Image Detection Model Switching for Precise Object Part Selection
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Solution Overview
Problem
Existing image detection systems struggle to accurately and efficiently allow users to select and manipulate specific objects or parts of objects within an image for processes like tracking, autofocus, and counting, especially when multiple detection models with varying granularities are involved.
Innovation Solution
An information processing apparatus that switches between trained models with different detection granularities, displays detection results on a screen, and allows users to select objects or specific parts based on these results, enabling processes like tracking, autofocus, and counting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple trained models with different detection granularities are used, then detection accuracy and versatility are improved, but device complexity and model selection difficulty increase
Solution Approach 1:
The system dynamically switches between different trained models based on user selection and detection needs. The display unit switches which trained model is active and displays results accordingly, allowing the system to adapt its detection granularity dynamically rather than using a fixed model
Solution Approach 2:
A determination unit acts as an intermediary between the multiple trained models and the user. This unit determines which object should undergo predetermined processes based on user operations on detection results, managing the complexity of model selection and interpretation
2Adaptability or versatility
If multiple trained models with different detection granularities are used, then the ability to detect various objects and parts is improved, but ease of operation deteriorates due to model switching requirements
Solution Approach 1:
The system provides self-service by automatically managing model switching based on user interactions. When users perform operations on detection results, the determination unit automatically determines which object to process and which model to use, eliminating the need for users to manually manage model complexity
Solution Approach 2:
The system implements feedback loops where user operations on displayed detection results trigger automatic model switching and process determination. The display unit shows results from the current model, and user interactions feed back to the determination unit, which adjusts model selection accordingly
Data Source
AI summary
A display unit switches which trained model, among a plurality of trained models whose granularities of detection for an object to be detected from an image are different from each other, is a trained model of interest, and displays on a screen a result of detection by the trained model of interest. A determination unit determines an object on which a predetermined process is to be performed based on a user operation on the result of detection by the trained model of interest.