Dual Sensing With Radar–Image Fusion and Two-Stage Clustering
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Solution Overview
Problem
Existing sensing technologies, such as cameras and radars, face limitations in harsh environments, with cameras failing in bad weather and radars unable to identify object types effectively.
Innovation Solution
A dual sensing method integrating camera and radar information through two-stage clustering to determine regions of interest, combining radar and image data for enhanced object detection and tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera sensing is used for object detection, then object type identification is improved, but reliability deteriorates in harsh environments
Solution Approach 1:
The patent combines camera and radar sensing systems into a dual sensing architecture. The camera provides rich image information for object type identification, while the radar provides reliable detection in harsh environments. By merging these two sensing systems, the patent achieves both accurate object identification and environmental robustness, resolving the contradiction between identification accuracy and reliability in bad weather conditions.
2Reliability
If radar sensing is used for object detection, then reliability in harsh environments is improved, but object type identification capability deteriorates
Solution Approach 1:
The patent integrates radar and camera sensing capabilities into a unified dual sensing system. The radar component ensures reliable detection in harsh environments through its immunity to weather conditions, while the camera component provides detailed image information for object type identification. This merging allows the system to achieve both reliability and identification accuracy simultaneously.
3Measurement precision
If dual sensing with clustering is applied, then object detection accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent applies clustering algorithms to segment and organize the sensing data from both camera and radar sources. By dividing the complex sensing data into meaningful clusters and regions of interest, the system simplifies the processing workflow and improves detection accuracy without overwhelming complexity. The clustering process organizes the dual sensing information in a structured manner, making the system manageable and efficient.
Data Source
AI summary
A dual sensing method of an object and a computing apparatus for object sensing are provided. In the method, a first clustering is performed on radar information including a plurality of sensing points and is for determining a first part of the sensing points to be an object. A second clustering is performed on a result of the first clustering and is for determining that the sensing points determined to be the object in the result of the first clustering are located in a region of a first density. A result of the second clustering is taken as a region of interest. According to the region of interest, object detection and/or object tracking is performed on combined information formed by combining the radar information and an image, whose respective detection region and photographing region are overlapped.