Anomaly Detection Model for Image Labeling Accuracy
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Solution Overview
Problem
The existing labeling processes for AI models are labor-intensive and prone to errors, especially when image features are similar, leading to poor training effects and repeated retraining, which consumes significant time and resources.
Innovation Solution
An anomaly labeled-assistant detection system that uses a computing apparatus with an anomaly labeled detection model to automatically compare labeled and inference categories for image data, identifying and listing anomaly labeled data, thereby reducing human intervention and improving training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to label image data, then labeling flexibility is maintained, but labeling accuracy deteriorates due to human errors and high fatigue
Solution Approach 1:
The system enables automated self-labeling through AI models that can independently identify and label image data without human intervention. The model processes images, generates labels, and the results are automatically verified through consistency checks, allowing the system to serve itself in the labeling process while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical human labeling process with an automated AI-based system. The AI model uses algorithmic processing to identify objects and generate labels, substituting human visual inspection and manual labeling operations with computational processes that eliminate human errors and fatigue.
2Productivity
If AI model is used to assist labeling, then labeling efficiency is improved, but labeling accuracy deteriorates when image features are too similar
Solution Approach 1:
The system implements feedback mechanisms where the AI model's labeling results are automatically verified through consistency checks. The model processes images, generates labels, and these results are fed back into the system for verification against ground truth data or through cross-validation, allowing the system to identify and correct errors even when image features are similar.
Solution Approach 2:
The patent applies preliminary action by using the AI model to pre-label image data before final verification. The model performs initial labeling on all images, and then automated verification processes check these preliminary labels against expected patterns or ground truth data, ensuring accuracy is maintained even for challenging similar-looking images.
3Reliability
If repeated retraining is performed to improve model accuracy, then model performance is improved, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary verification of labeling results using automated consistency checks before committing to retraining. By verifying labels against ground truth data or through cross-validation in advance, the system identifies only the necessary corrections needed, minimizing the scope and time of subsequent retraining while maintaining model accuracy.
Solution Approach 2:
The patent applies partial action by performing selective retraining only on specific image categories or subsets where labeling errors were identified, rather than retraining the entire model from scratch. This targeted approach reduces retraining time significantly while still improving overall model performance by addressing the specific weak points.
Data Source
AI summary
An anomaly labeled-assistant detection system and a method thereof are provided. The anomaly labeled-assistant detection system includes a computing apparatus and a storage apparatus. The computing apparatus includes an anomaly labeled detection model, where the computing apparatus detects a plurality of pieces of labeled image data with a labeled category through the anomaly labeled detection model, the anomaly labeled detection model respectively generates an inference category corresponding to each piece of labeled image data, and the computing apparatus compares the labeled category and the inference category according to each piece of labeled image data, and automatically lists the labeled image data as anomaly labeled data when the labeled category of the labeled image data is different from the inference category. The storage apparatus is electrically connected to the computing apparatus, to store the labeled image data.