Synthetic Traffic Sign Data Generation via 3D Modeling
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Solution Overview
Problem
Current methods for generating synthetic traffic sign data struggle to create realistic and diverse datasets for training traffic sign detection models, particularly for rare or region-specific signs, due to the scarcity of real-world data and privacy concerns, leading to biased and less accurate models.
Innovation Solution
A computer-implemented method that extracts the face of detected traffic signs from sample images, forms a 3D model, and places it in background images based on the distribution of real-world sign placements, generating more realistic and diverse synthetic data for training models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world traffic sign data is collected for training detection models, then model accuracy for common signs is improved, but data scarcity for rare or region-specific signs persists and privacy concerns arise
Solution Approach 1:
The patent creates synthetic copies of traffic signs by extracting the face of detected signs and placing them into background images. This copying approach generates unlimited training data for rare and region-specific signs without requiring additional real-world data collection, thereby resolving the data scarcity problem while maintaining detection accuracy.
Solution Approach 2:
The patent transitions from 2D image data to 3D modeling by creating three-dimensional representations of traffic signs. This dimensional change allows for more realistic rendering and placement in various backgrounds, improving the quality and diversity of synthetic training data for models.
2Adaptability or versatility
If synthetic traffic sign data is generated to increase data diversity, then model robustness to rare signs is improved, but realism and operational adequacy of generated images deteriorate
Solution Approach 1:
The patent employs 3D modeling to create traffic sign representations that capture depth, perspective, and spatial relationships. This three-dimensional approach enables more realistic rendering when placing signs in background images, significantly improving the visual authenticity and operational adequacy of synthetic data compared to traditional 2D manipulation methods.
Solution Approach 2:
The patent introduces an intermediary process that extracts the face of the detected sign and uses it to construct a 3D model, which then serves as the basis for generating realistic synthetic images. This intermediary 3D representation acts as a bridge between real and synthetic data, ensuring high fidelity and realism in the generated images.
3Productivity
If traditional synthetic data generation methods are used, then data generation speed is improved, but the complexity and resource requirements of creating realistic 3D models increase
Solution Approach 1:
The patent performs preliminary actions by extracting and storing the face of detected traffic signs and pre-processing them into 3D model formats. This preliminary preparation enables rapid generation of realistic synthetic images later, as the computationally intensive 3D modeling work is done in advance, balancing speed and complexity effectively.
Solution Approach 2:
The patent reuses extracted sign faces across multiple synthetic image generations. Once a sign face is extracted and converted to a 3D model, it can be copied and placed in numerous different backgrounds and scenarios without requiring repeated complex modeling operations, thereby improving data generation efficiency while maintaining realism.
Data Source
AI summary
The present invention is related to a computer-implemented method for generating synthetic traffic sign data for training a traffic sign detection model. The method includes: extracting, from a sample image depicting a surrounding environment of a vehicle including a detected traffic sign, a face of the detected traffic sign, using information indicative of an orientation of the detected traffic sign; forming a 3D model of the traffic sign having the extracted face of the detected traffic sign; and generating synthetic traffic sign data by placing the 3D model of the traffic sign in one or more background images, wherein the 3D model is placed in the one or more background images based on a distribution of a placement of real-world traffic signs in a dataset of real-world images.