Dynamic Weight Adjustment for Multi-Detector Object State Classification
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Solution Overview
Problem
Existing methods for detecting target objects in images using multiple detectors face challenges in accurately integrating outputs due to inappropriate weighting of detection results, leading to suboptimal shape discrimination performance.
Innovation Solution
A learning apparatus that includes detection units, an estimation unit, a classification unit, and a weight calculation unit to estimate the state of a target object and calculate weights for each detection unit based on detection results, allowing for appropriate weighting and improved detection performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple detectors are used to detect target objects, then detection coverage is improved, but detection accuracy deteriorates due to inappropriate weighting of detection results
Solution Approach 1:
The patent dynamically changes the weighting parameters of detection results based on the estimated state of the target object. By adjusting the weight according to object state (e.g., occlusion level, pose, size), the system optimizes detection accuracy for each specific situation while maintaining comprehensive detection coverage through multiple detectors.
Solution Approach 2:
The patent introduces dynamic weight adjustment based on real-time estimation of target object state. Instead of using fixed weights, the system adapts the weighting scheme dynamically according to the current state of the target object, allowing the integration process to respond to changing detection conditions and improve overall accuracy.
2Device complexity
If weights are set inappropriately for integrating detection results, then integration process is simple, but shape discrimination performance deteriorates
Solution Approach 1:
The patent implements a self-adjusting weighting mechanism where the system automatically estimates the target object state and determines appropriate weights without external intervention. The estimation unit and weight calculation unit work together to self-optimize the integration process, eliminating the need for manual weight tuning while improving shape discrimination performance.
Solution Approach 2:
The patent introduces a feedback loop where detection results are used to estimate the target object state, which then feeds back into the weight calculation process. This closed-loop system continuously refines the weighting based on actual detection performance and object state, improving shape discrimination without requiring complex external control mechanisms.
3Adaptability or versatility
If weights are manually set for each detection result, then adaptability to different target states is improved, but operation complexity increases
Solution Approach 1:
The patent automates the weight setting process by implementing a self-service system where the estimation unit automatically determines the target object state and the weight calculation unit derives appropriate weights. This eliminates manual weight setting operations while maintaining high adaptability to different target states through automatic state-based weight adjustment.
Data Source
AI summary
A learning apparatus comprises a plurality of detection units configured to detect a part or whole of a target object in an image and output a plurality of detection results; an estimation unit configured to estimate a state of the target object based on at least one of the plurality of detection results; a classification unit configured to classify the image into a plurality of groups based on the state of the target object; and a weight calculation unit configured to calculate weight information on each of the plurality of detection units for each of the groups based on the detection results.