X-ray security check system and method based on convolutional neural network

Abstract

The invention discloses an X-ray security check system based on a convolutional neural network. The X-ray security check system comprises an X-ray security check module, an image acquisition module, aback-end server and a display module which are connected in sequence; the X-ray security check module is used for acquiring an X-ray image of an object and transmitting the X-ray image to the image acquisition module; the image acquisition module is used for acquiring an X-ray image of an object to be detected from an image output interface of the X-ray security check module, converting the X-rayimage into a data format accepted by the security check system, and transmitting the X-ray image in the data format to the back-end server; the back-end server is used for carrying out hazardous article detection on the X-ray image to obtain a detection result and transmitting the detection result to the display module; the display module is used for man-machine interaction, displaying a detection result and giving an alarm prompt to the detected dangerous goods; according to the method, the requirements for real-time performance and accuracy of the security check process are fully considered, and the system and method have a wide application prospect.

Description

technical field [0001] The invention relates to the field of security inspection and prevention, in particular to an X-ray security inspection system and method based on a convolutional neural network. Background technique [0002] X-ray security inspection machine is currently the most widely used security inspection prevention and control facility, often used in subway stations, railway stations, airports, government office buildings, exhibition centers and other places. The X-ray security inspection machine is suitable for non-invasive screening of objects to be inspected, and its main part is an X-ray generator, which is used to perform X-ray imaging on the object to be inspected when it enters the tunnel of the security inspection machine. The color of the image of the X-ray security inspection machine depends on the material of the object to be inspected. Generally, organic materials (such as paper, clothes and most explosives) are displayed in orange, and mixed materi...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and provide an X-ray security inspection system based on a convolutional neural network, which applies an object detection algorithm to the input image to calibrate the position of suspicious dangerous goods and judge the category of dangerous goods. Due to the problem of decreased detection accuracy caused by overlapping objects, this system does not need to undergo shape and color detection separately during the detection process, and uses a lightweight and efficient convolutional neural network to ensure real-time detection of dangerous goods. Practical functions that are helpful to actual security inspection scenarios, such as data analysis functions for dangerous goods detection, incremental training functions, and remote monitoring

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