Muon Tomography Vehicle Imaging Autonomous Threat Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current muon tomography systems for vehicle inspection face challenges in efficiently and autonomously processing 3D muon vehicle images to detect potential threat objects without requiring long exposure times, which can lead to false positives and compromised detection accuracy.
Innovation Solution
The implementation of autonomous processing techniques using Data Modeling methods, such as Entropyology and random-field-information science, to analyze voxel data from muon vehicle images, allowing for the identification of threat objects by shape without relying on prior samples and enabling high-speed detection with reduced exposure times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional muon tomography processing methods are used, then detection accuracy can be maintained, but processing time is excessively long and false positives increase
Solution Approach 1:
The patent segments the 3D muon vehicle image into multiple 2D cross-sectional slices. This segmentation allows parallel processing of individual slices through histogram analysis and binning operations, significantly reducing overall processing time while maintaining detection accuracy through systematic analysis of each slice
Solution Approach 2:
The patent performs preliminary histogram analysis and data binning on muon imaging data before final target identification. By pre-processing the data to identify frequency distributions and separate signal from background noise early in the pipeline, the system reduces computational complexity for subsequent processing steps
2Quantity of substance
If longer exposure times are used, then more muon data is collected, but false positives increase and detection reliability decreases
Solution Approach 1:
The patent changes the parameter of data representation by transforming raw muon imaging data into histogram frequency distributions and then into binned data sets. This parameter transformation enables the system to process fewer data points more efficiently, reducing exposure time requirements while maintaining reliable detection through statistical analysis
Solution Approach 2:
The patent replaces traditional mechanical/physical processing methods with Data Modeling techniques including entropy-based analysis and random-field-information science. This substitution enables autonomous identification of threat objects by shape and density characteristics without requiring extensive manual processing or prolonged data collection
3Measurement precision
If manual processing methods are used, then detailed analysis is possible, but automation level is low and processing efficiency decreases
Solution Approach 1:
The patent implements self-service through autonomous Data Modeling techniques that automatically perform histogram analysis, data binning, background recognition, and target identification without human intervention. The system serves itself by using embedded algorithms to process muon imaging data, identify vehicles, detect threat objects, and determine their shapes and locations autonomously
Solution Approach 2:
The patent transforms the processing approach by changing from manual visual inspection parameters to automated computational parameters including histogram frequencies, bin distributions, and Data Modeling metrics. This parameter change enables high-level automation while preserving detailed analysis capabilities through systematic computational methods
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
This approach enables efficient and robust autonomous identification of threat objects within vehicles, providing accurate detection without the need for prolonged exposure times, thus enhancing the speed and reliability of muon tomography imaging systems.
Implementation Method 1
As a muon moves through material, Coulomb scattering of the charges of subatomic particles perturb its trajectory. The total deflection depends on several material properties, but the dominant parameters are the atomic number, Z, of the nuclei and the material density.
Implementation Method 2
Energetic muons interact strongly enough with matter by ionization to be easily detected, and can penetrate large thicknesses without significant impairment.
Data Source
AI summary
Techniques, apparatus and systems for muon tomography vehicle imaging use autonomous processing of 3-dimensional muon tomography vehicle images based on Data Modeling techniques and various applications including analyzing vehicle voxel data such as muon vehicle images to detect potential threat objects and then to further discriminate the identified potential threat objects by shape.