X-Ray Object Detection Using Zeff Segmentation for Liquid Hazards
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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 using X-ray image processing and effective atomic number values, combined with energy-band-based multi-energy image reconstruction and artificial neural networks, to segment and identify liquid substances by calculating Zeff values and predicting their properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CT scanners use shape-based object specification and property inference, then the detection process is simple and fast, but the detection accuracy for liquid hazardous substances and narcotics is very low
Solution Approach 1:
The patent transforms the detection approach from shape-based to material-property-based by calculating effective atomic number (Zeff) values and electron density for each pixel in the X-ray image. This parameter transformation enables differentiation of liquid hazardous substances from other materials based on their intrinsic atomic properties rather than external shape characteristics.
Solution Approach 2:
The patent segments the image processing into multiple energy bands (e.g., low energy band and high energy band) and performs separate Zeff calculations for each band. By comparing Zeff values across different energy bands, the system can more accurately identify and characterize liquid hazardous substances and narcotics, resolving the contradiction between detection accuracy and process complexity.
2Measurement precision
If CT scanners rely on external shape characteristics, then the detection method is straightforward, but liquid substances in containers cannot be accurately identified
Solution Approach 1:
The patent calculates effective atomic number (Zeff) values for each pixel by analyzing X-ray attenuation at different energy levels. This parameter change from shape-based to atomic-number-based detection enables accurate identification of liquid substances within containers, as Zeff values are intrinsic to the material composition rather than dependent on container shape.
Solution Approach 2:
The patent introduces effective atomic number calculation as an intermediary step between X-ray image acquisition and substance identification. By using Zeff values as a mediator that reflects the atomic composition of materials, the system can penetrate through container walls and accurately identify liquid hazardous substances and narcotics based on their material properties rather than container characteristics.
3Measurement precision
If multi-energy image reconstruction and effective atomic number calculation are implemented, then detection accuracy for hazardous substances increases significantly, but processing time and computational complexity increase
Solution Approach 1:
The patent segments the energy spectrum into distinct energy bands (low energy and high energy bands) and performs separate image reconstruction and Zeff calculation for each band. This segmentation allows parallel processing of different energy components, improving computational efficiency while maintaining high detection accuracy for hazardous substances through multi-energy analysis.
Solution Approach 2:
The patent performs preliminary image reconstruction and Zeff calculation for each energy band before conducting the final hazardous substance identification. By pre-processing the data into energy-specific segments with their respective Zeff values, the system reduces the computational burden during the final detection phase, thereby reducing overall processing time while maintaining high accuracy.
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 rate for hazardous substances, including liquids, by segmenting and identifying containers and contents using multi-view images.
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
Implementation Method 3
calculating effective atomic number values (Zeff) by using the X-ray image
Implementation Method 4
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
Data Source
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.