Dynamic Computational Load Adjustment for Luggage Screening
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Solution Overview
Problem
Current luggage screening systems experience high rates of false positives, leading to increased screening times and the need for additional inspections, as they lack the capability for efficient and cost-effective high-throughput automatic detection of harmful objects within luggage or parcels.
Innovation Solution
A system comprising an acquisition subsystem with scanning detectors, a reconstruction subsystem for image data reconstruction, a computer-aided detection subsystem for analysis, and a feedback loop to dynamically adjust computational load, utilizing risk variables to prioritize investigative parameters and incorporate additional modalities for enhanced identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current screening systems are used, then security screening can be performed, but false positives are high and screening time increases substantially
Solution Approach 1:
The system dynamically adjusts computational load and inspection depth based on risk assessment. High-risk items receive more intensive analysis while low-risk items are quickly cleared, enabling the system to adapt its processing intensity to the actual needs of each item and thereby reducing overall screening time without compromising security.
Solution Approach 2:
The system incorporates feedback loops that continuously learn from previous inspections and adjust detection parameters. This feedback mechanism allows the system to refine its detection algorithms over time, reducing false positives and improving accuracy while maintaining efficient screening speeds.
2Productivity
If high-throughput screening is implemented, then screening speed increases, but detection precision may deteriorate
Solution Approach 1:
The system dynamically allocates computational resources based on the complexity and risk level of each item. Simple items are processed quickly with standard algorithms, while complex items trigger enhanced analysis modes, allowing the system to maintain both high throughput and high precision without sacrificing either aspect.
Solution Approach 2:
The system applies different levels of detection precision to different regions and items based on their risk characteristics. Rather than applying uniform high precision to all items (which would reduce throughput), the system concentrates intensive analysis only where needed, maintaining overall precision while enabling high throughput.
3Reliability
If computational load is increased for better detection, then detection accuracy improves, but system processing time increases
Solution Approach 1:
The system dynamically adjusts computational load based on risk assessment and item characteristics. Instead of consistently applying maximum computational intensity, the system scales processing power up or down in real-time, achieving high accuracy when needed while minimizing processing time for low-risk items.
Solution Approach 2:
The system applies partial computational action sufficient to achieve detection goals without excessive processing. By using risk-based triage, the system applies intensive analysis only to items that require it, avoiding wasted computational resources on items that can be quickly cleared with standard processing.
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
Enables efficient screening of up to one thousand pieces of luggage per hour with a reduced number of false positives, allowing for rapid and accurate detection of harmful objects, such as explosive devices, by integrating risk variables and multi-stage reconstruction and analysis processes.
Implementation Method 1
an acquisition subsystem having a scanning device with at least one scanning detector for acquiring view data of the contents of an article
Data Source
AI summary
A system and method for ascertaining the identity of an object within an enclosed article. The system includes an acquisition subsystem, a reconstruction subsystem, a computer-aided detection (CAD) subsystem, and an alarm resolution subsystem. The acquisition subsystem communicates view data to the reconstruction subsystem, which reconstructs it into image data and communicates it to the CAD subsystem. The CAD subsystem analyzes the image data to ascertain whether it contains any area of interest. A feedback loop between the reconstruction and CAD subsystems allows for continued, more extensive analysis of the object. Other information, such as risk variables or trace chemical detection information may be communicated to the CAD subsystem to dynamically adjust the computational load of the analysis.