Dynamic Threshold Hysteresis for Object Detection Noise
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Solution Overview
Problem
Existing object detection systems in monitored regions face challenges with false assessments due to interfering signals or noise, leading to incorrect determination of object presence.
Innovation Solution
The system employs dynamic threshold hysteresis, where the detection thresholds are adjusted based on the noise level, using a higher threshold when noise is present and a lower threshold when noise is absent, to reliably determine object presence or absence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed threshold level is used for object detection, then the detection process is simple and fast, but false detections occur when noise is present
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed threshold to a dynamic threshold that adapts to noise conditions. The threshold is adjusted based on measured noise levels, allowing the system to maintain high detection accuracy in varying environmental conditions without requiring complex manual calibration
Solution Approach 2:
The patent implements feedback by measuring the noise level in the monitored region and using this information to adjust the detection threshold. The system continuously monitors noise and adapts the threshold accordingly, creating a closed-loop control system that improves reliability while managing complexity through automated adaptation
2Reliability
If a higher threshold is used to reduce false positives from noise, then noise interference is reduced, but actual object detections may be missed
Solution Approach 1:
The system dynamically adjusts the threshold based on measured noise levels rather than using a static high threshold. When noise is low, the threshold remains sensitive to detect weak object signals. When noise increases, the threshold adapts upward to reject false positives. This dynamic adjustment resolves the contradiction by making the threshold context-dependent
Solution Approach 2:
The patent changes the detection threshold parameter based on noise conditions. By measuring noise levels and adjusting the threshold parameter accordingly, the system maintains optimal sensitivity across different environmental conditions, preventing both false positives and missed detections
3Measurement precision
If noise measurement and threshold adjustment are implemented, then detection accuracy improves, but system complexity and processing time increase
Solution Approach 1:
The system performs preliminary noise measurement during dedicated measurement periods before object detection. By characterizing the noise environment in advance, the system establishes an appropriate threshold for subsequent detection, enabling accurate detection without requiring complex real-time analysis during the critical detection phase
Data Source
AI summary
A method and device for object detection are disclosed. In one aspect, a method comprises transmitting a plurality of signals into a region; measuring a noise level during a noise measurement time interval corresponding to each respective one of the plurality of transmitted signals; generating a threshold signal dependent on the noise level; and comparing a first plurality of signals received by a sensor, each of the received signals corresponding to a respective one of the plurality of the transmitted signals, with the respective threshold signal. In another aspect, a device comprising components adapted to carry out the steps of object detection is disclosed. In one example, a thresholding circuit is adapted to generate a threshold signal have a level corresponding to substantially a peak-to-peak level of the measured noise level.