GDFDNet: A Novel Graph-Based Dynamically Fused Dual-Stream Network for Accuracy Prohibited Items Detection

Abstract

In various security inspection scenarios, prohibited items detection in X-ray images is of great significance for safeguarding public safety and effectively reducing the potential risks of crimes and terrorist activities. However, existing detection methods still face the challenge of overlapping image features when distinguishing prohibited items from complex backgrounds. To tackle this issue, this paper proposes a novel Graph-based Dynamically Fused Dual-Stream Network (GDFDNet). This network introduces highly decoupled HSV color space features, which are dynamically fused with RGB color space features to fully explore the edge and material characteristics of overlapping prohibited items. Specifically, we adopt a dynamically fused RGB-HSV dual-stream framework and design two key modules: a Graph-based Edge-Aware module (GEA) and a Graph-based Material-Aware Fusion module (GMAF). The former is utilized to enhance multi-scale edge features of prohibited items, while the latter is responsible for performing the dynamic fusion of features in dual color spaces guided by edge features, and then extracting valuable material information of prohibited items from the overlapping foreground and background features. Extensive experiments on the SIXray and PIDray datasets demonstrate that our proposed network significantly outperforms the existing state-of-the-art methods.