Synthetic Training Images for Irregular Object Recognition
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Solution Overview
Problem
Existing artificial neural network models face challenges in recognizing objects with irregular shapes or amorphous forms due to the difficulty in collecting comprehensive datasets for such objects, particularly in autonomous driving environments.
Innovation Solution
A method is employed to generate training data by inputting prompts into a language model to describe objects with irregular shapes, acquiring text data, generating arrangement information, and creating images with these objects arranged within a context, thereby training the neural network to recognize these objects effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive datasets are collected for objects with irregular shapes, then the object recognition performance is improved, but the data collection difficulty and time increase significantly
Solution Approach 1:
The patent uses image generation models to create synthetic copies of objects with irregular shapes instead of collecting real-world data. The system generates training images by synthesizing objects with various irregular shapes, sizes, and positions in driving environments, providing comprehensive training data without physical data collection
Solution Approach 2:
The patent introduces an image generation model as an intermediary between the need for training data and the final object recognition model. This intermediary synthesizes training images with irregular objects based on text descriptions, eliminating the need for direct data collection while maintaining training effectiveness
2Reliability
If comprehensive datasets are collected for objects with irregular shapes, then the object recognition performance is improved, but the complexity of the data collection process increases
Solution Approach 1:
The system replaces complex physical data collection processes with automated image synthesis. By using generative models to create synthetic training images, the patent eliminates the need for complex data annotation, object positioning, and environment setup that would otherwise be required
Solution Approach 2:
The image generation model automatically generates diverse training images with irregular objects in various contexts without human intervention. The system self-generates captions, object positions, and environmental details, eliminating the need for manual data collection and annotation processes
3Quantity of substance
If datasets are artificially collected for irregular shaped objects, then the training data availability is improved, but the cost and effort of data collection increase
Solution Approach 1:
The patent generates unlimited synthetic training images of objects with irregular shapes through automated image synthesis. The system can create as many training samples as needed by varying object parameters, positions, and environmental conditions, providing abundant training data at minimal effort
Solution Approach 2:
The system varies parameters such as object shape, size, position, and environmental conditions to generate diverse training images. By changing these parameters programmatically, the patent creates comprehensive training datasets without the need for physical data collection for each variation
Data Source
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AI summary
A training data generation method for training an artificial neural network model including inputting a first prompt related to at least one first object in a specific context into a language model, acquiring first text data related to the at least one first object output from the language model, acquiring a first image related to the specific context, generating, based on at least one of the first text data or the first image, arrangement information related to an arrangement of the at least one first object for the first image, generating, based on at least one of the first text data, the first image, or the arrangement information, a second image in which the at least one first object is arranged in the first image, and outputting the second image.