Generative Diffusion Model for Synthetic Damaged Sign Dataset Creation
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Solution Overview
Problem
Current diffusion models are ill-equipped for real-world applications due to their reliance on large datasets, making it impractical to train models for rare scenarios encountered in real environments.
Innovation Solution
A system and method for generating datasets of rare scenarios, specifically damaged signs, using a generative diffusion model that processes image data, class information, and damage information with machine learning algorithms to produce output features and generate image data of damaged signs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diffusion models are trained on large datasets containing hundreds of millions of images, then the model provides sufficient information regarding structure and appearance of a large variety of objects, but it becomes impractical to train models for rare scenarios encountered in real environments
Solution Approach 1:
The system performs preliminary actions by generating synthetic damaged sign images before actual training occurs. A generative diffusion model creates a comprehensive dataset of damaged signs with various damage types (cracks, fading, occlusions, deformations) that can be used to pre-train or fine-tune detection models, eliminating the need to collect rare real-world damaged sign images
Solution Approach 2:
The system creates copies of undamaged signs and systematically applies damage transformations to generate synthetic damaged sign images. The generative model produces realistic copies of damaged signs that mimic real-world variations, allowing training without requiring actual physical damaged signs for every possible scenario
2Ease of manufacture
If diffusion models are trained on insufficient datasets, then training becomes practical and feasible, but the model becomes ill-equipped for real world applications
Solution Approach 1:
The system changes parameters by systematically varying damage parameters (damage type, damage severity, damage position, sign class) in the synthetic data generation process. This creates a diverse training dataset from limited inputs, enabling models trained on small datasets to achieve real-world applicability by exposing them to numerous parameter variations during training
3Adaptability or versatility
If datasets are collected for rare scenarios of real environments, then broader real world applications become possible, but data collection becomes impractical or impossible
Solution Approach 1:
Instead of collecting rare real-world damaged sign images through time-consuming field data collection, the system creates synthetic copies using generative AI. The diffusion model generates realistic damaged sign images for rare scenarios (e.g., specific damage types on specific sign classes) instantly, eliminating the need for physical data collection campaigns
Solution Approach 2:
The system performs preliminary data generation by creating comprehensive datasets of rare damaged sign scenarios before they are needed for training. This advance preparation eliminates the need for time-consuming data collection in the field, as all rare scenarios can be synthesized on-demand using the generative model
Data Source
AI summary
A system for generating a dataset of damaged signs includes at least one computer configured to receive a first set of image data indicating damaged signs from a first set of classes and undamaged signs from a second set of classes, receive class information indicating a class type for each sign, and receive damage information indicating a damage type for each of the damaged signs. The at least one computer is also configured to process the first set of image data, the class information, and the damage information with a learning algorithm to produce output features, and generate output image data indicating an output sign from the second set of classes using a generative diffusion model that processes the output features, where the output image data indicates a damage type matching the damage type of at least one of the plurality of damaged signs.