We present an X-ray material classifier region-based convolutional neural network (XMC R-CNN) model for detecting the typical guns and the typical knives in X-ray baggage images. The XMC R-CNN model is used to solve the problem of contraband detection in overlapped X-ray baggage images by the X-ray material classifier algorithm and the organic stripping and inorganic stripping algorithm, and better detection rate and the miss rate are achieved. The detection rates of guns and knives are 96.5% and 95.8%, and the miss rates of guns and knives are 2.2% and 4.2%. The contraband detection technology based on the XMC R-CNN model is applied to X-ray baggage images of security inspection. According to user needs, the safe X-ray baggage images can be automatically filtered in some specific fields, which reduces the number of X-ray baggage images that security inspectors need to screen. The efficiency of security inspection is improved, and the labor intensity of security inspection is reduced. In addition, the security inspector can screen X-ray baggage images according to the boxes of automatic detection, which can improve the effect of security inspection.

中文翻译：

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使用XMC R-CNN模型进行X射线行李安全图像内的违禁品检测

我们提出了一种基于X射线材料分类器区域的卷积神经网络（XMC R-CNN）模型，用于检测X射线行李图像中的典型枪支和典型刀具。利用XMC R-CNN模型，解决了X射线材料分类器算法，有机剥离和无机剥离算法对重叠X射线行李图像中违禁品的检测问题，具有更好的检测率和漏检率。枪和刀的检出率分别为96.5％和95.8％，枪和刀的漏检率分别为2.2％和4.2％。将基于XMC R-CNN模型的违禁品检测技术应用于安检的X射线行李图像。根据用户需求，可以在某些特定字段中自动过滤安全X射线行李图像，这减少了安全检查人员需要筛选的X射线行李图像的数量。提高了安全检查的效率，减轻了安全检查的工作强度。另外，安检人员可以根据自动检测框对X射线行李图像进行筛选，可以提高安检的效果。