Attention-Guided Dual-Energy X-Ray Image Analysis for Prohibited Item Detection Using YOLOv11 and Transformer Fusion

X-ray inspection has become an integral part of public safety screening systems for detecting prohibited items. However, this remains a challenging task due to severe object overlap, low inter-material contrast, and pronounced class imbalance in contraband detection. Dual-energy X-ray imaging provides enhanced material discrimination, but how to effectively exploit this complementary information toward realtime detection remains a challenge. The limitations discussed above are addressed by the proposal of an attention-guided dual-energy X-ray detection framework, known as AG-DEXDet. A transformer-based feature fusion module, combined with a YOLOv11-based one-stage detector, captures global contextual dependencies and improves robustness in highly congested scenarios. This is further combined with the Class-Specific Augmentation Framework to mitigate data imbalance problems across different restricted-item classes. The balance dualenergy X-ray dataset is used for testing the proposed model, comprising 12 contraband categories. The test results revealed that AG-DEXDet achieved 98.09% detection accuracy and a mean Average Precision of 0.9815 while maintaining realtime inference performance. Compared to traditional CNN- or region-based detectors, the proposed approach achieves higher detection accuracy and greater stability in complex screening environments. Effective Attention-Guided Fusion of Dual-Energy Representations toward Real-Time Security Inspection. These results clearly indicate that AG-DEXDet is highly feasible for implementation in practical X-ray screening systems. Future work will focus on extending the framework to larger-scale datasets and to multi-view X-ray imaging scenarios.