Perception Weakness Detection for High-Value Training Data Selection
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Solution Overview
Problem
Existing machine learning-based perception systems face inefficiencies in identifying and addressing systemic biases and defects, requiring significant manual effort for data labeling and analysis, which is time-consuming and costly.
Innovation Solution
An automated system and method for identifying perception weaknesses and high-value training data using a defect detection engine, which reduces manual analysis and optimizes labeling efforts by automatically selecting and labeling data that improves model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to identify perception weaknesses and select training data, then identification accuracy can be achieved, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces the mechanical manual analysis system with an automated defect detection engine that uses machine learning to identify perception weaknesses and select training data candidates. This substitution maintains identification accuracy while dramatically reducing time consumption and cost by automating the entire workflow from weakness detection to training data selection.
Solution Approach 2:
The defect detection engine performs self-service by automatically analyzing sensor data, identifying perception weaknesses, generating candidate training data, and selecting high-value training samples without requiring manual human intervention. The system serves itself by using its own outputs to improve its performance iteratively.
2Reliability
If comprehensive manual labeling is performed to ensure complete training data coverage, then model performance can be improved, but cost and time resources are excessively consumed
Solution Approach 1:
Instead of uniformly labeling all training data, the patent applies local quality by using the defect detection engine to identify and label only the specific regions or data samples that contain perception weaknesses. This targeted approach ensures that labeling resources are concentrated on the most critical areas that will improve model performance, rather than wasting resources on already well-performing regions.
Solution Approach 2:
The patent implements partial action by selecting only a subset of training data candidates that are most likely to improve model performance, rather than labeling all available data. The defect detection engine ranks candidates and selects the top portions that provide the highest value, achieving effective model improvement with reduced labeling effort.
3Productivity
If automated defect detection is implemented to reduce manual effort, then productivity increases, but system complexity increases
Solution Approach 1:
The defect detection engine is designed as a universal system that performs multiple functions: detecting perception weaknesses in sensor data, generating candidate training data, selecting high-value training samples, and providing feedback for model retraining. This multi-functionality consolidates what would otherwise require multiple separate systems into a single integrated solution, managing complexity while maintaining high productivity.
Solution Approach 2:
The patent introduces an intermediary defect detection engine that mediates between the raw sensor data and the training data labeling process. This intermediary component automatically analyzes data, identifies weaknesses, and prepares candidate training data, serving as a bridge that simplifies the overall system architecture while enabling automated high-productivity operation.
Data Source
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AI summary
Methods are described for automatically identifying perception weaknesses for training data to be used in improving the performance of perception systems. Deficiencies in simulated data are also identified. The methods, which can be incorporated into a system or into instructions placed on storage media, include comparing perception system results between baseline results and results with augmented inputs and identifying perception weaknesses responsive to that comparison. The perception system is retrained using the relabeled data and is improved thereby.