Multi-Sensor Object Recognition with Reliability-Based Data Fusion
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Solution Overview
Problem
Existing object recognition technologies using single sensors face challenges in accuracy, especially in adverse conditions, and struggle to effectively combine data from multiple sensors for reliable object recognition.
Innovation Solution
An electronic device and method that utilize multiple sensors to obtain and process data, calculate recognition reliability, and match object information across sensors, allowing for successful recognition without manual correction, by using a processor to determine and store object information in a database based on the reliability of each sensor's data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single sensor image recognition is used, then device complexity is reduced, but recognition accuracy deteriorates in adverse conditions
Solution Approach 1:
The patent combines data from multiple sensors (first sensor and second sensor) to perform object recognition. The processor integrates sensing data from both sensors and uses their respective recognition reliabilities to determine final object information, thereby improving recognition accuracy in adverse conditions while managing device complexity through systematic data fusion.
2Reliability
If multiple sensors are used for object recognition, then recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The patent changes the parameter of sensor quantity from one to multiple, and introduces a processor that evaluates recognition reliability as a new parameter. By systematically processing and weighting data from multiple sensors based on their individual recognition reliabilities, the system achieves improved accuracy while managing the increased complexity through structured data fusion algorithms.
3Measurement precision
If manual correction of algorithm parameters is performed, then recognition accuracy is improved, but recognition time increases
Solution Approach 1:
The system performs self-correction by automatically evaluating recognition reliability from multiple sensors and adjusting object recognition results without manual intervention. The processor autonomously determines which sensor data to trust based on recognition reliability metrics, eliminating the need for external manual parameter correction while maintaining high accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where recognition reliability from multiple sensors is continuously evaluated and used to adjust the final object recognition result. The system uses the recognition reliability information as feedback to automatically correct and improve recognition accuracy in real-time, avoiding manual correction delays.
4Reliability
If multiple sensor data are combined, then recognition reliability is improved, but data processing complexity increases
Solution Approach 1:
The patent introduces recognition reliability as a key parameter for evaluating and weighting data from multiple sensors. By changing the processing approach to systematically evaluate and integrate recognition reliability metrics, the system manages data processing complexity while improving recognition reliability through structured multi-sensor data fusion.
Data Source
AI summary
A method of recognizing an object, and a device therefor are provided. The method includes obtaining first sensing data from a first sensor that senses the object, obtaining second sensing data from a second sensor that senses the object, obtaining a first object recognition reliability for the object and a second object recognition reliability for the object respectively based on the first sensing data and the second sensing data, based on the first object recognition reliability and the second object recognition reliability, matching object information of the object recognized using the second sensing data to the first sensing data, and storing the matched object information in a database of one of the first sensor and the second sensor.