Composite Image Generation With Content-Event Inputs for Defense AI
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Solution Overview
Problem
The defense industry faces a challenge in securing sufficient training data for AI models due to high costs and long times required, limiting the effectiveness of AI technology applications.
Innovation Solution
A method and system for generating composite images using an artificial neural network model that integrates content and event information, enabling the creation of high-quality images in a specific domain style, such as IR, 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 effectiveness is improved, but cost and time requirements increase significantly
Solution Approach 1:
The patent uses generative AI to create synthetic training data that copies the essential characteristics and patterns of real defense industry data without requiring actual field data collection. The system generates composite images representing battlefield scenarios, equipment operations, and threat situations that can serve as effective training data substitutes, thereby reducing time and cost while maintaining model training effectiveness
Solution Approach 2:
The system performs preliminary data preparation by generating synthetic training data in advance through automated composite image generation. This allows training data to be prepared beforehand without waiting for actual field data collection, enabling faster model development and deployment cycles while maintaining data quality and relevance
2Reliability
If actual training data is secured for AI models in the defense industry, then model training effectiveness is improved, but cost increases significantly
Solution Approach 1:
The patent uses generative AI to create synthetic training data that copies the essential characteristics and patterns of real defense industry data without requiring actual field data collection. The system generates composite images representing battlefield scenarios, equipment operations, and threat situations that can serve as effective training data substitutes, thereby reducing time and cost while maintaining model training effectiveness
Solution Approach 2:
The system generates inexpensive synthetic data that can be produced in large quantities at low cost through automated composite image generation. These synthetic training samples serve as disposable or reusable data assets that eliminate the need for expensive actual field data collection while providing sufficient training material for AI model development
3Productivity
If composite images are generated using generative AI, then data acquisition cost and time are reduced, but image quality and realism must be maintained
Solution Approach 1:
The system employs dynamic parameter adjustment in the composite image generation process, allowing the generative AI model to adaptively optimize image quality based on specified criteria. The system can dynamically adjust generation parameters such as resolution, detail level, and realism characteristics to balance production efficiency with image quality requirements for different training scenarios
Solution Approach 2:
The system incorporates feedback mechanisms where generated composite images are evaluated against quality standards and real data characteristics. This feedback loop allows the generative AI to iteratively improve image quality by comparing synthetic outputs with reference data and adjusting generation parameters accordingly, ensuring that productivity gains do not compromise manufacturing precision
Data Source
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AI summary
An example image generation method includes acquiring first content information representing structural information of objects to be generated in a composite image, receiving first event information associated with a specific event to be generated in the composite image, generating the composite image in a first domain style based on the first content information and the first event information by using an artificial neural network model, and outputting the composite image in the first domain style.