WAFAN: Bridging Domain Gaps in X-ray Security Inspection via Weighted Adaptive Feature Adversarial Networks

X-ray security inspection systems face significant challenges when deployed across different scanning devices due to domain shifts that substantially degrade prohibited item recognition performance. Existing deep learning approaches assume consistent label distributions between training and testing domains, which is impractical in real-world security scenarios where devices vary in penetration capabilities and imaging characteristics. To address this critical limitation, we propose a Weighted Adaptive Feature Adversarial Network (WAFAN) that leverages partial domain adaptation (PDA) to achieve robust cross-domain prohibited item recognition. Our approach introduces three key innovations: (1) a similarity-based weight assignment strategy that effectively distinguishes relevant target-domain classes from irrelevant source-domain categories, mitigating negative transfer effects; (2) an adaptive convolution module integrated via residual connections that explicitly captures and compensates for cross-domain feature distribution differences; and (3) a multi-space adversarial learning framework that ensures domain-invariant feature extraction. Comprehensive experiments on PIDray and SIXray datasets demonstrate that WAFAN achieves superior performance over state-of-the-art PDA methods, with classification accuracy exceeding 80% on target domains while maintaining computational efficiency. The proposed method offers a practical solution for deploying AI-powered security inspection systems across heterogeneous X-ray devices without requiring extensive retraining or domain-specific annotations.