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

• 来源期刊: 光电子快报（英文版）
• 年，卷（期）: 2020,（3）
• 页码范围: 225-229
• 页数: 5页
• 语种: 英文
• DOI: 10.1007/s11801-020-9116-z

摘要

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.