X-Ray Object Detection Using Zeff for Liquid Hazard Screening
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Solution Overview
Problem
Existing CT scanners struggle to accurately detect liquid hazardous substances and narcotics due to their shapeless nature, resulting in low detection rates and accuracy.
Innovation Solution
An object detection method and system that utilizes effective atomic number values (Zeff) and energy-band-based multi-energy image reconstruction, combined with an artificial neural network model, to segment and identify liquid substances by calculating and segmenting X-ray images, and incorporating multi-view image analysis for comprehensive detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing CT scanners use shape-based object specification and property inference, then the detection process is simple, but the detection accuracy for liquid hazardous substances and narcotics is very low
Solution Approach 1:
The patent changes the detection parameter from shape-based inference to effective atomic number (Zeff) calculation. By computing Zeff values for different energy bands and comparing them against a database of known substances, the system achieves accurate identification of liquid hazardous substances and narcotics regardless of their shape or container form
Solution Approach 2:
The patent introduces an effective atomic number calculation module as an intermediary between X-ray imaging and object identification. This module computes Zeff values that serve as a characteristic fingerprint for substance identification, enabling accurate detection without direct shape analysis
2Measurement precision
If existing CT scanners detect objects based on external shapes, then the system is easy to operate, but liquid substances with no fixed shape cannot be accurately detected
Solution Approach 1:
The system transitions from shape-based detection to effective atomic number-based detection. This parameter change enables accurate identification of liquid substances that lack fixed external shapes, as Zeff values remain consistent regardless of the substance's physical form or container geometry
Solution Approach 2:
The patent replaces the mechanical/visual shape analysis approach with a computational physics approach based on X-ray attenuation properties. By substituting shape-based inference with effective atomic number calculation, the system achieves reliable liquid substance detection without requiring complex manual intervention
3Productivity
If multi-energy image reconstruction with effective atomic number calculation is implemented, then detection rate increases by 200-500%, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing effective atomic number values for multiple energy bands in a database. During actual detection, the system only needs to compute Zeff for the scanned objects and compare against pre-prepared reference data, significantly reducing real-time processing requirements while maintaining high detection rates
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Significantly increases the accuracy of object specification and detection rates for both solid and liquid hazardous substances, including explosives and narcotics, by up to 200-500% improvement over previous methods.
Implementation Method 1
An X-ray generator is a device for generating an X-ray by colliding an accelerated electron beam with an anode target
Implementation Method 2
CT scanners or X-ray security scanners may irradiate an X-ray onto a subject, some of which is absorbed by the subject, detecting the remaining transmitted radiation with multiple detectors
Data Source
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AI summary
Provided is an object detection method and system capable of increasing a detection rate for hazardous substances such as liquid explosives as well as existing explosives, the object detection method including (a) preparing an X-ray image of a subject, (b) calculating effective atomic number values (Zeff) by using the X-ray image, and (c) segmenting a target object image from the X-ray image by using the effective atomic number values and energy-band-based multi-energy image reconstruction.