Image Classifier Evaluation Using Safety-Critical Region Relevance
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Solution Overview
Problem
The evaluation of image classifiers, particularly those based on machine learning, is difficult due to their 'black box' nature, making it challenging to ensure their safe operation in safety-critical applications like autonomous vehicles.
Innovation Solution
A method for evaluating image classifiers by ascertaining a dataset with annotations, determining reachable regions, assigning relevance values, and classifying images to assess accuracy and safety, with iterative training to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data-trained image classifiers (neural networks) are used to achieve best classification performance, then classification accuracy is improved, but interpretability and safety assurance deteriorate due to black box nature
Solution Approach 1:
The patent introduces an intermediary evaluation system that mediates between the image classifier and the safety assurance requirement. This evaluation system uses annotated image datasets and relevance values to assess the classifier's performance on safety-critical regions, providing transparent metrics about the classifier's behavior without altering the classifier itself.
Solution Approach 2:
The patent segments the image classification task into different regions based on relevance values. Instead of evaluating the entire image uniformly, the system identifies and evaluates specific regions that are relevant for safety assessment, allowing focused analysis on critical areas while maintaining overall classification accuracy.
2Extent of automation
If image classifiers are used for safety-critical applications, then automation capability is improved, but difficulty of detecting and measuring safety performance worsens
Solution Approach 1:
The patent implements a feedback mechanism where the evaluation system continuously assesses the image classifier's performance on safety-critical regions and provides measurable feedback. This feedback loop enables monitoring of safety performance metrics and can trigger retraining or adjustment processes to maintain safe operation levels.
Solution Approach 2:
The patent replaces manual safety assessment mechanisms with an automated evaluation system that processes annotated image datasets and calculates relevance-based metrics. This substitution transforms the previously manual and subjective safety measurement process into an objective, quantifiable, and repeatable automated measurement system.
3Measurement precision
If relevance values are assigned to image regions to enable safety evaluation, then measurement precision of safety-critical regions is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by assigning different relevance values to different image regions based on their safety-criticality. Instead of uniform evaluation, the system identifies regions with higher relevance (more safety-critical) and evaluates them with greater precision, while less critical regions receive lower evaluation weight, optimizing the balance between precision and complexity.
Data Source
AI summary
A computer-implemented method for evaluating an image classifier, in which a classifier output of the image classifier is provided for the actuation of an at least semi-autonomous robot. The evaluation method includes: ascertaining a first dataset including image data and annotations being assigned to the image data, the annotations including information about the scene imaged in the respective image and/or about image regions to be classified and/or about movement information of the robot; ascertaining regions of the scenes that are reachable by the robot based on the annotations; ascertaining relevance values for image regions to be classified by the image classifier; classifying the image data of the first image dataset with the aid of the image classifier; evaluating the image classifier based on image regions correctly classified by the image classifier and incorrectly classified image regions, as well as the calculated relevance values of the corresponding image regions.