GAN Image Translation for Labeled Synthetic Vision Data
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Solution Overview
Problem
Autonomous vehicles face challenges in situational awareness and object detection due to the time-consuming and error-prone process of data labeling, which is impractical for small fleets and requires vast amounts of labeled images, limiting the scalability and accuracy of machine learning.
Innovation Solution
Utilizing a generative adversarial network (GAN) for unpaired image-to-image translation to generate synthetic images that augment real data, addressing the limitations of real data quantity and realism, enabling efficient and accurate training of computer vision functions for object recognition in unknown environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data labeling processes are used to train autonomous vehicles, then object detection accuracy can be improved, but the time consumption and manual effort increase significantly
Solution Approach 1:
The patent uses synthetic image generation to create copies of real-world scenarios through simulation. Instead of manually labeling real images, the system generates synthetic images with automatically embedded ground truth labels, copying the essential features and annotations needed for training while eliminating the time-consuming manual labeling process
Solution Approach 2:
The system performs preliminary action by pre-generating and pre-labeling synthetic training data before actual deployment. The synthetic images with embedded labels are created in advance through simulation environments, allowing the autonomous vehicle system to be trained without requiring real-time or post-hoc manual labeling of field data
2Quantity of substance
If vast amounts of real labeled images are collected for training, then machine learning model performance improves, but the scalability and feasibility decrease for small fleets
Solution Approach 1:
The patent employs synthetic image generation to create unlimited training data copies through simulation. Instead of physically collecting and labeling real images from field deployments, the system generates synthetic images that replicate real-world scenarios, providing abundant training data without the logistical constraints of physical data collection
Solution Approach 2:
The system changes parameters by transitioning from physical data collection to virtual simulation generation. By adjusting simulation parameters such as environmental conditions, object positions, and lighting scenarios, the system can generate diverse training data with varying characteristics without requiring additional field missions or manual intervention
3Measurement precision
If manual semantic segmentation and classification are performed on each image, then data quality and accuracy improve, but the process becomes error-prone and impractical for small fleets
Solution Approach 1:
The patent uses synthetic image generation to copy real-world scenarios with automatically embedded ground truth labels. The simulation environment programmatically creates images with known object positions, categories, and attributes, eliminating manual labeling errors and ensuring consistent, error-free annotations across all training data
Solution Approach 2:
The system performs self-service by automatically generating labeled training data through simulation without requiring human annotators. The synthetic data generation process inherently includes ground truth labels through the simulation model itself, making the data labeling process automated, consistent, and free from human error or variability
Data Source
AI summary
An apparatus for identifying objects receives a dataset comprising an image having first real image features; trains a neural network to recognize the first real image features in the received dataset; and performs a synthetic image augmentation to generate synthetic image features corresponding to the first real image features in the received dataset using the neural network. The synthetic image augmentation allows for improved training of a computer vision function for recognizing second real image features, corresponding to the synthetic image features, in a real-world environment.