Airborne Trajectory Data Synthesis for Faster Vision Model Training
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Solution Overview
Problem
Current methods for generating training data for machine learning models in computer vision applications, particularly for airborne object trajectory data, are time-consuming, expensive, and potentially hazardous, relying on manual annotation and simulation of various scenarios.
Innovation Solution
The system transforms actual trajectory data from a single encounter scenario into synthetic trajectory data for different environments, allowing for the automated generation of training data by overlaying transformed trajectories onto imaging data, and modifying these trajectories to simulate various flight maneuvers and external conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual annotation and simulation methods are used to generate training data, then accuracy and reliability of training data can be maintained, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent uses actual trajectory data from real airborne objects as templates to generate synthetic trajectory data. By copying the structure and characteristics of real trajectories and transforming them through mathematical operations, the system creates realistic training data without manual annotation, thus maintaining accuracy while reducing time consumption
Solution Approach 2:
The system pre-processes actual trajectory data into transformed trajectory data that can be directly applied to imaging data. This preliminary transformation creates a reusable library of trajectory patterns that can be quickly applied to generate multiple training scenarios without repeating the annotation process
2Measurement precision
If manual annotation processes are used to create training data, then data accuracy is maintained, but the cost increases significantly
Solution Approach 1:
Instead of manually annotating each training sample, the system copies actual trajectory data and transforms it synthetically. This approach preserves the measurement precision of real trajectories while eliminating the costly manual annotation process, as the transformation algorithms automatically generate accurate trajectory labels
Solution Approach 2:
The system uses actual trajectory data from airborne objects to automatically generate its own training data without external manual intervention. The transformation process is self-contained, using the actual data to create synthetic variations that serve as training samples, eliminating the need for expensive human annotators
3Adaptability or versatility
If diverse trajectory scenarios are generated through manual simulation, then model versatility is improved, but the complexity of the generation process increases
Solution Approach 1:
The system generates diverse trajectory scenarios by transforming actual trajectory data through various parameter changes including coordinate system transformations, temporal transformations, and spatial transformations. These parameter modifications create different trajectory patterns and scenarios from a single actual trajectory, achieving versatility through mathematical transformations rather than complex simulation logic
Data Source
AI summary
Systems and methods to automatically generate training data including airborne object trajectory data may receive real trajectory data from within a first environment, receive imaging data associated with a second environment, and transform and/or modify the real trajectory data to synthetic trajectory data for the second environment. Then, the synthetic trajectory data may be superimposed within the imaging data of the second environment. In addition, images of an airborne object may be rendered along the synthetic trajectory data to generate training data that may be used to train machine learning models or algorithms for various computer vision applications.