(GAN)has been widely used in image generation and regarded as a power model for data augmentation.In this paper,we propose a data augmentation method for X-ray prohibited item images based on GAN.First,the network structure and loss function of the self-attention generative adversarial network(SAGAN)are improved to generate the realistic X-ray prohibited item images.Then,the images generated by our model are evaluated using GAN-train and GAN-test.Experimental results of GAN-train and GAN-test are 99.91%and 98.82%respectively.It implies that our model can enlarge the X-ray prohibited item image database effectively.