Transformer-based dual-view X-ray security inspection image analysis

Artificial intelligence technology is rapidly advancing and has been widely applied in the field of intelligent security inspection. Utilizing computer vision technology to detect prohibited items in X-ray images has drawn much attention. Due to the transmission effect of X-rays, single-view security inspection images are prone to object occlusion and poor imaging angles, which seriously affects the performance of object detection models. Dual-view security inspection equipment can simultaneously capture X-ray transmission images of the item under inspection from both horizontal and vertical angles, which can effectively address issues of poor imaging angles and object occlusions that single-view imaging cannot resolve. In this paper, we introduced the artificial intelligence technology in dual-view security inspection image analysis, and proposed the dual-view feature fusion and prohibited item detection model in X-ray security inspection images based on the Vision Transformer framework. The detection model contains two input channels: the main and secondary channel. The main function of the main channel is to detect prohibited items in security inspection images, while the secondary channel is dedicated to providing effective feature information of prohibited items for the main channel. Two feature interaction modules are applied in the proposed model to realize dual channel information exchange and supplement from local and global perspectives respectively. Simulation results based on the public Dualray dataset have demonstrated the state-of-the-art performance of the proposed dual-view X-ray image detection model. Code is available at https://github.com/zhg-SZPT/Trans2Ray.