DAGNet: A Dual-View Attention-Guided Network for Efficient X-ray Security Inspection

TL;DR

DAGNet introduces a dual-view attention-guided network that enhances feature representation and fusion for more accurate and efficient X-ray security inspection, addressing viewpoint dependency issues in complex baggage scenarios.

Contribution

The paper presents a novel dual-view network with three modules that improve feature interaction, hierarchical enhancement, and fusion for X-ray security inspection.

Findings

Outperforms existing methods on multiple architectures

Significantly improves contraband detection accuracy

Enhances feature representation in complex stacking scenarios

Abstract

With the rapid development of modern transportation systems and the exponential growth of logistics volumes, intelligent X-ray-based security inspection systems play a crucial role in public safety. Although single-view X-ray baggage scanner is widely deployed, they struggles to accurately identify contraband in complex stacking scenarios due to strong viewpoint dependency and inadequate feature representation. To address this, we propose a Dual-View Attention-Guided Network for Efficient X-ray Security Inspection (DAGNet). This study builds on a shared-weight backbone network as the foundation and constructs three key modules that work together: (1) Frequency Domain Interaction Module (FDIM) dynamically enhances features by adjusting frequency components based on inter-view relationships; (2) Dual-View Hierarchical Enhancement Module (DVHEM) employs cross-attention to align features between views and capture hierarchical associations; (3) Convolutional Guided Fusion Module (CGFM) fuses features to suppress redundancy while retaining critical discriminative information. Collectively, these modules substantially improve the performance of dual-view X-ray security inspection. Experimental results demonstrate that DAGNet outperforms existing state-of-the-art approaches across multiple backbone architectures. The code is available at:https://github.com/ShilongHong/DAGNet.