Simulated Training Images for Machine Learning Object Detection
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Solution Overview
Problem
Training machine learning models requires a large and diverse dataset, which can be time-consuming to collect, especially for rare classes and edge cases, and is challenging due to difficulties in data annotation and replicating real-world conditions.
Innovation Solution
The use of simulated images generated by a simulator and rendering engine to create a training dataset, combined with real-world data, to train machine learning models for detecting objects in unannotated input images, including initializing a simulation environment, adding simulated objects, and modifying scene configuration parameters to enhance visual diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world data collection is used to train machine learning models, then the training data covers authentic real-world conditions, but the data collection process is time-consuming and difficult to annotate
Solution Approach 1:
The patent creates simulated training images that copy real-world scenes, objects, and conditions through a simulation environment. These synthetic images replicate authentic visual data including rare classes and edge cases without requiring physical data collection, thereby maintaining data authenticity while eliminating time-consuming field work and annotation processes.
Solution Approach 2:
The simulation environment pre-generates diverse training images covering all possible classes and edge cases before actual model training begins. This preliminary action ensures comprehensive coverage of rare scenarios that would be difficult to capture in real-world data collection, allowing the model to be trained on a complete dataset from the outset.
2Productivity
If simulated images are used to train machine learning models, then data collection time is reduced and diverse scenarios are covered, but the simulated images may lack realism compared to real-world images
Solution Approach 1:
The patent employs scene configuration parameters that control various aspects of the simulated environment including lighting conditions, object positions, camera angles, and environmental features. By adjusting these parameters, the simulation generates visually diverse yet realistic images that maintain the statistical properties of real-world data while achieving high productivity in data generation.
3Reliability
If a large and diverse training dataset is collected to cover all types of situations, then model performance is improved, but the complexity and cost of data annotation increases
Solution Approach 1:
The simulation environment automatically generates training images with embedded ground truth annotations including object locations, classes, and scene configurations. This self-service capability eliminates the need for manual annotation of large datasets, reducing annotation complexity while maintaining comprehensive coverage of diverse scenarios necessary for high model performance.
4Adaptability or versatility
If real-world data is collected to cover rare classes and edge cases, then the model can handle all situations, but the difficulty of replicating real-world conditions increases
Solution Approach 1:
The simulation environment serves multiple functions simultaneously: it generates images for all object classes, creates edge case scenarios, controls lighting and environmental conditions, and provides ground truth annotations. This multi-functional system achieves comprehensive model coverage without the complexity of replicating diverse real-world conditions through physical data collection.
Data Source
AI summary
A method that includes obtaining real training samples that include real images that depict real objects, obtaining simulated training samples that include simulated images that depict simulated objects, defining a training dataset that includes at least some of the real training samples and at least some of the simulated training samples, and training a machine learning model to detect subject objects in unannotated input images using the training dataset.