To detect prohibited items in challenging categories, human inspectors typically rely on images from two distinct views (vertical and side). Can AI detect prohibited items from dual-view X-ray images in the same way humans do? Existing X-ray datasets often suffer from limitations, such as single-view imaging or insufficient sample diversity. To address these gaps, we introduce the Large-scale Dual-view X-ray (LDXray), which consists of 353,646 instances across 12 categories, providing a diverse and comprehensive resource for training and evaluating models. To emulate human intelligence in dual-view detection, we propose the Auxiliary-view Enhanced Network (AENet), a novel detection framework that leverages both the main and auxiliary views of the same object. The main-view pipeline focuses on detecting common categories, while the auxiliary-view pipeline handles more challenging categories using ``expert models" learned from the main view. Extensive experiments on the LDXray dataset demonstrate that the dual-view mechanism significantly enhances detection performance, e.g., achieving improvements of up to 24.7% for the challenging category of umbrellas. Furthermore, our results show that AENet exhibits strong generalization across seven different detection models for X-ray Inspection

本研究针对现有单视图X射线检测数据集的不足，提出了大型双视角X射线（LDXray）数据集，以支持更全面的模型训练和评估。通过引入辅视增强网络（AENet），该研究展示了在双视角图像中结合主视图与辅视图的方法显著提高了违禁物品的检测性能，尤其在难度较大的类别如雨伞中提高了24.7%的检测准确率。

双视角X射线检测：人工智能能像人类一样从双视角X射线图像中检测违禁物品吗？