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

doi:https://doi.org/10.1007/s11801-020-9116-z

Archive > Volume 16 Issue 3 > 2020,16(3):225-229. DOI:https://doi.org/10.1007/s11801-020-9116-z CSTR:[cstr] Prev Next

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

Article
Figures
Metrics
Preview PDF
Reference
Related
Cited by
Materials

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.

Keywords:

Project Supported:

Clc Number:

More...

More...

Get Citation

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