AI Model Accuracy via Cross-Object Preprocessing
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Solution Overview
Problem
Existing artificial intelligence learning methods struggle to achieve high accuracy in predictive results, particularly in detecting objects that are difficult to identify using traditional approaches.
Innovation Solution
The method involves configuring an input dataset for AI learning, creating an AI model by repeatedly learning from this dataset, and using preprocessing information from a first object to enhance the learning process for a second object, where correlation information is utilized to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional AI learning methods are used for object detection, then the learning process is simple and fast, but the detection accuracy is low for difficult-to-identify objects
Solution Approach 1:
The patent applies preliminary action by performing preprocessing on a first object before using its information to assist in detecting a second object. The system pre-extracts features, performs preprocessing operations, and stores this information in advance, which then serves as auxiliary data to improve the detection accuracy of difficult-to-identify objects without requiring complex real-time processing during the actual detection phase.
2Measurement precision
If more data and complex models are used to improve detection accuracy, then predictive accuracy improves, but computational resources and time increase
Solution Approach 1:
The system applies self-service by having the first object's preprocessing information serve the detection needs of the second object. Instead of independently processing each object with full computational resources, the system reuses preprocessed information from related objects, allowing the AI model to achieve high detection accuracy while reducing redundant computational work and learning time.
3Measurement precision
If correlation information from multiple sources is integrated, then detection accuracy improves, but information processing complexity increases
Solution Approach 1:
The patent applies merging by integrating correlation information from multiple sources including tag information from texts or images, scene-object relationships, and inter-frame correlations. The system combines these diverse information types into a unified preprocessing framework that enhances detection accuracy while managing processing complexity through systematic integration rather than separate handling of each information source.
Data Source
AI summary
Proposed are method, apparatus, and system for improving accuracy of artificial intelligence learning-based predictive results. The method for improving accuracy of artificial intelligence learning-based predictive results include configuring an input dataset by collecting and preprocessing data for artificial intelligence learning, creating an artificial intelligence model by repeatedly learning on the basis of the input dataset, and driving and providing a predictive result for a target object in input data using the artificial intelligence model. In the method, in the creating of an artificial intelligence model, a first object and a second object are distinguished in the input dataset and preprocessing information of the first object is used in a repeated learning process for the second object.