Semantic Visual Parameter Mapping for Computer Vision Sensitivity Analysis
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Solution Overview
Problem
Existing computer vision models face challenges in dealing with open contexts, requiring improved development and testing to ensure safety-critical applications, particularly in automotive systems, where expert opinions on visual parameters can be incomplete or misleading, leading to inefficient training and testing processes.
Innovation Solution
A method for generating a data structure comprising a semantic mapping of visual parameters, which involves obtaining a computer vision model, initial visual parameters, applying a sensitivity analysis to refine these parameters, and creating a language entity-based specification to enhance model performance and safety by defining a refined visual parameter set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If expert opinions are used to define visual parameters for computer vision model training, then the parameter selection process is simplified, but the completeness and accuracy of the parameter set deteriorates
Solution Approach 1:
The system performs self-service by automatically analyzing the computer vision model's own performance characteristics and sensitivity to visual parameters, eliminating the need for external expert opinions. The model itself provides the information needed to identify critical parameters through automated sensitivity analysis and performance evaluation
Solution Approach 2:
The system implements feedback loops where the computer vision model's performance scores are continuously evaluated against ground truth data, and sensitivity analysis results are used to refine the visual parameter set. This iterative feedback process ensures both completeness and accuracy of the parameter selection
2Reliability
If a comprehensive visual parameter set is used to ensure model performance, then the model accuracy improves, but the complexity of the parameter space increases
Solution Approach 1:
The system extracts only the critical visual parameters that have the most significant impact on model performance, separating them from the complete but unnecessary parameter space. Sensitivity analysis identifies and extracts the essential parameters that drive performance variations
Solution Approach 2:
The visual parameter space is segmented into critical and non-critical parameters based on sensitivity analysis results. The system focuses on the segmented critical parameters that most affect model performance, reducing the overall complexity while maintaining accuracy
3Measurement precision
If sensitivity analysis is performed to refine visual parameters, then the parameter quality improves, but the computational time increases
Solution Approach 1:
The system performs partial sensitivity analysis by focusing only on the most influential parameters identified through initial screening, rather than exhaustively analyzing all possible parameters. This partial action approach achieves sufficient parameter quality without excessive computational time
Data Source
AI summary
Facilitating the description or configuration of a computer vision model by generating a data structure comprising a plurality of language entities defining a semantic mapping of visual parameters to a visual parameter space based on a sensitivity analysis of the computer vision model.