Synthetic Security Imaging Data Using 3D Simulation for Threat Detection
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Solution Overview
Problem
Existing systems for generating training data for threat recognition algorithms in baggage and passenger screening are costly, time-consuming, and resource-intensive, and struggle to capture the variety of real-world scenarios, leading to inaccurate and inefficient detection of prohibited items.
Innovation Solution
A system and method for generating synthetic data using 3D modeling and physics-based simulations to create randomized models, which are then processed to generate simulated image outputs and ground truth annotations, reducing the need for manual scanning and increasing the variety of training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual scanning and real data collection methods are used to generate training data, then the data can capture real-world scenarios, but the process becomes costly, time-consuming, and resource-intensive
Solution Approach 1:
The patent creates synthetic copies of real-world security screening scenarios through 3D modeling and physics-based simulations. Instead of manually scanning physical objects, the system generates virtual representations that replicate real-world conditions, including baggage contents, passenger body scans, and threat item placements. This copying approach maintains detection accuracy while eliminating the time and resource costs of physical data collection
Solution Approach 2:
The system varies multiple parameters in the synthetic data generation process, including object positions, orientations, lighting conditions, and material properties. By systematically changing these parameters across thousands of simulated scenarios, the system captures the diversity of real-world situations without requiring physical rescanning, thus improving detection robustness while reducing time loss
2Reliability
If extensive manual scanning is performed to capture various target object variations, then detection accuracy can be improved, but the cost and resource usage increase significantly
Solution Approach 1:
The patent uses virtual copying to generate diverse training data scenarios. Instead of manually scanning numerous physical variations of baggage and threats, the system creates synthetic copies with varied configurations through software-based 3D modeling. This maintains detection reliability by covering edge cases and rare scenarios while dramatically improving the ease of generating additional training data
Solution Approach 2:
The synthetic data generation system serves multiple functions simultaneously: it generates training data for various threat types, creates ground truth annotations, simulates different scanning conditions, and produces diverse object configurations all through a single automated platform. This multi-functionality improves detection reliability across multiple threat categories while making data generation easier and more efficient
3Measurement precision
If real data is collected through physical scanning, then the data reflects actual conditions, but privacy concerns prohibit manual inspection of certain images
Solution Approach 1:
The patent replaces sensitive real-world images with synthetic copies that preserve the structural and visual characteristics needed for training detection algorithms. Millimeter wave passenger scans and baggage x-ray images are replicated in virtual form, maintaining inspection accuracy while eliminating privacy violations since no actual human subjects or real baggage are involved
Solution Approach 2:
The synthetic data acts as an intermediary between the need for realistic training data and privacy protection requirements. Instead of directly using sensitive real-world images that raise privacy concerns, the system creates intermediate virtual representations that serve the same training function without compromising individual privacy or security protocols
4Productivity
If synthetic data is generated through 3D modeling and simulations, then costs and time are reduced, but the system complexity increases
Solution Approach 1:
While the underlying physics engines and 3D modeling tools are complex, the system presents a simplified interface for generating synthetic data. Users can specify parameters and generate realistic training scenarios without needing to understand the complex simulation physics, thus achieving high productivity while managing system complexity through abstraction layers
Data Source
AI summary
Systems and methods for generating a large volume of synthetic stream-of-commerce security imaging data is disclosed. Methods for creating synthetic baggage x-ray scans, synthetic passenger millimeter wave scans, synthetic passenger video surveillance data, and introducing prohibited items to real security images are also disclosed.