Content-Guided Composite Image Generation for Defense AI Training
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Solution Overview
Problem
The defense industry faces a severe shortage of training data for AI models, which is costly and time-consuming to obtain.
Innovation Solution
A method and system for generating composite images using an image generation model that combines background and specific object images, extracting content information, and training the model with multiple pieces of content information to create images of a specific domain style, such as infrared, for use as training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If actual training data is secured for AI models in the defense industry, then model training quality is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent uses image generation models to create synthetic training data that copies the essential characteristics and patterns of real defense industry images. The model learns from a small set of actual training data and generates numerous synthetic samples that preserve the statistical properties and visual features needed for effective model training, thereby achieving good training quality without requiring large amounts of actual data.
Solution Approach 2:
The patent performs preliminary training of the image generation model using a small subset of actual training data before generating synthetic samples. This preliminary action allows the model to learn the underlying data distribution and patterns, which are then used to generate high-quality synthetic training data, reducing the need for extensive actual data collection and preparation time.
2Reliability
If actual training data is secured for AI models in the defense industry, then model training quality is improved, but cost increases significantly
Solution Approach 1:
The patent uses image generation models to create synthetic training data that copies the essential characteristics and patterns of real defense industry images. The model learns from a small set of actual training data and generates numerous synthetic samples that preserve the statistical properties and visual features needed for effective model training, thereby achieving good training quality without requiring large amounts of actual data.
Solution Approach 2:
The patent replaces expensive and difficult-to-obtain actual training data with inexpensive synthetic data generated by the image generation model. The synthetic data serves as a disposable substitute that can be generated on-demand without the high costs associated with collecting, annotating, and managing real defense industry images.
3Manufacturing precision
If multiple pieces of content information are extracted and used for training, then image generation quality is improved, but processing complexity increases
Solution Approach 1:
The patent segments the content information extraction process into multiple independent modules, each responsible for extracting a specific type of information (e.g., semantic segmentation, edge detection, depth estimation). This segmentation allows for parallel processing of different information types and simplifies the overall system architecture, making it easier to manage and train despite the multiple information streams.
Solution Approach 2:
The patent merges multiple extracted content information types into a unified representation that is fed into the image generation model. By combining semantic segmentation, edge information, depth data, and other features into a cohesive input format, the system achieves high-generation quality while maintaining manageable processing complexity through integrated processing pipelines.
Data Source
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AI summary
The present disclosure relates to an image generation method performed by at least one processor. The image generation method may include: receiving an input image including a background and a specific object; extracting at least one piece of content information about the input image; and generating a composite image of a specific domain style associated with the at least one piece of content information by using an image generation model.