The detection of prohibited items in X-ray security inspections is vital for ensuring public safety. However, the long-tail distribution of item categories, where certain prohibited items are far less common, poses a big challenge for detection models, as rare categories often lack sufficient training data. Existing methods struggle to classify these rare items accurately due to this imbalance. In this paper, we propose a Dual-level Boost Network (DBNet) specifically designed to overcome these challenges in X-ray security screening. Our approach introduces two key innovations: (1) a specific data augmentation strategy employing Poisson blending, inspired by the characteristics of X-ray images, to generate realistic synthetic instances of rare items which can effectively mitigate data imbalance; and (2) a context-aware feature enhancement module that captures the spatial and semantic interactions between objects and their surroundings, enhancing classification accuracy for underrepresented categories. Extensive experimental results demonstrate that DBNet improves detection performance for tail categories, outperforming sota methods in X-ray security inspection scenarios by a large margin 17.2%, thereby ensuring enhanced public safety.

本研究解决了X射线安检中禁止物品检测面临的长尾分布问题，尤其是对稀有物品分类准确性的挑战。论文提出了一种双级增强网络（DBNet），通过新的数据增强策略和上下文感知特征增强模块，显著提高了对稀有类别的检测性能，相比现有方法提升了17.2%。

用于长尾禁止物品检测的双级增强网络