Synthetic RF Image Generation for ML Training Data
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Solution Overview
Problem
Current full-body security scanning technologies face challenges in generating high-quality RF imaging data for machine learning, which requires extensive and costly manual data collection, and raises concerns about personal and data privacy.
Innovation Solution
A process and device for generating synthetic RF images using a 3D human model, simulated through electromagnetic transformations, allowing for the creation of diverse images representing various angles, constitutions, postures, and movements, reducing the need for manual data collection and enhancing data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection with real human beings is used, then high-quality RF imaging data for machine learning is obtained, but the process becomes tedious, time-consuming and costly
Solution Approach 1:
The patent creates synthetic RF images by copying and transforming 3D body models through electromagnetic simulation, rather than collecting data from real human subjects. This generates unlimited training data without the time and cost constraints of manual collection, while maintaining the physical accuracy needed for machine learning training.
Solution Approach 2:
The patent pre-computes electromagnetic transformations and stores them as lookup tables before actual image generation is needed. This preliminary action allows rapid synthesis of RF images during training without performing time-consuming electromagnetic simulations in real-time, resolving the contradiction between data quality and collection time.
2Quantity of substance
If manual data collection with real human beings is used, then sufficient training data is obtained, but costs increase significantly
Solution Approach 1:
The patent uses synthetic 3D body models and electromagnetic simulations to generate training data copies, eliminating the need for expensive manual data collection campaigns. This approach provides unlimited training data quantity at minimal computational cost compared to recruiting and scanning real human subjects.
Solution Approach 2:
The patent creates a universal 3D body model framework that can generate diverse RF images representing different body types, postures, and conditions through parameter variation. This single versatile system replaces the need for multiple separate data collection campaigns针对不同body types, significantly reducing overall costs while providing comprehensive training data.
3Measurement precision
If real human beings are used for data collection, then authentic RF imaging data is obtained, but personal and data protection rights are compromised
Solution Approach 1:
The patent replaces real human subjects with synthetic 3D body models to generate RF imaging data. This copying approach maintains data authenticity for machine learning training while completely eliminating privacy risks associated with collecting data from real individuals, as the models are abstract representations without personal identifiers.
Solution Approach 2:
The patent introduces synthetic 3D body models as an intermediary between the need for authentic training data and the requirement for privacy protection. These models serve as a mediator that provides physically accurate RF imaging characteristics without exposing or storing any personal information from real human subjects.
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 the rapid generation of large quantities of labeled RF images, reducing costs and time, while ensuring data privacy, and allows for efficient training and development of machine learning algorithms for full-body security scanning.
Implementation Method 1
generating the at least one synthetic RF image in accordance with an imaging transformation of the 3D body sample
Data Source
AI summary
Disclosed are a process (1) and a device (2) for generating at least one synthetic radio frequency, RF, image for machine learning. The process (1) comprises: having (11) a three-dimensional, 3D, body model of a human; sampling (12) the 3D body model; and generating (13) the at least one synthetic RF image in accordance with an imaging transformation of the 3D body sample. This provides labeled data in the form of RF images for training of machine learning algorithms.