ZHU Yue,ZHANG Hai-gang,AN Jiu-yuan,YANG Jin-feng.GAN-based data augmentation of prohibited item X-ray images in security inspection[J].Optoelectronics Letters,2020,16(3):225-229 GAN-based data augmentation of prohibited item X-ray images in security inspection Author Name Affiliation ZHU Yue  Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China  ZHANG Hai-gang  Institute of Applied Articial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen Polytechnic, Shenzhen 518055, China  AN Jiu-yuan  Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China  YANG Jin-feng  Institute of Applied Articial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen Polytechnic, Shenzhen 518055, China  Abstract:        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. Hits: 961 Download times: 0 View Full Text    Download reader