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.
作者
ZHU Yue
ZHANG Hai-gang
AN Jiu-yuan
YANG Jin-feng
朱越;张海刚;安久远;杨金锋(Tianjin Key Lab for Advanced Signal Processing,Civil Aviation University of China,Tianjin 300300,China;Institute of Applied Articial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area,Shenzhen Polytechnic,Shenzhen 518055,China)
基金
the National Natural Science Foundation of China(No.61806208)
the Fundamental Research Funds for the Central Universities(No.3122018S008)
the Tianjin Education Committee Research Project(No.2018KJ246).
