Synthetic Image Augmentation with Context Preservation
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Solution Overview
Problem
Current methods for generating training data for deep learning models, such as image recognition, lack diversity and realism, particularly in incorporating real-world imperfections like rust, dirt, or damage, which can lead to models performing poorly in real-world scenarios.
Innovation Solution
A synthetic augmentation system that uses a neural network pipeline to introduce realistic modifications or augmentations, like defects, to images while preserving important contextual information. This system involves removing essential content like text from images, processing the images with a generative diffusion model, and then blending the original content back into the augmented image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generative diffusion models are used to synthesize images with real-world imperfections, then the realism and diversity of training data is improved, but the contextual information and text content on objects may be undesirably modified or lost
Solution Approach 1:
The image processing is divided into distinct segments: text detection and masking, diffusion model application to the masked regions, and text reintegration. This segmentation allows the diffusion model to generate realistic imperfections only in non-text areas while preserving text content intact.
Solution Approach 2:
Text content is extracted and masked from the image before applying the diffusion model. This extraction prevents the diffusion model from modifying or distorting text, while still allowing realistic imperfections to be generated in the remaining image areas.
2Adaptability or versatility
If more diverse and imperfect training data is generated, then the robustness of deep learning models is improved, but the complexity of the image processing pipeline increases
Solution Approach 1:
Text detection and masking are performed as preliminary actions before applying the diffusion model. This preliminary preparation simplifies the subsequent diffusion process by clearly defining which regions should be modified and which should be preserved, making the overall pipeline more manageable despite its multiple steps.
Solution Approach 2:
A mask serves as an intermediary between the original image and the diffusion model output. The mask guides the diffusion process to apply imperfections only where appropriate while protecting text regions, simplifying the integration of multiple processing steps into a coherent pipeline.
Data Source
AI summary
Approaches presented herein are directed to generating synthetic images with one or more augmentations realistically added to objects in the images, while ensuring the preservation and integrity of semantic or contextual information within the image. A synthetic augmentation system may identify and extract foreground image data (e.g., text), and a version of the image with the foreground image data removed can be processed by a generative diffusion model. One or more inputs can be provided to specify aspects such as a type or strength of augmentation to be performed. After an augmented image is generated using the generative diffusion model, the previously removed text can be blended back into the image. A synthetic augmentation system may use one or more blending weights for the text, such as a defect blending weight and a letter blending weight. The final result is a synthetic image with added realistic augmentations that preserves the semantic content.