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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This work has been supported by the National Natural Science Foundation of China (No.61806208), the Fundamental Research Funds for the Central Universities (No.3122018S008), and the Tianjin Education Committee Research Project (No.2018KJ246).
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Zhu, Y., Zhang, Hg., An, Jy. et al. GAN-based data augmentation of prohibited item X-ray images in security inspection. Optoelectron. Lett. 16, 225–229 (2020). https://doi.org/10.1007/s11801-020-9116-z
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DOI: https://doi.org/10.1007/s11801-020-9116-z