Abstract
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.
Highlights
In recent years, with the increasing seriousness of terrorist activities, the safety of air transport has been paid more and more attention by all countries in the world
An explosive is hidden under several steel plates in the baggage, and it cannot be seen at all in the original image. It is difficult for the security inspector to determine that there is a dangerous object in this area. e XMC R-convolutional neural networks (CNNs) 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
E main contribution of 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 organic stripping and inorganic stripping algorithm, and the detection rate and the miss rate that meet the requirements of screening on-site are achieved by the deep learning method. e detection rate is greater than 95%, and the miss rate is less than 5%
Summary
With the increasing seriousness of terrorist activities, the safety of air transport has been paid more and more attention by all countries in the world. As the most popular machine learning method, deep learning has achieved excellent results in object classification and detection. The XMC R-CNN method based on deep learning will be used to detect the contraband within X-ray baggage security images. E 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.
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