Object Detection Confidence Threshold Adjustment
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Solution Overview
Problem
Existing object detection technologies using AI struggle to adapt to varying application scenarios due to fixed confidence threshold settings, leading to high false positive or false negative rates.
Innovation Solution
An object detection device and method that dynamically adjusts the confidence threshold value based on the results of auxiliary recognition models and the correlation degree between these results and the detected object, using a processor to integrate real-time video and environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed confidence threshold value is used, then the system is simple to operate, but the false positive or false negative rate increases due to inability to adapt to different application scenarios
Solution Approach 1:
The patent applies the dynamics principle by transitioning from a fixed confidence threshold to a dynamically adjusted threshold. The system continuously monitors environmental values (such as lighting conditions, background complexity) and automatically adjusts the confidence threshold accordingly. This allows the system to maintain high reliability across different application scenarios while keeping the user interface simple, as the adjustment happens automatically without requiring manual user intervention.
Solution Approach 2:
The system implements self-service by automatically adjusting its own confidence threshold based on environmental conditions without requiring external intervention. The processor monitors environmental values and autonomously modifies the threshold to optimize detection accuracy for the current scenario, eliminating the need for users to manually configure thresholds while maintaining high reliability.
2Device complexity
If a fixed confidence threshold value is used, then the system structure is simple, but the detection accuracy decreases due to poor adaptation to environmental changes
Solution Approach 1:
The patent applies dynamics by making the confidence threshold adjustable based on environmental conditions. The system incorporates environmental value monitoring (lighting, background complexity) and automatically modifies the threshold to maintain high measurement precision. This dynamic adjustment resolves the contradiction by adding minimal complexity through automated environmental sensing while significantly improving detection accuracy.
Solution Approach 2:
The system implements parameter changes by modifying the confidence threshold parameter based on environmental values. The processor adjusts the threshold parameter dynamically according to detected environmental conditions such as lighting levels or background complexity. This allows the system to maintain high measurement precision across varying conditions without requiring a completely complex system architecture.
3Ease of manufacture
If the confidence threshold value is poorly set, then the system is easy to implement, but the false positive or false negative rate becomes too high
Solution Approach 1:
The system applies self-service by automatically determining optimal confidence threshold values based on environmental conditions. Rather than requiring careful manual configuration during deployment, the system monitors environmental values (lighting, background) and autonomously adjusts the threshold to achieve high reliability. This eliminates the need for complex setup procedures while ensuring low false positive and false negative rates.
Solution Approach 2:
The patent implements feedback by continuously monitoring environmental values and using this information to adjust the confidence threshold. The system creates a closed-loop where environmental conditions feed back into threshold adjustment, ensuring optimal detection performance. This feedback mechanism resolves the contradiction by automatically ensuring high reliability without requiring complex manual configuration during manufacturing or deployment.
Data Source
AI summary
An object detection device and a confidence threshold method adjustment method are provided. The object detection device includes an optical camera, multiple sensors, and a processor. The optical camera and sensors are used to respectively capture a real-time video and environmental value of a detection area. The processor performs an object recognition model and at least one second recognition model. The object recognition model determines whether an object exists in the detection area based on the real-time video to generate a first recognition result and a corresponding first confidence value. The at least one second recognition model generates a second result based on the environmental value respectively. The processor dynamically adjusts a confidence threshold value based on a value of the second result and a correlation degree between the object and the second result. The processor determines whether to generate an output result based on the adjusted confidence threshold value and the first confidence value from the first recognition result.