Baggage Screening Device Using Multi-Scan Risk Estimation
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Solution Overview
Problem
Current x-ray cabin baggage screening systems are inefficient for high-throughput applications due to low belt speeds, the need for human interpretation, high costs, and high false alarm rates, necessitating a low-cost, compact, and automated solution for quickly and accurately classifying items as threats or benign.
Innovation Solution
A multi-scan device combining an x-ray scanner for object detection, a non-ionizing radiation scanner for volume estimation, and a radar scanner for physical dimension analysis, with a processor using machine learning to generate a risk estimation for baggage items, allowing for rapid and accurate threat classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional x-ray cabin baggage screening systems are used, then security screening can be performed, but the throughput is low and belt speeds are slow
Solution Approach 1:
The patent replaces the traditional mechanical conveyor belt system with a automated scanning system that uses multiple scanners (x-ray, optical depth sensor, radar) to rapidly acquire and process bag data. This substitution eliminates the need for slow mechanical conveyor movement while maintaining security screening capabilities, achieving high throughput of 1200 bags per hour.
Solution Approach 2:
The system continuously acquires data from multiple scanners simultaneously as bags move through the scanning region, rather than stopping for sequential analysis. The conveyor operates at high speed (0.4-0.8 m/s) while scanners continuously capture images, and the processor continuously analyzes data in real-time, eliminating idle time and maximizing throughput.
2Measurement precision
If human screeners interpret baggage images, then complex items can be analyzed, but the cost and time required increase
Solution Approach 1:
The system performs self-service by automatically analyzing bag contents using machine learning algorithms that process data from multiple scanners. The processor automatically classifies bags as threat or benign without human intervention, achieving both high accuracy and rapid processing. The system self-calibrates and maintains detection standards without requiring human screeners.
Solution Approach 2:
The system creates a comprehensive digital copy of the bag contents through multi-scan imaging (x-ray, optical depth, radar) and analyzes this digital representation using machine learning. This digital copying replaces human visual inspection, enabling rapid automated analysis while maintaining detection accuracy through sophisticated image processing algorithms.
3Productivity
If conventional x-ray systems are used, then baggage can be scanned, but the machinery is large and bulky
Solution Approach 1:
The patent divides the scanning function into separate modular scanner components (x-ray scanner, optical depth sensor, radar scanner) that can be independently positioned and optimized. Each scanner focuses on specific characteristics (density, volume, dimensions), allowing for a more compact overall system architecture that maintains comprehensive screening capability while reducing machinery bulk.
Solution Approach 2:
The system transitions from traditional single-plane x-ray imaging to multi-dimensional data acquisition using optical depth sensing and radar. This dimensional expansion allows for more efficient use of space by capturing volumetric and depth information simultaneously, enabling compact system design while maintaining comprehensive detection capabilities.
4Reliability
If conventional screening systems operate, then security can be maintained, but false alarm rates are high
Solution Approach 1:
The machine learning system continuously refines its classification algorithms based on feedback from actual screening results. The system learns from positive and negative examples to improve its discrimination between threats and benign items, progressively reducing false alarms while maintaining security reliability. The processor provides feedback loops for algorithm optimization based on screening outcomes.
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
The system achieves high throughput, accurately classifying items in under 3 seconds with a rate of 1200 bags per hour, significantly reducing false positives and operational costs while maintaining high accuracy.
Implementation Method 1
an x-ray scanner, configured to scan an item of baggage using x-ray radiation and generate therefrom data indicative of objects within the item of baggage
Implementation Method 2
a second scanner, configured to estimate a volume of the item of baggage
Implementation Method 3
a third scanner, configured to estimate a physical dimension of at least one object within the item of baggage, wherein the third scanner uses non-ionising radiation
Implementation Method 4
The conveyor may be configured to move the item of baggage at a speed greater than 0.2 m/s, greater than 0.3 m/s, greater than 0.4 m/s, greater than 0.5 m/s, or at least 0.6 m/s
Data Source
AI summary
A device, a method, and a kit for screening items of baggage. The device comprising: an x-ray scanner, configured to scan an item of baggage using x-ray radiation, and generate therefrom data indicative of objects within the item of baggage; a second scanner configured to estimate a volume of the item of baggage; a third scanner, configured to estimate a physical dimension of at least one object within the item of baggage, wherein the third scanner uses non-ionising radiation; and a processor, configured to utilize the data indicative of objects within the item of baggage, the estimated volume, and the estimated physical dimension of at least one object within the item of baggage to generate a risk estimation for the item of baggage.