Object Recognition Device Using Dynamic Detection Probability
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Solution Overview
Problem
Existing object recognition devices face challenges in quickly recognizing distant objects based on weak reflected signals and often erroneously identify noise as objects, leading to delayed recognition of distant targets.
Innovation Solution
An object recognition device comprising a target sensor, tracking unit, detection probability calculation unit, and recognition unit, which calculates detection probability based on reflection intensity and presence probability, allowing for quick recognition of objects while suppressing noise identification, using a correlation between reflection intensity and detection probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the detection threshold is set high to avoid noise recognition, then false positive rate decreases, but detection speed and ability to detect distant objects deteriorates
Solution Approach 1:
The patent changes the detection parameter from a fixed threshold to a dynamic detection probability value that varies with reflection intensity. Stronger reflections produce higher detection probabilities, enabling faster recognition of distant objects while maintaining reliability through probabilistic evaluation rather than binary thresholding.
Solution Approach 2:
The detection probability is dynamically adjusted based on the observed reflection intensity in each processing cycle. This dynamic adaptation allows the system to respond differently to strong and weak signals, improving both detection speed for distant objects and reliability for noise rejection.
2Reliability
If continuous detection for a prescribed period is required to confirm object presence, then false detections are reduced, but recognition delay increases
Solution Approach 1:
The patent changes the confirmation criterion from a fixed time-duration requirement to a probability-based criterion. By calculating detection probability based on reflection intensity, the system can confirm object presence more quickly when the probability exceeds a threshold, reducing recognition delay while maintaining reliability.
3Length of stationary object
If weak reflected signals from distant objects are used for detection, then detection range is extended, but signal reliability decreases leading to erroneous noise identification
Solution Approach 1:
The patent changes the reliability assessment from a binary decision to a continuous probability value based on reflection intensity. Weak signals from distant objects receive lower detection probabilities, which are then evaluated against a threshold, allowing extended detection range while maintaining signal authenticity verification through probabilistic reasoning.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid recognition of objects from weak signals, improving detection accuracy by calculating presence probability based on reflection intensity, thereby reducing erroneous noise recognition and enhancing the ability to detect distant objects.
Implementation Method 1
receive a reflected wave produced by reflection of the probe wave from a target
Data Source
AI summary
An object recognition device according to one aspect of the present disclosure includes a target sensor, a tracking unit, a detection probability calculation unit, a presence probability calculation unit, and a recognition unit. The detection probability calculation unit is configured to calculate a detection probability of a target in the current process cycle, wherein the stronger the reflection intensity in the previous processing cycle, the higher is made the calculated detection probability. The presence probability calculation unit is configured to calculate the presence probability of the target with respect to the observed value of the reflection position in the current processing cycle, using the calculated detection probability. The recognition unit is configured to recognize a target being tracked as a target representing an object, in response to the presence probability being greater than or equal to a predetermined value.