Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. The large volume and high throughput of passengers, mailed parcels, etc., during rush hours make it a Big Data analysis task. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task even under resource-constrained and embedded execution scenarios, e.g., as is the case with fast, single-stage, anchor-based object detectors. This paper proposes a two-fold improvement of such algorithms for the X-ray analysis domain, introducing two complementary novelties. Firstly, more efficient anchors are obtained by hierarchical clustering the sizes of the ground-truth training set bounding boxes; thus, the resulting anchors follow a natural hierarchy aligned with the semantic structure of the data. Secondly, the default Non-Maximum Suppression (NMS) algorithm at the end of the object detection pipeline is modified to better handle occluded object detection and to reduce the number of false predictions, by inserting the Efficient Intersection over Union (E-IoU) metric into the Weighted Cluster NMS method. E-IoU provides more discriminative geometrical correlations between the candidate bounding boxes/Regions-of-Interest (RoIs). The proposed method is implemented on a common single-stage object detector (YOLOv5) and its experimental evaluation on a relevant public dataset indicates significant accuracy gains over both the baseline and competing approaches. This highlights the potential of Big Data analysis in enhancing public safety.

翻译：自动化检测X射线图像中的违禁物品可显著提升公共安全，通过提高安检员在机场、地铁、海关/邮局等场所的工作效率并减轻其心理负担。高峰时段大量旅客及邮寄包裹的高通量特性使其成为一项大数据分析任务。基于深度神经网络的现代计算机视觉算法已被证明即使在资源受限的嵌入式执行场景下（如快速、单阶段、基于锚框的目标检测器）也能胜任此任务。本文针对X射线图像分析领域提出该类算法的双重改进，引入两项互补创新。首先，通过对训练集真实标注框尺寸进行层次聚类获取更高效的锚框，使生成的锚框遵循与数据语义结构对齐的自然层次。其次，通过将高效交并比指标引入加权聚类非极大值抑制方法，修改目标检测流水线末端的默认非极大值抑制算法，以更好处理遮挡目标检测并减少误检数量。高效交并比能更显著区分候选边界框/感兴趣区域间的几何相关性。所提方法在通用单阶段目标检测器（YOLOv5）上实现，并在相关公开数据集上的实验评估表明，其准确率显著优于基线方法与对比方法。这凸显了大数据分析在提升公共安全领域的潜力。