Depth Estimation Model Training Data Rectification and Clustering
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Solution Overview
Problem
Current methods for training depth information estimation models face challenges in achieving accurate and efficient data processing, particularly in autonomous driving systems, where inappropriate data can lead to overfitting and suboptimal performance, and there is a need for enhanced data quality and processing techniques to improve the performance of artificial intelligence models.
Innovation Solution
A data processing method and apparatus that involves logging image data from cameras, transmitting it to a database, configuring training data through rectification and clustering processes, and training a model based on this data to enhance the quality and effectiveness of depth information estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the amount of training data is increased to improve model performance, then the performance of the artificial intelligence model tends to increase, but there may be inappropriate data learned and overfitting issues occur
Solution Approach 1:
The patent transforms raw image data into depth information by changing the parameter representation from 2D images to 3D depth maps. This parameter transformation allows the model to learn depth relationships without directly learning from potentially inappropriate training data, reducing overfitting while maintaining performance
Solution Approach 2:
The patent introduces an intermediary processing step that generates depth information from image data using multiple cameras and processing units. This intermediary depth information acts as a mediator between raw images and the final model output, filtering out inappropriate data characteristics that could cause overfitting
2Reliability
If more training data is collected and processed, then the quality of training data should improve, but the data processing complexity and time increase
Solution Approach 1:
The patent performs preliminary processing of image data by multiple cameras to generate depth information before the actual model training. This preliminary action of creating depth maps in advance reduces the processing burden during training, improving data quality while managing processing time
Solution Approach 2:
The patent divides the data processing into separate segments: image collection from multiple cameras, depth information generation, and model training. This segmentation allows parallel processing of different data aspects, reducing overall processing time while maintaining high data quality
Data Source
AI summary
Provided are a data processing method and apparatus for training a depth information estimation model. The data processing method for training a depth information estimation model, includes: logging image data collected from one or more cameras; transmitting the logged image data to a database; configuring training data, based on the image data of the database; and training a model, based on the training data.