Synthetic Training Image Generation 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 non-standard forms due to the difficulty in collecting comprehensive datasets for such objects, particularly in autonomous driving environments.
Innovation Solution
A method involving a language model to generate training data by defining object attributes like type, shape, and size, and arranging these objects in images, using text data and depth maps to create training images for neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If datasets are collected for objects with irregular shapes in autonomous driving environments, then the object recognition performance of the neural network model is improved, but the difficulty and cost of data collection increases significantly
Solution Approach 1:
The patent uses text-to-image generation models to create synthetic images of objects with irregular shapes, replacing the need for physical data collection. The system generates training images by copying and transforming object attributes (shape, size, position) into visual representations through AI models, thereby solving the data collection difficulty while maintaining recognition performance.
Solution Approach 2:
The patent introduces text data as an intermediary between the object description and the generated image. The system first converts object attributes into text descriptions, then uses these texts to guide the text-to-image generation process, creating a mediating layer that simplifies the overall data generation workflow.
2Adaptability or versatility
If comprehensive datasets for various types of objects are collected, then the versatility of the neural network model is improved, but the time and resources required for data collection increase
Solution Approach 1:
The patent performs preliminary generation of diverse object images using text-to-image models before they are needed for training. The system can pre-generate images of various object types, shapes, and contexts based on text descriptions, storing them for future use, thereby eliminating the need for time-consuming data collection when the model needs to be trained or updated.
Solution Approach 2:
The patent creates a universal image generation system that can produce training data for multiple object types using a single text-to-image model. The system handles various objects (animals, plants, artifacts, etc.) through the same generation pipeline, making the data collection process multi-functional and reducing the need for separate data collection efforts for each object category.
3Reliability
If training data for objects with undefined shapes is generated, then the neural network model's ability to recognize irregular objects is improved, but the complexity of the data generation process increases
Solution Approach 1:
The patent segments the data generation process into distinct modules: text description generation, attribute extraction, and image synthesis. Each module handles a specific aspect of the process, making the overall complex task manageable. The text-to-image model is divided into separate functional components that can be independently optimized and controlled.
Solution Approach 2:
The patent controls the generation of irregular objects by adjusting parameters in the text-to-image model, such as shape variability, size distribution, and position coordinates. By changing these parameters, the system can generate diverse irregular objects without manually designing each one, reducing process complexity while maintaining recognition reliability.
Data Source
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.