Generative adversarial networks and faster-region convolutional neural networks based object detection in X-ray baggage security imagery

Abstract

X-ray images have now been widely used in public transportation security. However, the ever-increasing number of dangerous object types, along with the complexity of the baggage's contents, makes it unsuitable for clearly displaying various baggages and hazards on X-ray images obtained directly from the inspection system. This article aims to improve the detection of dangerous objects by proposing a data augmentation method for enriching the X-ray prohibited item images using a Generative Adversarial Networks(GANs) based approach. Using the improved GANs model, a new X-ray images were generated with better quality and diversity, and the performance of the proposed method was evaluated using FID(Fréchet Inception Distance) score and Faster R-CNN (Region-based Convolutional Neural Network). Experimental results show that the new augmented X-ray security inspection image dataset can improve the detector performance. If other representative training datasets are utilized, we believe that our methodology could aid in the detection of other kinds of threat objects.