GAN-based data augmentation of prohibited item X-ray images in security inspection
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1. Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China;2. Institute of Applied Articial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen Polytechnic, Shenzhen 518055, China

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    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.

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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

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History
  • Received:July 11,2019
  • Revised:August 28,2019
  • Online: June 02,2020
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