Computer Vision Model Verification via Sensitivity Analysis
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Solution Overview
Problem
Current verification processes for computer vision models in autonomous systems are complex and require significant computational resources, often relying on human expert input and failing to adequately prioritize sensitive visual parameters, which is critical for safety-critical applications like autonomous driving.
Innovation Solution
A method that performs sensitivity analysis to reduce the number of visual parameters to be verified, focusing on those with higher variance, thereby streamlining the verification process by generating a verification parameter specification with fewer parameters or reduced ranges, and applying this to a computer-implemented verification method using a processor and data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a comprehensive visual parameter specification is used for verification, then the thoroughness of verification is improved, but the computational load and complexity increase significantly
Solution Approach 1:
The patent extracts and identifies only the most sensitive visual parameters from the comprehensive parameter specification through sensitivity analysis. By taking out the critical parameters that have the greatest impact on model performance and focusing verification efforts on these selected parameters, the system achieves thorough verification of essential aspects while reducing overall computational complexity and resource requirements.
2Reliability
If all visual parameters are verified in detail, then the safety guarantee is improved, but the verification time and computational resources increase
Solution Approach 1:
The patent changes the verification approach by transforming the parameter specification from including all visual parameters to including only the sensitive parameters identified through sensitivity analysis. This parameter reduction maintains safety guarantees for the most critical aspects while significantly reducing verification time and computational resource consumption, as the model is verified on a focused subset of parameters that matter most to its performance.
3Measurement precision
If human experts manually select visual parameters for verification, then the expertise-based selection is improved, but the subjectivity and inconsistency between experts increase
Solution Approach 1:
The patent implements self-service by enabling the verification system to automatically identify and select sensitive visual parameters through sensitivity analysis, replacing manual expert selection. The system autonomously determines which parameters to verify based on their impact on model performance, eliminating subjectivity and inconsistency between different experts while maintaining accurate parameter selection through objective, data-driven analysis.
4Productivity
If the visual parameter specification is reduced for faster verification, then the verification speed is improved, but the coverage of critical parameters may be reduced
Solution Approach 1:
The patent applies local quality by differentiating between sensitive and non-sensitive parameters, allocating verification resources preferentially to the sensitive parameters that have the greatest impact on model performance. Rather than uniformly reducing all parameters, the system maintains comprehensive verification coverage for critical parameters identified through sensitivity analysis while reducing or eliminating less important parameters, thus achieving fast verification without sacrificing coverage of essential aspects.
Data Source
AI summary
Reducing the number of parameters in a visual parameter set based on a sensitivity analysis of how a given visual parameter affects the performance of a computer vision model to provide a verification parameter set having a reduced size.