Dynamic Sensor Parameter Optimization for Object Recognition
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Solution Overview
Problem
Existing object recognition systems using sensors like cameras and lidar struggle to achieve optimal results due to fixed or deterministic sensor parameters, limiting the learning ability and efficiency of deep learning algorithms, especially in applications requiring precise measurements like the automotive sector.
Innovation Solution
A procedure and system that dynamically adjust sensor parameters of two sensors (such as camera and lidar) using a Reinforcement Learning algorithm, iteratively refining the sensor settings to maximize object recognition accuracy within specific sensor parameter ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed or deterministic sensor parameters are used, then device complexity is reduced and ease of operation is improved, but object recognition accuracy and algorithm performance are limited
Solution Approach 1:
The patent implements dynamic sensor parameters that can be adjusted in real-time based on environmental conditions and object characteristics. The sensor system transitions from fixed parameters to dynamically adaptable parameters, allowing the object recognition algorithm to optimize performance for different scenarios while maintaining manageable complexity through automated control.
Solution Approach 2:
The patent systematically varies sensor parameters such as exposure time, gain, and sampling frequency to find optimal settings for different recognition tasks. By implementing parameter sweeps and adaptive tuning mechanisms, the system explores the parameter space to identify configurations that maximize measurement precision without requiring manual intervention.
2Measurement precision
If sensor parameters are manually optimized for each scenario, then object recognition accuracy improves, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements pre-training and pre-optimization phases where sensor parameters are optimized offline for various scenarios. These pre-computed parameter sets are stored and automatically selected during operation, eliminating the need for real-time manual optimization while maintaining high detection precision across different conditions.
Solution Approach 2:
The system employs self-adjusting sensor parameters that automatically adapt to changing conditions without external intervention. The object recognition algorithm provides feedback to the sensor control system, which autonomously tunes parameters to maintain optimal performance, reducing both time loss and operational complexity.
3Productivity
If deep learning algorithms are used with fixed sensor parameters, then implementation simplicity is maintained, but the full potential of the algorithms cannot be utilized due to occlusion and unfavorable parameters
Solution Approach 1:
The patent implements a closed-loop system where the object recognition algorithm provides feedback on detection quality and confidence levels. This feedback is used to dynamically adjust sensor parameters, creating a responsive system that adapts to challenging conditions such as occlusion. The feedback mechanism enables the system to recover from suboptimal detections by adjusting parameters to capture additional informative data.
Data Source
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AI summary
The present invention relates to a method for adjusting the sensor parameters of at least one sensor (11) when using the at least one sensor (11) in functional connection with an object recognition algorithm (20) during object recognition, wherein the sensor parameters (16) to be adjusted are set on the at least one sensor (11), the at least one sensor (11) is activated with the set sensor parameters (15), and the at least one sensor (11) sensorially acquires data (12) and provides it to the object recognition algorithm (20), the object recognition algorithm (20) determines a result (21), comprising at least one detected object (13) with a match value assigned to the detected object, and provides it to a machine learning algorithm (30).The machine learning algorithm (30) varies the respective sensor parameters of the at least one sensor (11) within a respective sensor parameter range and provides the respective varied sensor parameters as adjustable sensor parameters (16) for the at least one sensor (11), and steps a. to d. are iteratively repeated until the respective match value assigned to the at least one detected object (13) reaches a maximum within at least one of the respective sensor parameter ranges. Furthermore, the present invention relates to a computer-readable medium for adjusting adjustable sensor parameters of at least one sensor.