Data Augmentation of X-Ray Images in Baggage Inspection Based on Generative Adversarial Networks

Abstract

Recognizing prohibited items in X-ray security checking images automatically based on Convolutional Neural Networks (CNNs) has attracted attention increasingly. However, there are no suitable X-ray security checking image databases to train a reliable CNN model. Therefore, we propose a data augmentation method for X-ray security checking images. First, a lot of new X-ray prohibited item images are generated using the improved Self-Attention Generative Adversarial Network (SAGAN). Next, a Cycle GAN based method is proposed to transform the item natural images into the X-ray images. It can enrich the diversity of the new images, including item shape and pose. Then, we combine the prohibited item images with background images to synthesize the new X-ray security checking images. Finally, two single shot multi-box detector (SSD) models are applied to verify whether the enlarged database has achieved data augmentation. Experimental results show that the performance of SSD model trained by the enlarged database is better than the SSD model trained by the original database. It implies that our method can achieve data augmentation for X-ray security checking images effectively.