X-ray baggage inspection is an essential task to detect threat objects at important controlled access places, which can guard personal safety and prevent crime. Generally, it is carried out by screeners to visually determine whether or not a bag contains threat objects. Whereas, manual detection exhibits distinct shortcomings, from high detection errors to different detection results produced by screeners. These limitations can be addressed by introducing automated detection model of threat objects for X-ray baggage inspection. However, existing automated detection methods cannot realize end-to-end detection and the detection results include only classification without location. In this paper, we propose an automated detection model of threat objects based on depthwise separable convolution. Our model is able to not only categorize the threat object but also locate it simultaneously. The network model has the advantage of high detection accuracy, fast computational speed, and a few parameters. Meanwhile, the precision of threat object regions is enhanced with the help of multi-scale prediction. A deformation layer is added in our model, which can provide invariance to affine warping. The experiments on the GDXray database (Mery et al. in J Nondestr Eval 34(4):42, 2015) demonstrate that the overall performance of our proposed model is superior to YOLOv3 (Redmon J and Farhadi A in YOLOv3: an incremental improvement, 2018) model, SSD (Liu et al. in SSD: single shot multibox detector. In: European Conference on Computer Vision (ECCV), pp. 21–37, 2016) model, and Tiny_YOLO (Redmon et al. in You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788, 2015) model.

X射线行李检查是一项重要任务，可在重要的可控制进入区域检测威胁物体，从而保护人身安全和预防犯罪。通常，它由检查人员执行以目视确定袋子是否装有威胁物。然而，从高检测错误到筛选器产生的不同检测结果，手动检测存在明显的缺陷。通过引入用于X射线行李检查的威胁物体的自动检测模型，可以解决这些限制。然而，现有的自动检测方法无法实现端到端检测，并且检测结果仅包括分类而没有位置。在本文中，我们提出了一种基于深度可分离卷积的威胁对象自动检测模型。我们的模型不仅可以对威胁对象进行分类，还可以同时定位它。该网络模型具有检测精度高，计算速度快，参数少等优点。同时，借助多尺度预测提高了威胁目标区域的精度。在我们的模型中添加了一个变形层，可以为仿射扭曲提供不变性。GDXray数据库上的实验（Mery等人，J Nondestr Eval 34（4）：42，2015年）表明，我们提出的模型的整体性能优于YOLOv3（YOLOv3中的Redmon J和Farhadi A：渐进改进， 2018年）模型，SSD（Liu等人，SSD：单发多盒检测器。在：欧洲计算机视觉会议（ECCV），2016年，第21-37页）中，以及Tiny_YOLO（Redmon等人，在You only look曾经：统一，实时物体检测。在：2016 IEEE计算机视觉和模式识别会议（CVPR），第779–788页，2015年）模型中。