Multi-dimensional Edge-based Graph Representation Learning for Obstructed Prohibited Items Detection in X-ray Images

X-ray security inspection has been widely used to maintain safety in public places and transportation systems. Due to the imaging characteristics of X-ray images, the stacking of items can cause translucency interference in the images, making it challenging to detect contraband items in backpacks or suitcases during security checks. Most existing methods have improved detection by adjusting the combination of features without considering the relationships between targets. In this paper, we propose a novel prohibited item graph representation learning algorithm to explicitly model inter-item relationships, aiming at improving their detection performance. Our approach starts with GTG module which generates a graph topology structure connecting the proposals output by the detection backbone network, where each proposal is treated as a node describing a candidate object. Then, the MDE module creates a set of multi-dimensional edge features to comprehensively and explicitly describe the relationships between each pair of connected nodes, allowing context information to be used for their detection. Extensive experiments validate the effectiveness of our method which not only enhances the detection accuracy, but also better identifies hard-to-distinguish objects in complex scenarios. This exploration opens up an uncharted graph-based direction previously unexplored in prior research, providing a new path for future studies in graph-based X-ray security inspection detection. Our code is provided in the Supplementary Material.