Adaptive Confidence Thresholding in Object Detection Systems
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Solution Overview
Problem
Existing object detection systems face challenges in setting a consistent confidence threshold for object classifications across all object classes and regions of a captured scene, leading to issues with false positives and false negatives.
Innovation Solution
The method involves running an object detector module on multiple images to store outputs and determining a representative confidence score for each region, allowing for adaptive threshold setting based on historical data, with lower thresholds in regions where high confidence scores are typical and higher thresholds in regions with rare object detections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single threshold confidence score is used for all object classes and regions, then the system operation is simple, but false positives and false negatives increase
Solution Approach 1:
The patent applies local quality by setting different threshold confidence scores for different regions of the captured scene based on historical data. Each region is analyzed to determine its specific characteristics (e.g., frequency of object occurrences, typical confidence scores), and thresholds are customized accordingly. This resolves the contradiction by maintaining simple overall system operation while improving detection accuracy through location-specific threshold adaptation.
Solution Approach 2:
The patent implements dynamic thresholds that adapt over time based on accumulated historical confidence scores and object detection patterns. The system continuously learns from past detections and adjusts thresholds dynamically for different regions and object classes, rather than using static universal thresholds. This dynamic adaptation improves reliability while keeping the operational framework straightforward.
2Measurement precision
If a high threshold confidence score is set to reduce false positives, then detection precision improves, but false negatives increase
Solution Approach 1:
The patent applies local quality by setting different threshold confidence scores for different regions of the captured scene based on historical data. Each region is analyzed to determine its specific characteristics (e.g., frequency of object occurrences, typical confidence scores), and thresholds are customized accordingly. This resolves the contradiction by maintaining simple overall system operation while improving detection accuracy through location-specific threshold adaptation.
Solution Approach 2:
The patent changes the threshold parameter dynamically based on historical confidence score distributions and regional characteristics. Instead of using a fixed high threshold that reduces false positives but increases false negatives, the system adjusts threshold values according to learned patterns from historical data, allowing optimal balance between precision and completeness for each specific context.
3Reliability
If a low threshold confidence score is set to reduce false negatives, then detection completeness improves, but false positives increase
Solution Approach 1:
The patent applies local quality by setting different threshold confidence scores for different regions of the captured scene based on historical data. Each region is analyzed to determine its specific characteristics (e.g., frequency of object occurrences, typical confidence scores), and thresholds are customized accordingly. This resolves the contradiction by maintaining simple overall system operation while improving detection accuracy through location-specific threshold adaptation.
Solution Approach 2:
The patent changes the threshold parameter dynamically based on historical confidence score distributions and regional characteristics. Instead of using a fixed low threshold that increases completeness but also false positives, the system adjusts threshold values according to learned patterns from historical data, allowing optimal balance between completeness and precision for each specific context.
Data Source
AI summary
A method, a device and a non-transitory computer-readable storage medium for setting a confidence threshold for objects detected in a region of a captured scene based on historical confidence scores for objects detected in that region.