Prohibited item detection in X-ray images is one of the most essential and highly effective methods widely employed in various security inspection scenarios. Considering the significant overlapping phenomenon in X-ray prohibited item images, we propose an Anti-Overlapping DETR (AO-DETR) based on one of the state-of-the-art general object detectors, DINO. Specifically, to address the feature coupling issue caused by overlapping phenomena, we introduce the Category-Specific One-to-One Assignment (CSA) strategy to constrain category-specific object queries in predicting prohibited items of fixed categories, which can enhance their ability to extract features specific to prohibited items of a particular category from the overlapping foreground-background features. To address the edge blurring problem caused by overlapping phenomena, we propose the Look Forward Densely (LFD) scheme, which improves the localization accuracy of reference boxes in mid-to-high-level decoder layers and enhances the ability to locate blurry edges of the final layer. Similar to DINO, our AO-DETR provides two different versions with distinct backbones, tailored to meet diverse application requirements. Extensive experiments on the PIXray and OPIXray datasets demonstrate that the proposed method surpasses the state-of-the-art object detectors, indicating its potential applications in the field of prohibited item detection. The source code will be released at https://github.com/Limingyuan001/AO-DETR-test.

翻译：X光图像中的违禁品检测是广泛应用于各类安检场景中最基本且高效的检测手段之一。针对X光违禁品图像中严重的重叠现象，我们提出了一种基于当前最优通用目标检测器DINO的抗重叠DETR（AO-DETR）。具体而言，为克服重叠现象导致的特征耦合问题，我们引入类别特定一对一分配（CSA）策略，该策略约束类别特定查询向量仅预测固定类别的违禁品，从而增强其从重叠的前景-背景特征中提取特定类别违禁品特征的能力。针对重叠现象引发的边缘模糊问题，我们提出前向密集查找（LFD）方案，该方案可提升中高层解码器层中参考框的定位精度，并增强最终层对模糊边缘的定位能力。与DINO类似，我们的AO-DETR通过采用不同主干网络提供两个版本，以满足多样化的应用需求。在PIXray和OPIXray数据集上的大量实验表明，所提方法超越了当前最优目标检测器，展现了其在违禁品检测领域的应用潜力。源代码将发布于https://github.com/Limingyuan001/AO-DETR-test。