Multi-label Recognition of Prohibited Items in Security X-ray Images Based on Convolutional Neural Network
In safety inspections,the detection and identification of prohibited items still excessively rely on the visual ex-perience of security inspectors.How to automatically identify common prohibited items in X-ray images and assist security inspectors in making decisions has become an urgent issue in the field of security inspection.This paper is based on deep learning technology to study the multi label recognition method for prohibited items in security X-ray images.By introducing the multi hot vector annotation method to annotate the security X-ray dataset,the Darknet-53 convolutional neural network is transferred and trained to determine the category of prohibited items in X-ray images.The experimental results show that the average accuracy of multi label recognition for prohibited items in X-ray images of security checks has reached over 98%,meeting the application requirements in real security check scenarios.
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