Synthetic Lane Line Data Generation for Model Training
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Solution Overview
Problem
Conventional lane line identifying models struggle to accurately identify lane line information in abnormal physical environments due to the lack of sufficient training samples, leading to poor visibility and reduced driving safety.
Innovation Solution
A method and apparatus that generate lane line images in abnormal environments using a generating model based on images from normal environments, allowing for the training of a lane line identifying model with acquired lane line information to improve identification accuracy across different conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional lane line identifying models are trained only with samples from normal physical environments, then the model structure remains simple and training data collection is easy, but the identification accuracy deteriorates in abnormal physical environments due to lack of sufficient training samples
Solution Approach 1:
The patent uses a generating model to create synthetic lane line images that replicate real-world lane line characteristics. The generating model learns from real lane line images and generates synthetic samples in abnormal environments, effectively copying the essential features of real data without requiring physical collection in dangerous or difficult-to-reach conditions.
Solution Approach 2:
The patent performs preliminary training of the generating model using real lane line images before using it to generate synthetic training data. This preliminary action establishes a robust data generation capability that can subsequently produce abundant training samples for various abnormal environments, solving the sample quantity problem before the main training task.
2Measurement precision
If more training samples from abnormal physical environments are collected manually, then the identification accuracy in abnormal environments improves, but the data collection cost, time, and safety risks increase significantly
Solution Approach 1:
Instead of manually collecting real images in abnormal environments, the system copies lane line features from normal environment images and reconstructs them synthetically in abnormal environments. This eliminates the time-consuming manual data collection process while maintaining training effectiveness.
Solution Approach 2:
The generating model serves itself by automatically generating synthetic training data without external human intervention for data collection. Once trained on initial real samples, the model independently produces abundant synthetic data for various abnormal conditions, eliminating the need for continuous manual data gathering.
3Adaptability or versatility
If a generating model is introduced to synthesize lane line images, then the training sample quantity and environmental diversity increase, but the system complexity and model training difficulty increase
Solution Approach 1:
The patent divides the overall training system into two independent modules: a generating model for synthetic data generation and a lane line identifying model for actual detection. This segmentation allows each model to be trained and optimized separately, reducing the complexity of training the entire system simultaneously while improving environmental adaptability.
4Productivity
If synthetic lane line images are generated using a generating model, then training efficiency improves and driving safety is enhanced, but the manufacturing complexity of the training system increases
Solution Approach 1:
The system copies successful patterns from simple real-world images to create complex synthetic training scenarios. By learning the essential features from a small set of real images and copying them into various synthetic conditions, the system achieves high training efficiency without requiring complex manual setup for each training scenario.
Data Source
AI summary
Embodiments of the present disclosure provide a method and apparatus for training a lane line identifying model. The method includes: acquiring a first image of a lane line, the first image being generated using a generating model based on a second image of the lane line, the first image and the second image of the lane line being associated with different physical environments respectively; acquiring lane line information in the second image of the lane line; and training the lane line identifying model using the first image and the acquired lane line information of the lane line.