Light2Ray: Lightweight dual-view prohibited item detection model in X-ray images based on Transformer

Using computer vision technology to detect prohibited items in X-ray images is an effective method for realizing intelligent security checking. Dual-view security checking can capture the images from both vertical and horizontal perspectives of the same package at the same time, addressing issues such as unfavorable imaging angles and object occlusion at the image acquisition end. In this paper, we proposed a novel prohibited item detection model based on Transformer architecture in dual-view X-ray images. Two feature fusion module, named as feature selection module and corss-attention fusion module, are introduced to make interaction and enhancement. To improve the model inference efficiency, we use MobileViT as the backbone network to reduce the model size. Simulation results based on Dualray dataset has demonstrated the performance of the proposed model.