Camera-Preserving Image Augmentation for Robust Model Training
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Solution Overview
Problem
Existing machine learning models struggle to effectively learn from diverse sensor configurations due to variations in camera properties, leading to overfitting and reduced robustness when deployed on different devices.
Innovation Solution
Train models using augmented images that maintain camera properties by applying image manipulation functions like cutouts, which remove and replace image portions with content from similar images, ensuring consistent sensor characteristics across training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data augmentation methods (flipping, rotating, cropping) are applied to account for sensor variations, then model generalization across different camera configurations is improved, but model performance for specific camera configurations deteriorates due to over-generalization
Solution Approach 1:
The patent segments the data augmentation process into two distinct stages: first applying geometric transformations (flipping, rotating, cropping) to account for camera configuration variations, then applying cutout augmentation to maintain specific camera properties. This segmentation allows the model to learn both generalizable features and configuration-specific characteristics without interference between the two augmentation types.
Solution Approach 2:
The patent applies preliminary geometric transformations to the training images before applying the cutout augmentation. By first adjusting the image geometry to match the target camera configuration and then applying cutouts based on the original image properties, the method ensures that the model learns accurate representations for specific configurations while still benefiting from generalization across configurations.
2Reliability
If images are augmented with various sensor characteristics to prevent overfitting, then model robustness is improved, but training complexity increases due to the need to manage multiple sensor configurations
Solution Approach 1:
The patent creates a universal training approach that works across multiple camera configurations by using a single set of training images that are augmented to represent both the target configuration and generalizable features. The cutout augmentation method is designed to be configuration-agnostic, allowing the same training pipeline to serve multiple device types without requiring separate training procedures for each sensor configuration.
Solution Approach 2:
The patent changes the parameters of the training images through controlled augmentations (geometric transformations and cutouts) to create a diverse training set that captures both specific and general characteristics. By adjusting parameters such as cutout size, location, and transformation type, the method generates varied training examples that improve robustness without requiring manual management of multiple sensor configurations.
3Measurement precision
If cutout augmentation is applied to maintain camera properties, then model accuracy for specific configurations is improved, but data diversity is reduced compared to traditional augmentation methods
Solution Approach 1:
The patent merges traditional geometric augmentation (flipping, rotating, cropping) with cutout augmentation in a unified training pipeline. By combining both augmentation types, the method achieves both data diversity from geometric transformations and configuration-specific accuracy from cutout-based augmentation, resolving the trade-off between diversity and precision.
Solution Approach 2:
The patent applies different augmentation strategies to different regions of the training data based on their specific characteristics. By analyzing the local properties of each training image and applying appropriate cutout parameters accordingly, the method maintains high accuracy for specific configurations while preserving overall data diversity through localized optimization of augmentation parameters.
Data Source
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AI summary
Systems and methods for training machine models with augmented data. An example method includes identifying a set of images captured by a set of cameras while affixed to one or more image collection systems. For each image in the set of images, a training output for the image is identified. For one or more images in the set of images, an augmented image for a set of augmented images is generated. Generating an augmented image includes modifying the image with an image manipulation function that maintains camera properties of the image. The augmented training image is associated with the training output of the image. A set of parameters of the predictive computer model are trained to predict the training output based on an image training set including the images and the set of augmented images.