On Using XMC R-CNN Model for Contraband Detection within X-Ray Baggage Security Images
On Using XMC R-CNN Model for Contraband Detection within X-Ray Baggage Security Images
复制标题
DOI：
10.1155/2020/1823034
复制
发表时间：
2020-09
影响因子：
--
通讯作者：
Yong Zhang;Weiwu Kong;Dong Li;Xudong Liu
中科院分区：
文献类型：
--
作者：
Yong Zhang;Weiwu Kong;Dong Li;Xudong Liu
我们提出了一种基于区域的 X 射线材料分类器卷积神经网络 (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。采用XMC R-CNN模型，通过X射线物质分类器算法以及有机剥离和无机剥离算法解决重叠X射线行李图像中的违禁品检测问题，取得了较好的检出率和漏检率。枪支、刀具检出率分别为96.5%和95.8%，枪支、刀具漏检率分别为2.2%和4.2%。基于XMC R-CNN模型的违禁品检测技术应用于安检X射线行李图像。根据用户需求，可以对某些特定领域的安全X射线行李图像进行自动筛选，减少了安检人员需要筛选的X射线行李图像数量。提高了安检效率，降低了安检劳动强度。另外，安检人员可以根据自动检测的箱子对X光行李图像进行筛查，可以提高安检效果。
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.