Synthetic Edge-Case Image Generation for Vision Model Training
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Solution Overview
Problem
AI-based perception systems in autonomous roadway safety systems struggle with rare and novel visual scenarios due to a lack of edge case data, leading to poor performance and safety risks.
Innovation Solution
A system utilizing AI language and text-to-image models generates synthetic edge case images through iterative prompting, adjusting complexity based on model performance, to enhance training data for computer vision models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real edge case data are collected for training, then model performance in rare scenarios improves, but data acquisition becomes extremely difficult and time-consuming
Solution Approach 1:
The patent uses text-to-image AI models to generate synthetic copies of edge case images based on text prompts describing rare scenarios. Instead of collecting real edge case data, the system creates artificial replicas that mimic the characteristics of rare visual scenarios, thereby avoiding the time-consuming data collection process while still providing training data for improving model reliability in edge cases.
Solution Approach 2:
The system performs preliminary generation of edge case training data using AI models before actual model training occurs. By pre-generating synthetic edge case images with diverse rare scenarios through text prompts, the system prepares training data in advance, eliminating the need for time-consuming real-world data collection and enabling immediate model training on comprehensive edge case scenarios.
2Adaptability or versatility
If more diverse edge case scenarios are included in training data, then model robustness improves, but the complexity of data collection and processing increases
Solution Approach 1:
The patent replaces complex mechanical and manual data collection systems with AI-based text-to-image generation systems. Instead of physically capturing diverse edge case scenarios through sensors and cameras in various conditions, the system uses language models and image generation models to synthetically create diverse scenarios from text descriptions, dramatically simplifying the data collection and processing complexity while maintaining model robustness.
3Quantity of substance
If synthetic data is generated using AI models, then data availability for rare scenarios increases, but the realism and authenticity of training data may decrease
Solution Approach 1:
The patent implements a feedback mechanism where generated synthetic images are evaluated for their quality and realism, and this feedback is used to refine the text prompts and generation parameters. The system iteratively improves the realism of synthetic images by analyzing generation results and adjusting prompts to better capture authentic edge case characteristics, thereby maintaining manufacturing precision while increasing data quantity.
Solution Approach 2:
The system adjusts various parameters in the text-to-image generation process, including prompt complexity, image resolution, stylistic modifiers, and generation settings, to optimize the realism of synthetic edge case images. By carefully tuning these parameters, the system produces synthetic data that closely mimics real-world edge case scenarios, maintaining high manufacturing precision while generating abundant training data.
Data Source
AI summary
A method for generating training data for a computer vision model can comprise providing an AI language model with first prompt data indicating visual scenarios to be evaluated by the computer vision model, generating, using the AI language model, based on the first prompt data and a prompting policy, second prompt data configured to cause an AI text-to-image model to generate images associated with the visual scenarios, generating the images using the second prompt data and the AI text-to-image model, applying the computer vision model to each image to generate, for each of the images, respective object detection data, and generating, for each image, performance data characterizing an effectiveness of the computer vision model, updating the prompting policy based on the performance data, and generating updated second prompt data based on the updated prompting policy.