A GAN based method for multiple prohibited items synthesis of X-ray security image

Abstract:

Detecting prohibited item based on convolutional neural networks (CNNs) is of great significance to ensure public safety. However, the natural occurrence of such prohibited items is a small-probability event, collecting enough datasets to support CNN training is a big challenge. In this paper, we propose a new method for synthesizing X-ray security image with multiple prohibited items from semantic label images basing on Generative Adversarial Networks (GANs). Theoretically, we can use it to synthesize as many X-ray images as needed. A new generator architecture with Res2Net is presented, which is more effective in learning multi-scale features of different prohibited items images. This method is extended by establishing the semantic label library which contains 14 000 images. So we totally synthesize 14 000 X-ray security images. The experimental results show the super performance (Fréchet Inception Distance (FID) score of 30.55). And we achieve 0.825 of mean average precision (mAP) with Single Shot MultiBox Detector (SSD) for object detection, demonstrating the effectiveness of our approach.