A Multi-Scale Feature Fusion Framework Integrating Frequency Domain and Cross-View Attention for Dual-View X-ray Security Inspections

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 equipment is widely deployed, it struggles to accurately identify contraband in complex stacking scenarios due to strong viewpoint dependency and inadequate feature representation. To address this, we propose an innovative multi-scale interactive feature fusion framework tailored for dual-view X-ray security inspection image classification. The framework comprises three core modules: the Frequency Domain Interaction Module (FDIM) enhances frequency-domain features through Fourier transform; the Multi-Scale Cross-View Feature Enhancement (MSCFE) leverages cross-view attention mechanisms to strengthen feature interactions; and the Convolutional Attention Fusion Module (CAFM) efficiently fuses features by integrating channel attention with depthwise-separable convolutions. Experimental results demonstrate that our method outperforms existing state-of-the-art approaches across multiple backbone architectures, particularly excelling in complex scenarios with occlusions and object stacking.