Image Annotation Precision Calculation via Intersection and APOT
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Solution Overview
Problem
Existing methods for training neural networks to recognize images face challenges in accurately calculating the precision of multiple independent image annotations, which affects the training phase and deployment of neural engines.
Innovation Solution
A method is introduced to calculate the precision of image annotations by computing the number of pixels in the intersection of annotations divided by the average pixels on target (APOT), providing a precision value that determines the adequacy of neural engine training and deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple independent image annotations are used to train neural networks, then the training data coverage is improved, but the measurement precision of annotation accuracy deteriorates due to lack of reliable precision calculation methods
Solution Approach 1:
The patent introduces a new parameter calculation method that computes precision values by determining the intersection area between multiple annotations and dividing it by the union area of all annotations. This parameter change enables quantitative measurement of annotation precision across multiple independent annotations, resolving the measurement precision deterioration issue while maintaining improved training data coverage
Solution Approach 2:
The patent replaces subjective human evaluation of annotation accuracy with an automated computational system that calculates precision values using intersection-over-union metrics. This substitution eliminates the need for manual verification while providing objective, consistent precision measurements, thereby improving measurement precision without sacrificing training data coverage
2Ease of operation
If traditional annotation evaluation methods are used, then the evaluation process is simple, but the reliability of neural engine training deteriorates due to inadequate precision assessment
Solution Approach 1:
The patent implements a self-service evaluation system where the annotation precision calculation automatically determines whether training data meets quality thresholds without requiring external manual assessment. The system computes precision values and makes autonomous decisions about training adequacy, maintaining operational simplicity while significantly improving training reliability through objective precision measurement
Solution Approach 2:
The patent introduces feedback mechanisms that use calculated precision values to determine whether additional annotations are needed before proceeding with neural engine training. The system provides quantitative feedback on annotation quality, enabling iterative improvement of training data reliability while keeping the evaluation process straightforward through automated threshold comparisons
Data Source
AI summary
A machine receives, for a given target in a given image, a plurality of annotations, each annotation indicating a range of pixels corresponding to the given target. The machine computes a number of pixels in an intersection of the plurality of annotations. The machine computes an average pixels on target (APOT) for the given target by computing, for each annotation, a number of pixels that corresponds to the given target and taking a mean of those numbers. The machine computes a precision value of the plurality of annotations as the number of pixels in the intersection divided by the APOT. The machine provides an output representing the precision value.