RFFRDet: A Refined Feature Fusion Rotation Detector for Prohibited Item Recognition in X-Ray Images

With increasing security threats in critical infrastructure, intelligent X-ray inspection systems have become essential for modern security frameworks. Contraband detection faces dual challenges: semantic dilution across cross-scale features and degraded directional sensitivity. Existing detection paradigms rely on Euclidean spatial assumptions while ignoring X-ray projection geometry characteristics, leading to feature representation ambiguity and insufficient spatial relationship modeling in complex occlusion scenarios. To address these challenges, we propose RFFRDet, a rotation detector based on refined feature decoupling. First, an Integrated Local Attention (ILA) module is constructed that enables material-aware and geometry-aware feature enhancement through channel-spatial decoupling. Second, a Multi-Resolution Feature Fusion (MRFF) network is designed that achieves optimal coupling between fine-grained spatial positioning and high-level semantic understanding through parallel multi-scale aggregation. Finally, we construct the first multi-directional X-ray security benchmark dataset with rotation annotations for 27,000 images. Extensive experiments demonstrate that RFFRDet achieves 97.4% mAR and 96.8% mAP, representing improvements of 1.6% and 1.5% over state-of-the-art methods, respectively.