Control-Conditioned Image Augmentation for Rare-Object Analysis
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Solution Overview
Problem
Conventional approaches for establishing image analysis models in autonomous driving face challenges such as high implementation costs, inflexible control conditions, and the generation of unrealistic samples, particularly in low-visibility and rare object scenarios, leading to poor performance and overfitting.
Innovation Solution
A system and method that includes a processing unit and storage unit to execute a sample generation process, filtering process, and model establishment process, using control conditions to generate and filter samples, ensuring diversity, realism, and quality, thereby enhancing adaptability and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image processing techniques (noise reduction, blur reduction, brightness enhancement) are applied to low visibility scenes, then image clarity is improved, but computational resources and time requirements increase significantly
Solution Approach 1:
The patent creates synthetic copies of real images by extracting features (edges, textures, colors) from clear template images and applying them to low-visibility scene images. This copying approach improves image clarity without requiring computationally intensive processing, as the synthesis operation is much lighter than full image processing
Solution Approach 2:
The patent segments the image processing task into separate feature extraction and feature application stages. By extracting edges, textures, and color information as independent features from template images and then applying them to target images, the system achieves clarity enhancement with reduced computational cost compared to processing the entire image at once
2Adaptability or versatility
If oversampling or undersampling is applied to adjust the proportion of rare target objects in training data, then sample distribution is improved, but model overfitting increases due to excessive homogeneous samples
Solution Approach 1:
The patent applies local quality by preserving the original image's local characteristics (texture, color distribution, lighting conditions) while only modifying the object content. This ensures that generated samples have diverse local features that prevent overfitting, while still maintaining the desired sample distribution for rare objects
Solution Approach 2:
The patent creates composite samples by combining the structural framework of real images with synthesized object content. The resulting composite images maintain the original image's quality attributes while introducing varied object instances, achieving both proper sample distribution and diversity to prevent overfitting
3Quantity of substance
If GANs are used to generate additional sample images, then sample size is increased, but training stability deteriorates and convergence becomes difficult
Solution Approach 1:
Instead of using GANs to generate images from scratch, the patent copies existing real images by extracting and reapplying their features. This copying approach avoids the training stability issues of GANs while still increasing sample size, as it works with existing image data rather than requiring complex generative model training
Solution Approach 2:
The patent uses a simpler, more stable feature extraction and application mechanism instead of complex GANs. The feature extraction and synthesis operations are computationally lighter and more stable than GAN training, providing a reliable alternative for sample generation that doesn't require careful hyperparameter tuning or face training convergence
4Manufacturing precision
If generated sample images undergo denoising and edge-smoothing processing, then image quality is improved, but processing time and complexity increase
Solution Approach 1:
The patent performs feature extraction and synthesis operations in advance during the sample generation phase, so that the images are pre-processed and optimized before being used for training. This preliminary action eliminates the need for subsequent denoising and edge-smoothing steps, saving processing time while maintaining image quality
Solution Approach 2:
By copying features from template images during generation, the patent creates clean, well-defined images that require minimal post-processing. The synthesized images inherit the quality characteristics of the template images, eliminating or reducing the need for time-consuming denoising and edge-smoothing operations
Data Source
AI summary
A data augmentation method for establishing an image analysis model is provided, including the first step and the second step. The first step includes inputting a set of control conditions into an image generation model to obtain a generated image that is generated by the image generation mode based on the control conditions. The set of control conditions includes a template image and control text, where the control text contains a first prompt associated with a specific scene. The second step includes composing a generated sample with the generated image and label data that correspond to the template image. The method further includes selectively excluding generated samples based on a set of filtering conditions and adding the remaining generated samples to the training dataset for establishing the analysis model.