Object Detection Model Training Using Geographic Transformations
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Solution Overview
Problem
Machine learning models trained for detecting objects in one geographic region often struggle to maintain high accuracy when applied to different regions due to varying properties such as object types, colors, and background content.
Innovation Solution
The method involves transforming training data using geographic properties like color ranges and object occurrences to create augmented training data, which includes transformations like pixel group translation, two-dimensional rotation, changed backgrounds, and object obfuscation, to improve model accuracy across different regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training data is transformed using geographic properties to create region-specific models, then detection accuracy in diverse geographic regions is improved, but data processing complexity and training time increase
Solution Approach 1:
The patent applies transformations to training data in advance before model training, including pixel group translations, two-dimensional rotations, background changes, and object obfuscations. This preliminary transformation creates augmented training datasets that pre-adapt models to various geographic conditions, eliminating the need for complex runtime adjustments and reducing operational complexity while improving detection accuracy across diverse regions
Solution Approach 2:
The patent systematically varies multiple parameters in training data transformations including spatial transformations (translations, rotations), background properties (color ranges, geographic features), and object properties (obfuscation levels, orientations). By training models on data with diverse parameter variations, the system improves reliability across different geographic regions without requiring complex runtime processing
2Adaptability or versatility
If multiple transformations are applied to create augmented training data, then model robustness across diverse environments is improved, but training time and computational resources increase
Solution Approach 1:
The patent performs all necessary data transformations and augmentations during the training phase, applying pixel group translations, rotations, background changes, and obfuscations to create comprehensive training datasets. This preliminary action ensures models are pre-adapted to diverse environmental conditions, achieving high robustness without requiring additional computational resources or time during runtime operations
Solution Approach 2:
The patent introduces dynamic variations in training data by applying random transformations within specified ranges (e.g., rotation angles, translation distances, background color variations). This dynamic augmentation creates diverse training scenarios that improve model adaptability to unseen environmental conditions while maintaining efficient training through systematic parameter sampling
3Measurement precision
If region-specific training data is created using geographic properties, then detection accuracy in target regions is improved, but data preparation complexity increases
Solution Approach 1:
The patent implements automated pipelines that extract geographic properties from region data and automatically apply appropriate transformations to create augmented training datasets. The system self-configures transformation parameters based on detected geographic characteristics (such as dominant background colors, typical object orientations, and environmental features), eliminating manual data preparation while improving detection accuracy for target regions
Solution Approach 2:
The patent automatically adjusts transformation parameters based on geographic region properties, including spatial transformations adapted to regional object orientations, background color variations matching local environments, and obfuscation levels appropriate to regional conditions. This automated parameter adaptation simplifies data preparation while achieving region-optimized detection accuracy
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a model to detect objects. One of the methods includes maintaining first training data that represents at least a plurality of first images of invertebrate fossils; determining, using at least one or more geographic properties of a geographic region, one or more transformations from a group comprising: a pixel group translation, a pixel group two-dimensional rotation, a changed background, or an object obfuscation; creating, using the one or more transformations and the first training data, second training data that represents at least a plurality of second images of invertebrate fossils; training, using the second training data, a machine learning model to detect invertebrate fossils in images of the geographic region; and providing, to a system, the trained machine learning model to enable the system to detect invertebrate fossils in images of the geographic region.