Generative object separation in X-ray images

Abstract

X-ray imaging is essential for security inspection; nevertheless, the penetrability of X-rays can cause objects within a package to overlap in X-ray images, leading to reduced accuracy in manual inspection and increased difficulty in auxiliary inspection techniques. Existing methods mainly focus on object detection to enhance the detection ability of models for overlapping regions by augmenting image features, including color, texture, and semantic information. However, these approaches do not address the underlying issue of overlap. We propose a novel method for separating overlapping objects in X-ray images from the perspective of image inpainting. Specifically, the separation method involves using a vision transformer (ViT) to construct a generative adversarial network (GAN) model that requires a hand-created trimap as input. In addition, we present an end-to-end approach that integrates Mask Region-based Convolutional Neural Network with the separation network to achieve fully automated separation of overlapping objects. Given the lack of datasets appropriate for training separation networks, we created MaskXray, a collection of X-ray images that includes overlapping images, trimap, and individual object images. Our proposed generative separation network was tested in experiments and demonstrated its ability to accurately separate overlapping objects in X-ray images. These results demonstrate the efficacy of our approach and make significant contributions to the field of X-ray image analysis.